@article {2434,
	title = {Moisture Content Estimation of Porous Building Stones using Hyperspectral Imaging},
	journal = {Scientific Data},
	volume = {13},
	year = {2025},
	month = {Dec},
	pages = {102},
	abstract = {Moisture poses a major threat to built heritage through processes such as frost damage, salt crystallization and biological growth, which accelerate stone deterioration. Stone moisture content can be determined non-destructively by spectral reflectance in the shortwave infrared (SWIR) wavelength range. To validate this, large cubic and small cylindrical samples from six stone types, Brick, Euville, Massangis, Neubrunn, Obernkirchen, and Savonni{\`e}res, with different controlled moisture levels were prepared, and a comprehensive SWIR hyperspectral image dataset was created. The acquired hyperspectral images were processed using the Normalized Relative Arc Lengths (NRAL) method to generate moisture maps that illustrate the spatial distribution of water within each sample. Because moisture distribution is influenced by pore characteristics and grain arrangement, the moisture maps were validated through petrographic examination. For quantitative validation, the mean moisture content of each sample was compared with the corresponding gravimetric moisture content. The results demonstrated strong agreement between the estimated and gravimetric moisture values, with root mean square errors ranging from 1 and 2 g/g ~{\texttimes} 100.},
	issn = {2052-4463},
	doi = {10.1038/s41597-025-06416-4},
	url = {https://doi.org/10.1038/s41597-025-06416-4},
	author = {Chaghdo, Danish Ali and Koirala, Bikram and Cristina, Laura and De Kock, Tim and Fontaine, Laurent and Hayen, Roald and Paul Scheunders}
}
@article {2422,
	title = {Soil Moisture Content Estimation From Hyperspectral Remote Sensing Data},
	journal = {IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
	volume = {18},
	year = {2025},
	pages = {22231-22240},
	abstract = {Because of its significant absorption power, especially in the shortwave infrared optical region, water dominates the optical reflectance properties of water-bearing materials. This allows us to study a material{\textquoteright}s water-related features, such as its moisture content, from optical reflectance. In this study, we proposed a framework to estimate soil moisture content from PRISMA hyperspectral remote sensing data. The proposed framework requires a dry endmember spectrum and an endmember spectrum of high soil moisture along with ground truth moisture content, obtained from ground measurements. The method takes into account the complex interaction of light with soils, the large variation of environmental conditions, leading to spectral variability and the soil-specific behavior of water. The framework is extensively validated using ground-measured soil moisture data from the International Soil Moisture Network database. A total of 1418 PRISMA images corresponding to 151 ground stations were analyzed. From 518 retained images, a total root-mean-squared error of 8.682 \% and R2 of 0.385 was obtained.},
	keywords = {Absorption, Hyperspectral, Hyperspectral imaging, Land surface, Machine learning regression, Optical imaging, Optical reflection, Optical sensors, PRISMA, Reflectivity, remote sensing, Soil measurements, Soil moisture, soil moisture content (SMC), vegetation mapping},
	doi = {10.1109/JSTARS.2025.3603841},
	author = {Jambhali, Ketaki Vinay and Koirala, Bikram and Bnoulkacem, Zakaria and Paul Scheunders}
}
@article {2421,
	title = {A Spectral-Spatial Attention Network for Hyperspectral Unmixing},
	journal = {IEEE Transactions on Geoscience and Remote Sensing},
	volume = {63},
	year = {2025},
	pages = {1-15},
	abstract = {Hyperspectral unmixing, an essential and fundamental task in remote sensing, focuses on estimating endmembers (spectrally pure components) and their fractional abundances within each mixed pixel of a hyperspectral image. With the advent of deep learning (DL), the field of hyperspectral unmixing has made significant progress. Among DL approaches, autoencoder-based models have shown promising results. However, most unmixing methods estimate the endmembers by the weights of the linear layers in the decoder of their networks, making their performance highly dependent on weight initialization. Moreover, noise is not explicitly accounted for in most recent methods that use spectral angle distance (SAD) loss. To avoid the initialization problems, we developed an innovative inversion strategy to directly estimate the endmembers. Moreover, to optimally account for noise, an end-to-end network is proposed, which integrates both denoising and unmixing. Finally, for an improved feature extraction, a novel spectral-spatial attention module (SSAM) is integrated into the network. Extensive experiments on synthetic and three real datasets show that the proposed method significantly and consistently outperforms the compared state-of-the-art methods. The full code is available at https://github.com/xuanwentao for public evaluation.},
	keywords = {Attention module, autoencoder, Autoencoders, autonomous unmixing module (AUM), Convolutional neural networks, Data mining, Decoding, deep denoising module (DDM), Feature extraction, Hyperspectral imaging, Hyperspectral unmixing, image reconstruction, Loss measurement, Neural networks, Noise reduction},
	doi = {10.1109/TGRS.2025.3576479},
	author = {Tao, Xuanwen and Koirala, Bikram and Rasti, Behnood and Plaza, Antonio and Paul Scheunders}
}
@article {2362,
	title = {An efficient method for water content estimation of building materials from spectral reflectance},
	journal = {NDT \& E International},
	volume = {147},
	year = {2024},
	pages = {103214},
	abstract = {We propose a nondestructive methodology to accurately estimate the water content of building materials from spectral reflectance in the shortwave infrared. The water content of a wet sample is estimated from the relative position of its reflectance spectrum on the curve between 2 reference spectra with known water content. By design, the approach is invariant to variations in illumination and acquisition conditions. Validation is done on datasets of clay powders and bricks. The experimental results provide confirmation of the effectiveness of the proposed method.},
	keywords = {Brick, Clay, Cultural heritage, Hyperspectral, remote sensing, Water content},
	issn = {0963-8695},
	doi = {https://doi.org/10.1016/j.ndteint.2024.103214},
	url = {https://www.sciencedirect.com/science/article/pii/S0963869524001798},
	author = {Bikram Koirala and Paul Scheunders}
}
@inproceedings {2398,
	title = {Exploring the Potential of Hyperspectral Imaging to Estimate the Moisture Content in Natural and Historical Stones},
	booktitle = {2024 14th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS)},
	year = {2024},
	pages = {1-5},
	keywords = {Building Physics, Cultural differences, Damage Monitoring, Estimation, Hyperspectral imaging, Moisture, Moisture content, Monitoring, Protocols, Reflectivity, Reliability, Signal processing, Water conservation},
	doi = {10.1109/WHISPERS65427.2024.10876512},
	author = {Chaghdo, Danish Ali and Koirala, Bikram and Cristina, Laura and Hayen, Roald and Paul Scheunders}
}
@inproceedings {2397,
	title = {Improving Spectral Unmixing Performance by Frequency Component Reduction},
	booktitle = {2024 14th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS)},
	year = {2024},
	pages = {1-5},
	keywords = {Fast Fourier Transform, Fast Fourier transforms, Genetic algorithms, Hyperspectral, Hyperspectral imaging, intimate mixtures, Noise, Noise level, Redundancy, Robustness, Signal processing algorithms, Stability analysis, Surface treatment, unmixing},
	doi = {10.1109/WHISPERS65427.2024.10876530},
	author = {Bnoulkacem, Zakaria and Koirala, Bikram and Paul Scheunders}
}
@article {2382,
	title = {A New Dual-Feature Fusion Network for Enhanced Hyperspectral Unmixing},
	journal = {IEEE Transactions on Geoscience and Remote Sensing},
	year = {2024},
	pages = {1-1},
	keywords = {Accuracy, dual-feature fusion, Estimation, Feature extraction, Hyperspectral imaging, Hyperspectral unmixing, image reconstruction, Noise reduction, Reflectivity, Scattering, similarity, Transformers, Transforms, unmixing module},
	doi = {10.1109/TGRS.2024.3505292},
	author = {Tao, Xuanwen and Koirala, Bikram and Plaza, Antonio and Paul Scheunders}
}
@article {2361,
	title = {Nonlinear Spectral Unmixing Using B{\'e}zier Surfaces},
	journal = {IEEE Transactions on Geoscience and Remote Sensing},
	volume = {62},
	year = {2024},
	pages = {1-16},
	keywords = {B{\'e}zier surface, Data models, Hyperspectral, Hyperspectral imaging, Manifolds, mineral powder mixtures, mixing models, nonlinearity, Reflectivity, Sensor phenomena and characterization, spectral variability, Surface treatment, Training},
	doi = {10.1109/TGRS.2024.3422495},
	author = {Koirala, Bikram and Rasti, Behnood and Bnoulkacem, Zakaria and Paul Scheunders}
}
@article {2344,
	title = {A Supervised Approach for Estimating Fractional Abundances of Binary Intimate Mixtures},
	journal = {IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
	year = {2024},
	pages = {1-11},
	keywords = {Earth, Estimation, Hyperspectral, Hyperspectral imaging, Mathematical models, mineral powder mixtures, mixing models, nonlinearity, Powders, Reflectivity, spectral variability, Vectors},
	doi = {10.1109/JSTARS.2024.3387750},
	author = {Koirala, Bikram and Rasti, Behnood and Paul Scheunders}
}
@inproceedings {2333,
	title = {Acritical Comparison of Linear and Nonlinear Unmixing for Intimate Mixtures},
	booktitle = {2023 13th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS)},
	year = {2023},
	pages = {1-5},
	keywords = {Conferences, Estimation, hyperspectral image, Hyperspectral imaging, intimate mixtures, linear and nonlinear unmixing, Powders, Reflectivity, Signal processing, Spectroradiometers},
	doi = {10.1109/WHISPERS61460.2023.10431354},
	author = {Koirala, Bikram and Das, Samiran and Rasti, Behnood and Ghamisi, Pedram and Gloaguen, Richard and Paul Scheunders}
}
@inproceedings {2335,
	title = {Corrosion detection using hyperspectral VNIR and SWIR imaging for drone-based inspection of electrical pylons},
	booktitle = {EUROCORR 2023, European Corrosion Congress, Proceedings},
	year = {2023},
	pages = {2},
	author = {Bnoulkacem, Zakaria and Vlaminck, Michiel and Luzardo Morocho, Gonzalo Raimundo and Zivariadab, Hamed and Zahiri, Zohreh and Koirala, Bikram and Platisa, Ljiljana and Luong, Hiep and Paul Scheunders and Mangialetto, Fr{\'e}d{\'e}ric and Bufi, Irid}
}
@inproceedings {2334,
	title = {Drone-Based Corrosion Detection on High-Voltage Transmission Towers Using Hyperspectral Imaging},
	booktitle = {2023 13th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS)},
	year = {2023},
	pages = {1-5},
	keywords = {Corrosion, corrosion detection, Drones, High-voltage techniques, high-voltage tower, Hyperspectral imaging, Inspection, Pipelines, Poles and towers, UAV},
	doi = {10.1109/WHISPERS61460.2023.10431111},
	author = {Vlaminck, Michiel and Bnoulkacem, Zakaria and Luzardo, Gonzalo and Zivariadab, Hamed and Zahiri, Zohreh and Koirala, Bikram and Mangialetto, Fr{\'e}d{\'e}ric and Bufi, Irid and Platisa, Ljiljana and Jayapala, Murali and Paul Scheunders and Luong, Hiep}
}
@inproceedings {2317,
	title = {An Extensive Multisensor Hyperspectral Benchmark Datasets of Intimate Mixtures of Mineral Powders},
	booktitle = { IEEE International Geoscience and Remote Sensing Symposium},
	year = {2023},
	month = {20 October 2023},
	pages = {5890-5893},
	abstract = {Since many materials behave as heterogeneous intimate mixtures with which each photon interacts differently, the relationship between spectral reflectance and material composition is very complex. Quantitative validation of spectral unmixing algorithms requires high-quality ground truth fractional abundance data, which are very difficult to obtain.In this work, we generated a comprehensive hyperspectral dataset of intimate mineral powder mixtures by homogeneously mixing five different clay powders (Kaolin, Roof clay, Red clay, mixed clay, and Calcium hydroxide). In total 325 samples were prepared. Among the 325 samples, 60 mixtures were binary, 150 were ternary, 100 were quaternary, and 15 were quinary. For each mixture (and pure clay powder), reflectance spectra are acquired by 13 different sensors, with a broad wavelength range between the visible and the long-wavelength infrared regions (i.e., between 350 nm and 15385 nm) and with a large variation in sensor types, platforms, and acquisition conditions. We will make this dataset public, to be used by the community for the validation of nonlinear unmixing methodologies (https://github.com/VisionlabUA/Multisensor_datasets)},
	doi = {10.1109/IGARSS52108.2023.10281467},
	author = {Bikram Koirala and Behnood Rasti and Zakaria Bnoulkacem and Andr{\'e}a De Lima Ribeiro and Yuleika Madriz and Erik Herrmann and Arthur Gestels and Thomas De Kerf and Koen Janssens and Gunther Steenackers and Richard Gloaguen and Paul Scheunders}
}
@article {2324,
	title = {A Multisensor Hyperspectral Benchmark Dataset For Unmixing of Intimate Mixtures},
	journal = {IEEE Sensors Journal},
	year = {2023},
	month = {2023/12/28},
	abstract = {Optical hyperspectral cameras capture the spectral reflectance of materials. Since many materials behave as heterogeneous intimate mixtures with which each photon interacts differently, the relationship between spectral reflectance and material composition is very complex. Quantitative validation of spectral unmixing algorithms requires high-quality ground truth fractional abundance data, which are very difficult to obtain. 

In this work, we generated a comprehensive laboratory ground truth dataset of intimately mixed mineral powders. For this, five clay powders (Kaolin, Roof clay, Red clay, mixed clay, and Calcium hydroxide) were mixed homogeneously to prepare 325 samples of 60 binary, 150 ternary, 100 quaternary, and 15 quinary mixtures. Thirteen different hyperspectral sensors have been used to acquire the reflectance spectra of these mixtures in the visible, near, short, mid, and long-wavelength infrared regions (350-15385) nm. Overlaps in wavelength regions due to the operational ranges of each sensor and variations in acquisition conditions resulted in a large amount of spectral variability. Ground truth composition is given by construction, but to verify that the generated samples are sufficiently homogeneous, XRD and XRF elemental analysis is performed. We believe these data will be beneficial for validating advanced methods for nonlinear unmixing and material composition estimation, including studying spectral variability and training supervised unmixing approaches. The datasets can be downloaded from the following link: https://github.com/VisionlabHyperspectral/Multisensor_datasets.},
	author = {Bikram Koirala and Behnood Rasti and Zakaria Bnoulkacem and Andr{\'e}a De Lima Ribeiro and Yuleika Madriz and Erik Herrmann and Arthur Gestels and Thomas De Kerf and Sandra Lorenz and Margret Fuchs and Koen Janssens and Gunther Steenackers and Richard Gloaguen and Paul Scheunders}
}
@article {2402,
	title = {Shadow-aware nonlinear spectral unmixing with spatial regularization},
	journal = {IEEE Transactions on Geoscience and Remote Sensing},
	volume = {61},
	year = {2023},
	pages = {5517516},
	author = {Guichen Zhang and Paul Scheunders and Daniele Cerra}
}
@inproceedings {2331,
	title = {Soil Moisture Content Estimation from Hyperspectral Remote Sensing Data},
	booktitle = {2023 13th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS)},
	year = {2023},
	pages = {1-5},
	keywords = {Hyperion EO-1, Hyperspectral, Hyperspectral imaging, machine learning, Moisture, Optical reflection, Optical saturation, Optical sensors, Optical signal processing, remote sensing, Soil moisture, soil moisture content},
	doi = {10.1109/WHISPERS61460.2023.10431194},
	author = {Jambhali, Ketaki Vinay and Koirala, Bikram and Bnoulkacem, Zakaria and Paul Scheunders}
}
@article {2318,
	title = {Study on the Potential of Oil Spill Monitoring in a Port Environment Using Optical Reflectance},
	journal = {Remote Sensing},
	year = {2023},
	month = {2023/10/13},
	abstract = {In this work, we studied the potential of the visible, near-infrared, and shortwave infrared wavelength regions for monitoring oil spill incidents using optical reflectance. First, a simple physical model was designed for accurate oil thickness and volume estimation using optical reflectance. The developed method was made invariant to changes in acquisition and illumination conditions. In the next step, an algorithm based on an artificial neural network was designed to detect spilled oil. The training samples that are required to optimize the parameters of the network were generated by utilizing the proposed physical model. To validate the method, experiments were conducted in laboratory and outdoor scenarios for detection and thickness/volume estimation on four different oil types. In particular, we developed hyperspectral datasets of oil samples with varying thickness between 500 {\textmu}m and 5000 {\textmu}m acquired using two different sensors, an Agrispec spectrometer and an Imec snapscan shortwave infrared hyperspectral camera, in strictly controlled experimental settings. To demonstrate the potential of the proposed method in outdoor environments using solely the visible wavelength region, we monitored the evolution of artificially spilled oil in an outdoor scene with an RGB camera mounted on a drone.},
	doi = {10.3390/rs15204950},
	author = {Bikram Koirala and Nicholus Mboga and Robrecht Moelans and Els Knaeps and Seppe Sels and Frederik Winters and Svetlana Samsonova and Steve Vanlanduit and Paul Scheunders}
}
@inproceedings {2332,
	title = {Unmixing Intimate Mixtures Using B{\'e}zier Surfaces},
	booktitle = {2023 13th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS)},
	year = {2023},
	pages = {1-5},
	keywords = {hyperspectral image, Hyperspectral imaging, intimate mixtures, Lighting, nonlinear unmixing, Reflectivity, Signal processing, Surface reconstruction, Surface treatment, Training},
	doi = {10.1109/WHISPERS61460.2023.10431306},
	author = {Koirala, Bikram and Rasti, Behnood and Bnoulkacem, Zakaria and Paul Scheunders}
}
@inproceedings {2227,
	title = {Analysis of Plant Stress Response Using Hyperspectral Imaging and Kernel Ridge Regression},
	booktitle = {11th International Conference on Robotics, Vision, Signal Processing and Power Applications},
	volume = {829},
	year = {2022},
	publisher = {Lecture Notes in Electrical Engineering, Springer},
	organization = {Lecture Notes in Electrical Engineering, Springer},
	address = {Singapore},
	doi = {10.1007/978-981-16-8129-5_66},
	author = {Mohd Shahrimie Mohd Asaari and Stien Mertens and Stijn Dhondt and Dirk Inze and Paul Scheunders}
}
@inproceedings {2311,
	title = {Deep Blind Unmixing using Minimum Simplex Convolutional Network},
	booktitle = {IEEE International Geoscience and Remote Sensing Symposium},
	year = {2022},
	month = {28/09/2022},
	pages = {28-31},
	abstract = {This paper proposes a deep blind hyperspectral unmixing network for datasets without pure pixels called minimum simplex convolutional network (MiSiCNet). MiSiCNet is the first deep learning-based blind unmixing method proposed in the literature which incorporates both spatial and geometrical information of the hyperspectral data, in addition to the spectral information. The proposed convolutional encoder-decoder architecture incorporates the spatial information using convolutional filters and implicitly applying a prior on the abundances. We added a minimum simplex volume penalty term to the loss function to exploit the geometrical information. We evaluate the performance of MiSiCNet on simulated and real datasets. The experimental results confirm the robustness of the proposed method to both noise and absence of pure pixels. Additionally, MiSiCNet considerably outperforms the state-of-the-art unmixing approaches. The results are given in terms of spectral angle distance in degree for the endmember estimation, and root mean square error in percentage for the abundance estimation. MiS-iCNet was implemented in Python (3.8) using PyTorch as the platform for the deep network and is available online: https://github.com/BehnoodRasti/MiSiCNet.},
	doi = {10.1109/IGARSS46834.2022.9883117},
	author = {Behnood Rasti and Bikram Koirala and Paul Scheunders and Jocelyn Chanussot}
}
@article {2410,
	title = {HapkeCNN: Blind Nonlinear Unmixing for Intimate Mixtures Using Hapke Model and Convolutional Neural Network},
	journal = {IEEE Transactions on Geoscience and Remote Sensing},
	volume = {60},
	year = {2022},
	pages = {1-15},
	keywords = {Albedo, Blind unmixing, convolutional neural network, deep learning (DL), endmember estimation, Estimation, Fans, Hapke model, hyperspectral image, image reconstruction, Kernel, minimum simplex volume, nonlinear unmixing, Photonics, Reflectivity, Solid modeling},
	doi = {10.1109/TGRS.2022.3202490},
	author = {Rasti, Behnood and Koirala, Bikram and Paul Scheunders}
}
@article {2261,
	title = {HapkeCNN: Blind nonlinear unmixing for intimate mixtures using Hapke model and convolutional neural network},
	journal = {IEEE Transactions on Geoscience and Remote Sensing},
	year = {2022},
	author = {Behnood Rasti and Bikram Koirala and Paul Scheunders}
}
@inproceedings {2231,
	title = {Hyperspectral clustering using atrous spatial-spectral convolutional network},
	booktitle = {IGARSS 2022, International Geoscience and Remote Sensing Symposium},
	year = {2022},
	address = {Kuala Lumpur, Malaysia},
	author = {Kasra Rafiezadeh Sahi and Pedram Ghamisi and Behnood Rasti and Paul Scheunders and Richard Gloaguen}
}
@article {2313,
	title = {Hyperspectral Unmixing Using Transformer Network},
	journal = {IEEE Transactions on Geoscience and Remote Sensing},
	volume = {60},
	year = {2022},
	month = { 03 August 2022},
	pages = {1-16},
	abstract = {Transformers have intrigued the vision research community with their state-of-the-art performance in natural language processing. With their superior performance, transformers have found their way into the field of hyperspectral image classification and achieved promising results. In this article, we harness the power of transformers to conquer the task of hyperspectral unmixing and propose a novel deep neural network-based unmixing model with transformers. A transformer network captures nonlocal feature dependencies by interactions between image patches, which are not employed in convolutional neural network (CNN) models, and hereby has the ability to enhance the quality of the endmember spectra and the abundance maps. The proposed model is a combination of a convolutional autoencoder and a transformer. The hyperspectral data is encoded by the convolutional encoder. The transformer captures long-range dependencies between the representations derived from the encoder. The data are reconstructed using a convolutional decoder. We applied the proposed unmixing model to three widely used unmixing datasets, that is, Samson, Apex, and Washington DC Mall, and compared it with the state-of-the-art in terms of root mean squared error and spectral angle distance. The source code for the proposed model will be made publicly available at https://github.com/preetam22n/DeepTrans-HSU .},
	doi = {10.1109/TGRS.2022.3196057},
	author = {Preetam Ghosh and Swalpa Kumar Roy and Bikram Koirala and Behnood Rasti and Paul Scheunders}
}
@article {2208,
	title = {Identification of corrosion minerals usning shortwave infrared hyperspectral imaging},
	journal = {Sensors},
	volume = {22},
	year = {2022},
	pages = {407},
	abstract = {In this study, we propose a new method to identify corrosion minerals in carbon steel using hyperspectral imaging (HSI) in the shortwave infrared range (900{\textendash}1700 nm). Seven samples were artificially corroded using a neutral salt spray test and examined using a hyperspectral camera. A normalized cross-correlation algorithm is used to identify four different corrosion minerals (goethite, magnetite, lepidocrocite and hematite), using reference spectra. A Fourier Transform Infrared spectrometer (FTIR) analysis of the scraped corrosion powders was used as a ground truth to validate the results obtained by the hyperspectral camera. This comparison shows that the HSI technique effectively detects the dominant mineral present in the samples. In addition, HSI can also accurately predict the changes in mineral composition that occur over time. },
	author = {Thomas De Kerf and G. Pipintakos and Zohreh Zahiri and Steve Vanlanduit and Paul Scheunders}
}
@article {2408,
	title = {MiSiCNet: Minimum Simplex Convolutional Network for Deep Hyperspectral Unmixing},
	journal = {IEEE Transactions on Geoscience and Remote Sensing},
	volume = {60},
	year = {2022},
	pages = {1-15},
	keywords = {Blind unmixing, convolutional neural network, Data mining, deep learning, deep prior, endmember extraction, Estimation, hyperspectral image, Hyperspectral imaging, Indexes, Libraries, Minimization, minimum simplex volume, Optimization, unmixing},
	doi = {10.1109/TGRS.2022.3146904},
	author = {Rasti, Behnood and Koirala, Bikram and Paul Scheunders and Chanussot, Jocelyn}
}
@article {2220,
	title = {MiSiCNet: Minimum Simplex Convolutional Network for Deep Hyperspectral Unmixing},
	journal = {IEEE Transactions on Geoscience and Remote Sensing},
	volume = {60},
	year = {2022},
	doi = {https://doi.org/10.1109/TGRS.2022.3146904},
	author = {Behnood Rasti and Bikram Koirala and Paul Scheunders and Jocelyn Chanussot}
}
@article {2260,
	title = {MS2A-Net: multi-view spectral-spatial association network for hyperspectral image clustering},
	journal = {IEEE-JSTARS, Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
	volume = {15},
	year = {2022},
	pages = {6518-6530},
	author = {Kasra Rafiezadeh Sahi and Pedram Ghamisi and Behnood Rasti and Richard Gloaguen and Paul Scheunders}
}
@article {2221,
	title = {Non-Destructive Analysis of Plant Physiological Traits Using Hyperspectral Imaging: A Case Study on Drought Stress},
	journal = {Computers and Electronics in Agriculture},
	volume = {195},
	year = {2022},
	doi = {10.1016/j.compag.2022.106806},
	author = {Mohd Shahrimie Mohd Asaari and Stien Mertens and Lennart Verbraeken and Stijn Dhondt and Dirk Inze and Bikram Koirala and Paul Scheunders}
}
@article {2409,
	title = {Non-destructive analysis of plant physiological traits using hyperspectral imaging: A case study on drought stress},
	journal = {Computers and Electronics in Agriculture},
	volume = {195},
	year = {2022},
	pages = {106806},
	abstract = {Conventional methods to access plant physiological traits are based on destructive measurements by means of biochemical extraction or leaf clipping, thereby limiting the throughput capability. With advances in hyperspectral imaging sensor, fast, non-invasive and non-destructive measurements of a plant{\textquoteright}s physiological status became feasible. In this work, a non-destructive method for the characterization of a plant{\textquoteright}s status from hyperspectral images is presented. A supervised data-driven method based on Machine Learning Regression (MLR) algorithms was developed to generate prediction models of four targeted physiological traits: water potential, effective quantum yield of photosystem II, transpiration rate and stomatal conductance. Standard Normal Variate (SNV) transformed reflectance spectra were used as the input variables for building the regression model. Three MLR algorithms: Gaussian Process Regression (GPR), Kernel Ridge Regression (KRR), and Partial Least Squares Regression (PLSR) were explored as candidate methods for building the prediction model of the targeted physiological traits. Validation results show that the non-linear prediction models, developed based on the GPR algorithm produced the best estimation accuracy on all plant traits. The best prediction models were applied to a small-scale phenotyping experiment to study drought stress responses in maize plants. Results show that all estimated traits revealed a significant difference between plants under drought stress and normal growth dynamics as early as after 3 days of drought induction.},
	keywords = {Drought stress, Hyperspectral imaging, Machine learning regression, Non-destructive measurements, Physiological traits, Plant phenotyping},
	issn = {0168-1699},
	doi = {https://doi.org/10.1016/j.compag.2022.106806},
	url = {https://www.sciencedirect.com/science/article/pii/S0168169922001235},
	author = {Mohd Shahrimie Mohd Asaari and Stien Mertens and Lennart Verbraeken and Stijn Dhondt and Dirk Inze and Koirala Bikram and Paul Scheunders}
}
@article {2403,
	title = {Quantitative detection of corrosion minerals in carbon steel using shortwave infrared hyperspectral imaging},
	journal = {RSC Advances},
	volume = {12},
	year = {2022},
	pages = {32775-32783},
	author = {Thomas De Kerf and Arthur Gestels and Janssens, Koen and Gunther Steenackers and Paul Scheunders and Steve Vanlanduit}
}
@article {2316,
	title = {A Robust Supervised Method for Estimating Soil Moisture Content From Spectral Reflectance},
	journal = {IEEE Transactions on Geoscience and Remote Sensing },
	volume = {60},
	year = {2022},
	month = {05 October 2022},
	pages = {1-13},
	abstract = {Due to the complex interaction of light with moist soils, the soil moisture content (SMC) is hard to estimate from the soil spectral reflectance. Spectral variability, caused by variations in viewing and illumination angle and between-sensor variability, further complicates the estimation. In this work, we developed a supervised methodology to accurately estimate SMC from spectral reflectance. The method determines a proxy for the SMC of moist soil, making use of the reflectance spectra of an air-dried and saturated soil sample. The proxy is made invariant to illumination and viewing angle, and sensor type. In the next step, the proxy is normalized with respect to the ground-truth SMC of the saturated soil to make the technique less dependent on the soil type. The normalized proxy can be directly used as an estimate of SMC. Alternatively, the nonlinear relationship between the normalized proxy and the actual SMC can be learned by supervised regression. Experiments are conducted on real moist soil data. In particular, we developed datasets of moist minerals, acquired by two different sensors, an Agrispec spectrometer and an Imec snapscan shortwave infrared (SWIR) hyperspectral camera, under strictly controlled experimental settings. The proposed methodology is also validated on the available real moist soil data from the literature. Compared to state-of-the-art methods, the proposed method accurately estimates the SMC.},
	doi = {10.1109/TGRS.2022.3212600},
	author = {Bikram Koirala and Zohreh Zahiri and Paul Scheunders}
}
@inproceedings {2312,
	title = {A Robust Supervised Method to Estimate Chlorophyll Ab Content from Spectral Reflectance},
	booktitle = {IEEE International Geoscience and Remote Sensing Symposium},
	year = {2022},
	month = {28/09/2022},
	pages = {326-329},
	abstract = {Leaf chlorophyll ab content is an important indicator of vegetation physiological status and is generally obtained from spectral reflectance. For non-destructive estimation of chlorophyll ab content, physical leaf reflectance models, such as the PROSPECT model and supervised methods have been applied. While the former generally does not perform optimal, the latter only performs well when trained on similar data. In this work, we developed a robust supervised method that overcomes this problem. The method derives a proxy for chlorophyll ab content as the relative position of a leaf reflectance spectrum on the arc spanned by the two extremes, containing high and low chlorophyll ab content. This proxy is found to be unaffected by spectral variability, caused by environmental and acquisition conditions. The relation between this proxy and the actual chlorophyll ab content is obtained by a supervised regression model, that is trained on a single leaf reflectance dataset, and that is transferable to other datasets. The proposed method is validated on seven real hyperspectral datasets.},
	doi = {10.1109/IGARSS46834.2022.9883839},
	author = {Bikram Koirala and Paul Scheunders}
}
@article {2258,
	title = {Shadow-aware nonlinear spectral unmixing for hyperspectral imagery},
	journal = {IEEE-JSTARS, Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
	volume = {15},
	year = {2022},
	pages = {5514-5533},
	author = {Guichen Zhang and Paul Scheunders and Daniele Cerra and Rupert Muller}
}
@inproceedings {2320,
	title = {Sparse Unmixing using Deep Convolutional Networks},
	booktitle = {IEEE International Geoscience and Remote Sensing Symposium},
	year = {2022},
	month = {28/09/2022},
	pages = {24-27},
	abstract = {This paper proposes a sparse unmixing technique using a convolutional neural network (SUnCNN). We reformulate the sparse unmixing problem into an optimization over the parameters of a convolutional network. Relying on a spectral library, the deep network learns in an unsuper-vised manner a mapping from a fixed input to the sparse abundances. Moreover, SUnCNN fulfills the sum-to-one constraint using a softmax activation layer. We compare SUnCNN with the state-of-the-art using a simulated and a real dataset. The experimental results show that the proposed deep learning-based unmixing method outperforms the oth-ers in terms of signal to reconstruction error. Additionally, SUnCNN is visually superior to the competing techniques. SUnCNN was implemented in Python (3.8) using PyTorch as the platform for the deep network and is available online: https://github.com/BehnoodRasti/SUnCNN.},
	doi = {10.1109/IGARSS46834.2022.9884790},
	author = {Behnood Rasti and Bikram Koirala and Paul Scheunders}
}
@article {2207,
	title = {Unsupervised Data Fusion with Deeper Perspective: A Novel Multi-Sensor Deep Clustering Algorithm},
	journal = {IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing },
	volume = {15},
	year = {2022},
	month = {1/2022},
	pages = {284-296},
	abstract = {The ever-growing developments in technology to capture different types of image data (e.g., hyperspectral imaging and Light Detection and Ranging (LiDAR)-derived digital surface model (DSM)), along with new processing techniques, have led to a rising interest in imaging applications for Earth observation. However, analyzing such datasets in parallel, remains a challenging task. In this paper, we propose a multi-sensor deep clustering (MDC) algorithm for the joint processing of multi-source imaging data. The architecture of MDC is inspired by autoencoder (AE)-based networks. The MDC paradigm includes three parallel networks, a spectral network using an autoencoder structure, a spatial network using a convolutional autoencoder structure, and lastly, a fusion network that reconstructs the concatenated image information from the concatenated latent features from the spatial and spectral network. The proposed algorithm combines the reconstruction losses obtained by the aforementioned networks to optimize the parameters (i.e., weights and bias) of all three networks simultaneously. To validate the performance of the proposed algorithm, we use two multi-sensor datasets from different applications (i.e., geological and rural sites) as benchmarks. The experimental results confirm the superiority of our proposed deep clustering algorithm compared to a number of state-of-the-art clustering algorithms. The code will be available at: https://github.com/Kasra2020/MDC.},
	doi = {10.1109/JSTARS.2021.3132856},
	author = {Kasra Rafiezadeh Sahi and Pedram Ghamisi and Behnood Rasti and Paul Scheunders and Richard Gloaguen}
}
@inproceedings {2307,
	title = {BOOSTING HYPERSPECTRAL IMAGE UNMIXING USING DENOISING: FOUR SCENARIOS},
	booktitle = {IEEE International Geoscience and Remote Sensing Symposium IGARSS},
	year = {2021},
	month = {12 October 2021},
	abstract = {We present an analysis of the influence of noise on the unmixing of hyperspectral data. We propose four scenarios to 1) investigate the effect of noise reduction as a preprocessing step on the performance of hyperspectral unmixing and 2) study the relation between noise and different endmembers selection strategies. Experiments are conducted on a simu-1ated and a real datasets with a wide range of signal to noise ratios (from 10 to 50 dB).},
	doi = {10.1109/IGARSS47720.2021.9553942},
	author = {Behnood Rasti and Bikram Koirala and Paul Scheunders and Pedram Ghamisi and Richard Gloaguen}
}
@inproceedings {2223,
	title = {Destriping Hyperspectral Imagery By Adaptive Anisotropic Total Variation And Truncated Nuclear Norm},
	booktitle = {Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS)},
	year = {2021},
	doi = {10.1109/WHISPERS52202.2021.9484033},
	author = {Ting Hu and N. Liu and W. Li and R. Tao and F. Zhang and Paul Scheunders}
}
@article {2206,
	title = {Hyperspectral and multispectral image fusion using coupled non-negative tucker tensor decomposition},
	journal = {Remote Sensing},
	volume = {13},
	year = {2021},
	abstract = {Fusing a low spatial resolution hyperspectral image (HSI) with a high spatial resolution multispectral image (MSI), aiming to produce a super-resolution hyperspectral image, has recently attracted increasing research interest. In this paper, a novel approach based on coupled non-negative tensor decomposition is proposed. The proposed method performs a tucker tensor factorization of a low resolution hyperspectral image and a high resolution multispectral image under the constraint of non-negative tensor decomposition (NTD). The conventional matrix factorization methods essentially lose spatio-spectral structure information when stacking the 3D data structure of a hyperspectral image into a matrix form. Moreover, the spectral, spatial, or their joint structural features have to be imposed from the outside as a constraint to well pose the matrix factorization problem. The proposed method has the advantage of preserving the spatio-spectral structure of hyperspectral images. In this paper, the NTD is directly imposed on the coupled tensors of the HSI and MSI. Hence, the intrinsic spatio-spectral structure of the HSI is represented without loss, and spatial and spectral information can be interdependently exploited. Furthermore, multilinear interactions of different modes of the HSIs can be exactly modeled with the core tensor of the Tucker tensor decomposition. The proposed method is straightforward and easy to implement. Unlike other state-of-the-art approaches, the complexity of the proposed approach is linear with the size of the HSI cube. Experiments on two well-known datasets give promising results when compared with some recent methods from the literature.},
	doi = {https://doi.org/10.3390/rs13152930},
	author = {M. Zare and M. Sadegh Helfroush and K. Kazemi and Paul Scheunders}
}
@article {2066,
	title = {Hyperspectral Image Restoration Using Adaptive Anisotropy Total Variation and Nuclear Norms},
	journal = {IEEE Transactions on Geoscience and Remote Sensing},
	volume = {59},
	year = {2021},
	pages = {1516-1533},
	author = {T. Hu and W. Li and N. Liu and R. Tao and F. Zhang and Paul Scheunders}
}
@inproceedings {2222,
	title = {A Multi-Sensor Subspace-Based Clustering Algorithm Using RGB and Hyperspectral Data},
	booktitle = {Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS)},
	year = {2021},
	doi = {10.1109/WHISPERS52202.2021.9483953},
	author = {Kasra Rafiezadeh Sahi and Pedram Ghamisi and R. Jackisch and Behnood Rasti and Paul Scheunders and Richard Gloaguen}
}
@article {2086,
	title = {Robust supervised method for nonlinear spectral unmixing accounting for endmember variability},
	journal = {IEEE Transactions on Geoscience and Remote Sensing},
	volume = {59},
	year = {2021},
	pages = {7434-7448},
	abstract = {Due to the complex interaction of light with mixed materials, reflectance spectra are highly nonlinearly related to the pure material endmember spectra, making it hard to estimate the fractional abundances of the materials. Changing illumination conditions and cross-sensor situations cause spectral variability, further complicating the unmixing procedure. In this work, we propose a supervised approach to unmix mineral powder mixtures, containing endmember variability.

First, the abundances are estimated by calculating the geodesic distances between the mixtures and the endmembers. It is argued and experimentally validated that the estimated geodesic abundances, although not correct, are invariant to external spectral variability.  Then, a supervised approach is applied to learn a mapping from the obtained geodesic abundances to spectra that follow a linear model. To learn this mapping, ground truth fractional abundances of a number of training samples are required. 
Although any nonlinear regression method can be used to learn the mapping, Gaussian process is found to be suitable when a limited number of training samples are available. The trained model is applicable to all manifolds that contain a similar nonlinear behavior as the trained manifold, e.g. when the same mixtures are measured by another sensor. Using the output spectra, a simple inversion of the linear model reveals the true abundances.

Experiments are conducted on simulated and real mineral mixtures. In particular, we developed data sets of homogeneously mixed mineral powder mixtures, acquired by 2 different sensors, an Agrispec spectrometer and a snapscan shortwave infrared hyperspectral camera, under strictly controlled experimental settings. The proposed approach is compared to other supervised approaches and nonlinear mixture models.  },
	doi = {https://doi.org/10.1109/TGRS.2020.3031012},
	author = {Bikram Koirala and Zohreh Zahiri and Alfredo Lamberti and Paul Scheunders}
}
@inproceedings {2254,
	title = {Spectral Unmixing Using Deep Convolutional Encoder-Decoder},
	booktitle = {2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS},
	year = {2021},
	month = {12 October 2021},
	pages = {3829-3832},
	abstract = {In this paper, we introduce {\textquoteleft}Unmixing Deep Image Prior{\textquoteright} (UnDIP), a deep learning-based technique for the linear hyperspectral unmixing problem. The proposed method contains two steps. First, the endmembers are extracted using a geometric endmember extraction method, i.e. a simplex volume maximization in a subspace of the dataset. Then, the abundances are estimated using a deep image prior. The proposed deep image prior uses a convolutional neural network to estimate the fractional abundances, relying on the extracted endmembers and the observed hyperspectral dataset. The results show considerable improvements compared to state-of-the-art methods.},
	doi = {10.1109/IGARSS47720.2021.9553425},
	author = {Behnood Rasti and Bikram Koirala and Paul Scheunders and Pedram Ghamisi}
}
@article {2167,
	title = {UnDIP: hyperspectral unmixing using deep image prior},
	journal = {IEEE Transactions on Geoscience and Remote Sensing},
	volume = {60},
	year = {2021},
	month = {31 March 2021},
	pages = {1-15},
	abstract = {In this article, we introduce a deep learning-based technique for the linear hyperspectral unmixing problem. The proposed method contains two main steps. First, the endmembers are extracted using a geometric endmember extraction method, i.e., a simplex volume maximization in the subspace of the data set. Then, the abundances are estimated using a deep image prior. The main motivation of this work is to boost the abundance estimation and make the unmixing problem robust to noise. The proposed deep image prior uses a convolutional neural network to estimate the fractional abundances, relying on the extracted endmembers and the observed hyperspectral data set. The proposed method is evaluated on simulated and three real remote sensing data for a range of SNR values (i.e., from 20 to 50 dB). The results show considerable improvements compared to state-of-the-art methods. The proposed method was implemented in Python (3.8) using PyTorch as the platform for the deep network and is available online: https://github.com/BehnoodRasti/UnDIP.},
	doi = {10.1109/TGRS.2021.3067802},
	author = {Behnood Rasti and Bikram Koirala and Paul Scheunders and Pedram Ghamisi}
}
@inproceedings {2224,
	title = {When is the right time to apply denoising?},
	booktitle = {IGARSS 2021, International Geoscience and Remote Sensing Symposium},
	year = {2021},
	address = {Brussels, Belgium},
	doi = {10.1109/IGARSS47720.2021.9553263},
	author = {Kasra Rafiezadeh Sahi and Behnood Rasti and Pedram Ghamisi and Paul Scheunders and Richard Gloaguen}
}
@article {2093,
	title = {Data Fusion Using a Multi-Sensor Sparse-Based Clustering Algorithm},
	journal = {Remote Sensing},
	volume = {12 (23)},
	year = {2020},
	author = {Kasra Rafiezadeh Sahi and Pedram Ghamisi and Behnood Rasti and R. Jackisch and Paul Scheunders and Richard Gloaguen}
}
@article {2059,
	title = {How Hyperspectral Image Unmixing and Denoising Can Boost Each Other},
	journal = {Remote Sensing},
	volume = {12},
	year = {2020},
	month = {28 May 2020},
	abstract = {Hyperspectral linear unmixing and denoising are highly related hyperspectral image (HSI) analysis tasks. In particular, with the assumption of Gaussian noise, the linear model assumed for the HSI in the case of low-rank denoising is often the same as the one used in HSI unmixing. However, the optimization criterion and the assumptions on the constraints are different. Additionally, noise reduction as a preprocessing step in hyperspectral data analysis is often ignored. The main goal of this paper is to study experimentally the influence of noise on the process of hyperspectral unmixing by: (1) investigating the effect of noise reduction as a preprocessing step on the performance of hyperspectral unmixing; (2) studying the relation between noise and different endmember selection strategies; (3) investigating the performance of HSI unmixing as an HSI denoiser; (4) comparing the denoising performance of spectral unmixing, state-of-the-art HSI denoising techniques, and the combination of both. All experiments are performed on simulated and real datasets.},
	keywords = {hyperspectral image; unmixing; denoising; linear mixing model; low-rank model; noise reduction; abundance estimation},
	doi = {https://doi.org/10.3390/rs12111728},
	url = {https://www.mdpi.com/2072-4292/12/11/1728$\#$cite},
	author = {Behnood Rasti and Bikram Koirala and Paul Scheunders and Pedram Ghamisi}
}
@article {2048,
	title = {A Machine Learning Framework for Estimating Leaf Biochemical Parameters From Its Spectral Reflectance and Transmission Measurements},
	journal = {IEEE Transactions on Geoscience and Remote Sensing},
	volume = {58},
	year = {2020},
	month = {04/2020},
	pages = {7393-7405},
	abstract = {Spectral measurements are commonly applied for the nondestructive estimation of leaf parameters, such as the concentrations of chlorophyll a and b, carotenoid, anthocyanin, brown pigment, leaf water content, and leaf mass per area for the quantification of vegetation physiology. The most popular way to estimate these parameters is by using spectral vegetation indices. The use of biochemical models allows us to use the full wavelength range (400-2500 nm) and to physically interpret the result. However, their performance is usually lower than that of supervised machine learning regression techniques. Machine learning regression techniques, on the other hand, have the disadvantage that the relationship between estimated parameters and the reflectance/transmission spectra is unclear. In this article, a hybrid between a supervised learning method and physical modeling for the estimation of leaf parameters is proposed. In this method, a machine learning regression technique is applied to learn a mapping from the true hyperspectral data set to a data set that follows the PROSPECT model. The PROSPECT model then reveals the actual leaf parameters. Two mapping methods, based on Gaussian processes (GPs) and kernel ridge regression (KRR) are proposed. As an alternative, mapping onto the leaf absorption spectra is proposed as well. The proposed methodology not only estimates the leaf parameters with a lower error but also solves the interpretation problem of the parameters estimated by the advanced machine learning regression techniques. This method is validated on the ANGERS and LOPEX data set.},
	doi = {10.1109/TGRS.2020.2982263},
	url = {https://www.researchgate.net/publication/340391024_A_Machine_Learning_Framework_for_Estimating_Leaf_Biochemical_Parameters_From_Its_Spectral_Reflectance_and_Transmission_Measurements},
	author = {Bikram Koirala and Zohreh Zahiri and Paul Scheunders}
}
@article {2007,
	title = {Analysis of hyperspectral images for detection of drought stress and recovery in maize plants in a high-throughput phenotyping platform},
	journal = {Computers and Electronics in Agriculture},
	volume = {162},
	year = {2019},
	pages = {749-758},
	doi = {https://doi.org/10.1016/j.compag.2019.05.018},
	author = {Mohd Shahrimie Mohd Asaari and Stien Mertens and Stijn Dhondt and Dirk Inze and Nathalie Wuyts and Paul Scheunders}
}
@article {1934,
	title = {Decision Fusion Framework for Hyperspectral Image Classification Based on Markov and Conditional Random Fields},
	journal = {Remote Sensing},
	volume = {11},
	year = {2019},
	chapter = {624},
	abstract = {Classification of hyperspectral images is a challenging task owing to the high dimensionality of the data, limited ground truth data, collinearity of the spectra and the presence of mixed pixels. Conventional classification techniques do not cope well with these problems. Thus, in addition to the spectral information, features were developed for a more complete description of the pixels, e.g., containing contextual information at the superpixel level or mixed pixel information at the subpixel level. This has encouraged an evolution of fusion techniques which use these myriad of multiple feature sets and decisions from individual classifiers to be employed in a joint manner. In this work, we present a flexible decision fusion framework addressing these issues. In a first step, we propose to use sparse fractional abundances as decision source, complementary to class probabilities obtained from a supervised classifier. This specific selection of complementary decision sources enables the description of a pixel in a more complete way, and is expected to mitigate the effects of small training samples sizes. Secondly, we propose to apply a fusion scheme, based on the probabilistic graphical Markov Random Field (MRF) and Conditional Random Field (CRF) models, which inherently employ spatial information into the fusion process. To strengthen the decision fusion process, consistency links across the different decision sources are incorporated to encourage agreement between their decisions. The proposed framework offers flexibility such that it can be extended with additional decision sources in a straightforward way. Experimental results conducted on two real hyperspectral images show superiority over several other approaches in terms of classification performance when very limited training data is available},
	keywords = {Classification, decision fusion, Hyperspectral},
	doi = {https://doi.org/10.3390/rs11060624},
	url = {https://www.mdpi.com/2072-4292/11/6/624},
	author = {Vera Andrejchenko and Wenzhi Liao and Wilfried Philips and Paul Scheunders}
}
@inproceedings {1986,
	title = {Fractional abundance estimation of mixed and compound materials by hyperspectral imaging.},
	booktitle = {10th Workshop on Hyperspectral Image and Signal Processing : Evolution in Remote Sensing (WHISPERS), Sept 2019},
	year = {2019},
	pages = {pp. 1-5},
	address = {Amsterdam, Netherlands},
	abstract = {The mechanical and chemical properties of a compound material are determined by the fractional abundances of its components. In this work, we present a spectral unmixing technique to estimate the fractional abundances of the components of mixed and compound materials from hyperspectral images. The estimation of fractional abundances in mixed materials faces the main challenge of intimate mixing. In compound materials, the mixing with water causes changes in chemical properties resulting in spectral variability and non-linearity. To address these challenges, a supervised method is proposed that learns a mapping from the hyperspectral data to spectra that follow the linear mixing model. Then, a linear unmixing technique is applied on the mapped spectra to estimate the fractional abundances. To demonstrate the potential of the proposed method, experiments are conducted on hyperspectral images from mixtures of red and yellow clay powders and hardened mortar samples with varying water to cement ratios.},
	doi = {doi: 10.1109/WHISPERS.2019.8920893},
	url = {http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=\&arnumber=8920893\&isnumber=8920817},
	author = {Bikram Koirala and Zohreh Zahiri and Mahdi Khodadadzadeh and Paul Scheunders}
}
@article {1913,
	title = {Nonlinear hyperspectral unmixing with graphical models},
	journal = {IEEE Transaction on Geoscience and Remote Sensing},
	volume = {57},
	year = {2019},
	pages = {4844-4856},
	author = {Rob Heylen and Vera Andrejchenko and Zohreh Zahiri and Mario Parente and Paul Scheunders}
}
@inproceedings {1985,
	title = {A semi-supervised method for Nonlinear Hyperspectral Unmixing},
	booktitle = {IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Yokohama, Japan, 2019},
	year = {2019},
	pages = {pp. 361-364},
	abstract = {As the interaction of light with the Earth surface is very complex, spectral reflectances are composed of nonlinear mixtures of the observed materials. Nonlinear mixing models have the disadvantage that not all spectra of a hyperspectral dataset necessarily follow the same particular mixing model. Moreover, most models lack a proper interpretation of the estimated parameters in terms of fractional abundances.  
In this paper, we present a semi-supervised nonlinear unmixing technique that overcomes these problems. In a first step, we apply a kernelized simplex volume maximization to select an overcomplete set of endmembers that precisely describe the hyperspectral data manifold. 
In a second step, this set is used as ground truth data in a supervised learning approach to generate fractional abundance maps from the entire dataset. For this, three methods are presented, based on kernelized sparse unmixing, feedforward neural networks, and gaussian processes.
The proposed method is validated on simulated data, a dataset obtained by ray tracing, and a real hyperspectral image.},
	doi = {doi: 10.1109/IGARSS.2019.8898846},
	author = {Bikram Koirala and Paul Scheunders}
}
@inproceedings {2008,
	title = {A spectral mixing model accounting for multiple reflections and shadow},
	booktitle = {IGARSS 2019, International Geoscience and Remote Sensing Symposium},
	year = {2019},
	pages = {286-289},
	address = {Yokohama, Japan},
	doi = {10.1109/IGARSS.2019.8897856},
	author = {Vera Andrejchenko and Zohreh Zahiri and Rob Heylen and Paul Scheunders}
}
@article {1987,
	title = {A supervised method for nonlinear hyperspectral unmixing},
	journal = {Remote Sensing},
	volume = {11},
	year = {2019},
	month = {10/2019},
	abstract = {Due to the complex interaction of light with the Earth surface, reflectance spectra can be described as highly nonlinear mixtures of the reflectances of the material constituents occuring in a given resolution cell of hyperspectral data. Our aim is to estimate the fractional abundance maps of the materials from the nonlinear hyperspectral data. The main disadvantage of using nonlinear mixing models is that the model parameters are not properly interpretable in terms of fractional abundances. Moreover, not all spectra of a hyperspectral dataset necessarily follow the same particular mixing model. In this work, we present a supervised method for nonlinear spectral unmixing. The method learns a mapping from a true hyperspectral dataset to corresponding linear spectra, composed of the same fractional abundances. A simple linear unmixing then reveals the fractional abundances. To learn this mapping, ground truth information is required, in the form of actual spectra and corresponding fractional abundances, along with spectra of the pure materials, obtained from a spectral library or available in the dataset. Three methods are presented for learning the nonlinear mapping, based on gaussian processes, kernel ridge regression, and feedforward neural networks. Experimental results conducted on an artificial dataset, a data set obtained by ray tracing, and a drill core hyperspectral dataset show that this novel methodology is very promising.},
	doi = {https://doi.org/10.3390/rs11202458},
	url = {https://www.mdpi.com/2072-4292/11/20/2458},
	author = {Bikram Koirala and Mahdi Khodadadzadeh and Cecilia Contreras and Zohreh Zahiri and Richard Gloaguen and Paul Scheunders}
}
@proceedings {1899,
	title = {ACIVS 2018, Advanced Concepts for Intelligent Vision Systems },
	volume = {11182},
	year = {2018},
	author = {J Blanc-Talon and D Helbert and Wilfried Philips and D Popescu and Paul Scheunders}
}
@conference {2006,
	title = {Automatic Detection of Surface Damages on Steel Structures using Near Infrared Hyperspectral Imaging},
	year = {2018},
	abstract = {Surface damages such as corrosion and coating delamination are very common on steel structures and, if not prevented, can accelerate degradation and eventually lead to failure of the whole structure. Hence, field monitoring is of utmost importance to guarantee the structural health and reduce the maintenance cost. Visual inspection is very perilous (especially for offshore structures), time-consuming, and largely depends on the experience of the inspector (Price \& Figueira 2017). Current non-destructive testing (NDT) techniques (e.g. based on ultrasonic or eddy current technology, acoustic emission ([Calabrese et al 2016; Wu et al 2016] or Electrical Resistance [ Li et al 2007; Mathiesen et al 2016]), require a direct contact with the surface, which causes difficulties when the structure or parts of it are not accessible.
Image processing techniques have been considered as a fast and easier alternative to contact-based techniques. These techniques are usually based on measuring the textural and morphological changes in digital images from the structures (Feliciano et al 2015; Li \& Cheng 2016). However, in reality other materials on steel structures such as dirt, paint, bird droppings, oil leakage, and biofouling can significantly complicate the inspection. Moreover, some effects of corrosion not necessarily reveal themselves in the visible wavelength region. Hyperspectral imaging outside the visible range is an advanced imaging technique that can reveal chemical-mineralogical surface changes that are not observable by the naked eye.
This paper studied the feasibility of detecting coating damages on steel components using hyperspectral imaging. Two different samples of industrial components, provided by ENGIE Laborelec, were scanned with a hyperspectral camera: a part of a flame tube of a heavy-duty gas turbine as well as an artificially corroded sample of coated steel. The images were collected with a snapshot hyperspectral camera in the visible and Near Infrared range (manufactured by IMEC) and analyzed using a k-means clustering method. The outputs showed that despite the visual similarities between coating layers, corroded, and base material, hyperspectral imaging was capable to distinguish between them. Spectral plots showed that differences in spectral behavior of corroded and coated materials were more apparent when the wavelength shifts from the visible towards the near-infrared range. While further investigation is needed, the current results demonstrated that hyperspectral image analysis is a promising technology to complement or even replace digital image analysis within the visible range for characterizing steel surfaces in situ.},
	author = {Zohreh Zahiri and Bart Ribbens and Steve Vanlanduit and Paul Scheunders}
}
@article {1901,
	title = {Close-range hyperspectral image analysis for the early detection of plant stress responses in individual plants in a high-throughput phenotyping platform},
	journal = {ISPRS Journal of Photogrammetry and Remote Sensing },
	volume = {138},
	year = {2018},
	pages = {121-138},
	author = {Mohd Shahrimie Mohd Asaari and Puneet Mishra and Stien Mertens and Stijn Dhondt and Dirk Inze and Nathalie Wuyts and Paul Scheunders}
}
@inbook {1900,
	title = {Contributions of Machine Learning to Remote Sensing Data Analysis},
	booktitle = {Comprehensive Remote Sensing},
	volume = {2},
	year = {2018},
	pages = {199-243},
	author = {Paul Scheunders and Devis Tuia and G Moser}
}
@inproceedings {1898,
	title = {Detection of plant responses to drought using close-range hyperspectral imaging in a high-throughput phenotyping platform},
	booktitle = {IEEE Whispers 2018, Workshop on Hyperspectral Image and Signal Processing, Amsterdam, 23-26 September  },
	year = {2018},
	author = {Mohd Shahrimie Mohd Asaari and Stien Mertens and S Dhondt and Nathalie Wuyts and Paul Scheunders}
}
@inproceedings {1895,
	title = {Fusion of hyperspectral and Lidar images using non-subsampled shearlet transform},
	booktitle = {IEEE IGARSS 2018, International Geoscience and Remote Sensing Symposium, Valencia, Spain, July 23-27},
	year = {2018},
	author = {M. Reza Soleimanzadeh and Azam Karami and Paul Scheunders}
}
@inproceedings {1896,
	title = {Hyperspectral and multispectral image fusion based on spectral matching in the shearlet domain},
	booktitle = {IEEE IGARSS 2018, International Geoscience and Remote Sensing Symposium, Valencia, Spain, July 23-27},
	year = {2018},
	author = {H. Rezaei and Azam Karami and Paul Scheunders}
}
@inproceedings {1870,
	title = {MRF-based decision fusion for hyperspectral image classification},
	booktitle = {IEEE International Geoscience and Remote Sensing Symposium (IGARSS)},
	year = {2018},
	address = {Valencia, Spain},
	abstract = {The high dimensionality of hyperspectral images, the limited availability of ground-truth data as well as the low spatial resolution (causing pixels to contain mixtures of materials) hinder hyperspectral image classification. In this work we propose a novel hyperspectral classification method where we combine the outcome of spectral unmixing with the outcome
of a supervised classifier. In particular, we consider fractional abundances obtained from a Sparse Unmixing method along
with posterior probabilities acquired from a Multinomial Logistic Regression classifier. Both sources of information are
fused using a Markov Random Field framework. We conducted experiments on publicly available real hyperspectral
images: Indian Pines and University of Pavia using a very limited number of training samples. Our results indicate that
the proposed decision fusion approach significantly improves the classification result over using the individual sources and outperforms the state of the art methods.},
	url = {https://www.igarss2018.org/Papers/viewpapers.asp?papernum=2751},
	author = {Vera Andrejchenko and Rob Heylen and Wenzhi Liao and Wilfried Philips and Paul Scheunders}
}
@inproceedings {1875,
	title = {A NEURAL NETWORK METHOD FOR NONLINEAR HYPERSPECTRAL UNMIXING},
	booktitle = {IEEE International Geoscience and Remote Sensing Symposium (IGARSS)},
	year = {2018},
	pages = {pp. 4233-4236},
	address = {Valencia, Spain},
	abstract = {Because of the complex interaction of light with the Earth surface, a hyperspectral pixel can be composed of a highly nonlinear mixture of the reflectances of the materials on the ground. When nonlinear mixing models are applied, the estimated model parameters are usually hard to interpret and to link to the actual fractional abundances. Moreover, not all spectral reflectances in a real scene follow the same particular mixing model. In this paper, we present a supervised learning method for nonlinear spectral unmixing. In this method, a neural network is applied to learn mappings of the true spectral reflectances to the reflectances that would be obtained if the mixture was linear. A simple linear unmixing then reveals the actual abundance fractions. This technique is model-independent and allows for an easy interpretation of the obtained abundance fractions. We validate this method on several artificial datasets, a data set obtained by ray tracing, and a real dataset.},
	doi = {doi: 10.1109/IGARSS.2018.8518995},
	url = {https://ieeexplore.ieee.org/document/8518995},
	author = {Bikram Koirala and Rob Heylen and Paul Scheunders}
}
@article {1902,
	title = {Noise reduction in hyperspectral imagery: overview and application},
	journal = {Remote Sensing },
	volume = {10},
	year = {2018},
	pages = {482},
	author = {B Rasti and Paul Scheunders and P Ghesami and G Licciardi and Jocelyn Chanussot}
}
@article {1903,
	title = {A simple computer vision pipeline reveals the effects of isolation on social interaction dynamics in Drosophila},
	journal = {Plos Computational Biology},
	volume = {14},
	year = {2018},
	pages = {e1006410},
	author = {G Liu and Tanmay Nath and Z Guo and G Linneweber and A Claeys and J Li and M Bengochea and Steve De Backer and Barbara Weyn and M Sneyders and H Nicasy and P Yu and Paul Scheunders and Bassem Hassan}
}
@inproceedings {1897,
	title = {Spectral variability in a multilinear mixing model},
	booktitle = {IEEE IGARSS 2018, International Geoscience and Remote Sensing Symposium, Valencia, Spain, July 23-27},
	year = {2018},
	author = {T Dox and Rob Heylen and Paul Scheunders}
}
@proceedings {1904,
	title = {ACIVS 2017, Advanced Concepts for Intelligent Vision Systems},
	volume = {10617},
	year = {2017},
	author = {J Blanc-Talon and R Penne and Wilfried Philips and D Popescu and Paul Scheunders}
}
@inproceedings {1807,
	title = {Estimation of the intrinsic dimensionality in hyperspectral imagery via the hubness phenomenon},
	booktitle = {LVA ICA 2017, International conference on latent variable analysis and signal separation, Grenoble, France, February 21-23, Lecture Notes in Computer Science},
	volume = {10169},
	year = {2017},
	author = {Rob Heylen and Mario Parente and Paul Scheunders}
}
@article {1800,
	title = {Estimation of the number of endmembers in a hyperspectral image via the hubness phenomenon},
	journal = {IEEE Transactions on Geoscience and Remote Sensing},
	volume = {55},
	year = {2017},
	pages = {2191-2200},
	author = {Rob Heylen and Mario Parente and Paul Scheunders}
}
@article {1806,
	title = {Evaluating palmiet wetland decline: a comparison of three methods},
	journal = {Remote Sensing Applications: Society and Environment},
	volume = {8},
	year = {2017},
	pages = {212-223},
	author = {A J Rebelo and Paul Scheunders and K J Esler and P Meire}
}
@article {1803,
	title = {Fusion of Hyperspectral and LiDAR Data for Classification of Cloud-Shadow Mixed Remote Sensing Scene},
	journal = {IEEE-JSTARS, Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
	volume = {10},
	year = {2017},
	pages = {3768-3781},
	author = {Renbo Luo and Wenzhi Liao and H Zhang and L Zhang and Y Pi and Paul Scheunders and Wilfried Philips}
}
@article {1802,
	title = {Habitat mapping and quality assessment of NATURA 2000 Heatland using airborne imaging spectroscopy},
	journal = {Remote Sensing},
	volume = {9},
	year = {2017},
	chapter = {266},
	author = {Birgen Haest and Jeroen Vanden Borre and Toon Spanhove and Guy Thoonen and Stephanie Delalieux and Kooistra, L. and C A M{\"u}cher and D Paelinckx and Paul Scheunders and Pieter Kempeneers}
}
@article {1801,
	title = {Hyperspectral leaf reflectance of Carpines betulus L. saplings for urban air quality estimation},
	journal = {Environmental Pollution},
	volume = {220},
	year = {2017},
	pages = {159-167},
	author = {Melanka Brackx and Shari Van Wittenberghe and Jolien Verhelst and Paul Scheunders and Roeland Samson}
}
@inproceedings {1808,
	title = {Pixel purity vertex component analysis},
	booktitle = {IEEE IGARSS 2017, International Geoscience and Remote Sensing Symposium, Fort Worth, USA, July 23-28},
	year = {2017},
	author = {Rob Heylen and Mario Parente and Paul Scheunders}
}
@article {1804,
	title = {Spatial Resolution Enhancement of Hyperspectral Images Using Spectral Unmixing and Bayesian Sparse Representation},
	journal = {Remote Sensing},
	volume = {9},
	year = {2017},
	chapter = {541},
	author = {E.K. Ghasrodashti and Azam Karami and Rob Heylen and Paul Scheunders}
}
@article {1805,
	title = {On the use of dorsiventral reflectance asymmetry of hornbeam (Carpinus betulus L.) leaves  in air pollution estimation},
	journal = {Environmental Monitoring and Assessment},
	volume = {189},
	year = {2017},
	chapter = {472},
	author = {Melanka Brackx and Jolien Verhelst and Paul Scheunders and Roeland Samson}
}
@proceedings {1905,
	title = {ACIVS 2016, Advanced Concepts for Intelligent Vision Systems},
	volume = {10016},
	year = {2016},
	author = {C Distante and J Blanc-Talon and Wilfried Philips and D Popescu and Paul Scheunders}
}
@article {1686,
	title = {Advanced techniques for computational and information sciences},
	journal = {Mathematical Problems in Engineering},
	volume = {2016},
	year = {2016},
	edition = {Article ID 5623913},
	author = {W. Guo and C.-C. Hung and Paul Scheunders and B.-C. Kuo and Y. Lan and Z. Ma}
}
@inproceedings {1809,
	title = {Alternating angle minimization based unmixing with endmember variability},
	booktitle = {IEEE IGARSS 2016, International Geoscience and Remote Sensing Symposium, pp. 6974-6977, Beijing, July 10-15 },
	year = {2016},
	doi = {10.1109/IGARSS.2016.7730819},
	author = {Rob Heylen and Paul Scheunders and Alina Zare and Paul Gader}
}
@article {1641,
	title = {Automatic forensic analysis of automotive paints using optical microscopy},
	journal = {Forensic Science International},
	volume = {259},
	year = {2016},
	pages = {210-220},
	author = {Guy Thoonen and B. Nys and Y. Vander Haegen and G. De Roy and Paul Scheunders}
}
@inproceedings {1717,
	title = {Classification of hyperspectral images with very small training size using sparse unmixing},
	booktitle = {IEEE International Geoscience and Remote Sensing Symposium (IGARSS)},
	year = {2016},
	address = {Beijing, China},
	author = {Vera Andrejchenko and Rob Heylen and Paul Scheunders and Wilfried Philips and Wenzhi Liao}
}
@inproceedings {1737,
	title = {Close range hyperspectral imaging for plant phenotyping},
	booktitle = {Hyperspectral Imaging and Applications Conference},
	year = {2016},
	month = {October/2016},
	address = {Coventry, UK},
	author = {Puneet Mishra and Mohd Shahrimie Mohd Asaari and Stien Mertens and Nathalie Wuyts and Stijn Dhondt and Paul Scheunders}
}
@inproceedings {1700,
	title = {Compression Of Hyperspectral Images Using Block Coordinate Descent Search And Compressed Sensing},
	booktitle = {8th workshop on Hyperspectral Image and Signal Processing : Evolution in Remote Sensing},
	year = {2016},
	month = {08/2016},
	address = {Los Angeles, USA},
	author = {Shirin Hassanzadeh and Azam Karami and Rob Heylen and Paul Scheunders}
}
@inproceedings {1699,
	title = {Denoising Of Hyperspectral Images Using Shearlet Transform And Fully Constrained Least Squares Unmixing},
	booktitle = {8th workshop on Hyperspectral Image and Signal Processing : Evolution in Remote Sensing},
	year = {2016},
	month = {08/2016},
	address = { Los Angeles, USA},
	author = {Azam Karami and Rob Heylen and Paul Scheunders}
}
@article {1682,
	title = {Fusion of Hyperspectral and Multispectral Images Using Spectral Unmixing and Sparse Coding},
	journal = {IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
	volume = {9},
	year = {2016},
	pages = {2377-2389},
	abstract = {Unlike multispectral (MSI) and panchromatic (PAN) images, generally the spatial resolution of hyperspectral images (HSI) is limited, due to sensor limitations. In many applications, HSI with a high spectral as well as spatial resolution are required. In this paper, a new method for spatial resolution enhancement of a HSI using spectral unmixing and sparse coding (SUSC) is introduced. The proposed method fuses high spectral resolution features from the HSI with high spatial resolution features from an MSI of the same scene. Endmembers are extracted from the HSI by spectral unmixing, and the exact location of the endmembers is obtained from the MSI. This fusion process by using spectral unmixing is formulated as an ill-posed inverse problem which requires a regularization term in order to convert it into a well-posed inverse problem. As a regularizer, we employ sparse coding (SC), for which a dictionary is constructed using high spatial resolution MSI or PAN images from unrelated scenes. The proposed algorithm is applied to real Hyperion and ROSIS datasets. Compared with other state-of-the-art algorithms based on pansharpening, spectral unmixing, and SC methods, the proposed method is shown to significantly increase the spatial resolution while perserving the spectral content of the HSI.},
	issn = {1939-1404},
	doi = {10.1109/JSTARS.2016.2528339},
	author = {Zahara Hashemi Nezhad and Azam Karami and Rob Heylen and Paul Scheunders}
}
@article {1681,
	title = {Hyperspectral Image Compression Optimized for Spectral Unmixing},
	journal = {IEEE Transactions on Geoscience and Remote Sensing},
	volume = {pp},
	year = {2016},
	month = {06/2016},
	chapter = {1},
	abstract = {In this paper, we present a new lossy compression method for hyperspectral images that aims to optimally compress in both spatial and spectral domains and simultaneously minimizes the effect of the compression on linear spectral unmixing performance. To achieve this, a nonnegative Tucker decomposition is applied. This decomposition is a function of three dimension parameters. By employing a link between this decomposition and the linear spectral mixing model, an optimization problem is defined to find the optimal parameters by minimizing the root-mean-square error between the abundance matrices of the original and reconstructed data sets. The resulting optimization problem is solved by a particle swarm optimization algorithm. An approximate method for fast estimation of the free parameters is introduced as well. Our simulation results show that, in comparison with well-known state-of-the-art lossy compression methods, an improved compression and spectral unmixing performance of the reconstructed hyperspectral image is obtained. It is noteworthy to mention that the superiority of our method becomes more apparent as the compression ratio grows.},
	issn = {0196-2892},
	doi = {10.1109/TGRS.2016.2574757},
	author = {Azam Karami and Rob Heylen and Paul Scheunders}
}
@article {1685,
	title = {Hyperspectral unmixing with endmember variability via alternating angle minimization},
	journal = {IEEE Transactions on Geoscience and Remote Sensing},
	volume = {54},
	year = {2016},
	pages = {4983-4993},
	author = {Rob Heylen and Alina Zare and Paul Gader and Paul Scheunders}
}
@inproceedings {1810,
	title = {Lidar information extraction by attribute filters with partial reconstruction},
	booktitle = {IEEE IGARSS 2016, International Geoscience and Remote Sensing Symposium, pp. 1484-1487, Beijing, July 10-15 },
	year = {2016},
	doi = {10.1109/IGARSS.2016.7729379},
	author = {Wenzhi Liao and M Della Mura and X Huang and Jocelyn Chanussot and S Gautama and Paul Scheunders and Wilfried Philips}
}
@inproceedings {1736,
	title = {Modeling effects of illumination and plant geometry on leaf reflectance spectra in close-range hyperspectral imaging},
	booktitle = {8th WHISPERS - Evolution in Remote Sensing},
	year = {2016},
	month = {August/2016},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Los Angeles, USA},
	author = {Mohd Shahrimie Mohd Asaari and Puneet Mishra and Stien Mertens and Stijn Dhondt and Nathalie Wuyts and Paul Scheunders}
}
@article {1640,
	title = {A multilinear mixing model for nonlinear spectral unmixing},
	journal = {IEEE Transactions on Geoscience and Remote Sensing},
	volume = {54},
	year = {2016},
	pages = {240-251},
	author = {Rob Heylen and Paul Scheunders}
}
@inproceedings {1697,
	title = {Optical Solutions for Improving Spatial Resolution of Hyperspectral Sensors},
	booktitle = {8th workshop on Hyperspectral Image and Signal Processing : Evolution in Remote Sensing},
	year = {2016},
	month = {08/2016},
	address = { Los Angeles, USA},
	author = {Ashkan  Adibi and Azam Karami and Rob Heylen and Paul Scheunders}
}
@inproceedings {1698,
	title = {Superresolution Of Hyperspectral Images Using Spectral Unmixing And Sparse Regularization},
	booktitle = {IGARSS  2016},
	year = {2016},
	month = {07/2016},
	address = {Beijing China},
	author = {Zahara Hashemi Nezhad and Azam Karami and Rob Heylen and Paul Scheunders}
}
@proceedings {1906,
	title = {ACIVS 2015, Advanced Concepts for Intelligent Vision Systems},
	volume = {9386},
	year = {2015},
	author = {S Battiato and J Blanc-Talon and G Gallo and Wilfried Philips and D Popescu and Paul Scheunders}
}
@article {1571,
	title = {Band-specific Shearlet-based Hyperspectral Image Noise Reduction},
	journal = {IEEE Transaction Geosciences and Remote Sensing },
	volume = {53},
	number = {13},
	year = {2015},
	month = {03/2015},
	chapter = {1},
	author = {Azam Karami and Rob Heylen and Paul Scheunders}
}
@inproceedings {1637,
	title = {A fast alternative for the pixel purity index algorithm},
	booktitle = {IEEE IGARSS 2015, International Geoscience and Remote Sensing Symposium, Milan, Italy, July 26-31},
	year = {2015},
	pages = {1781-1784},
	author = {Rob Heylen and Muhamed Awais Akhter and Paul Scheunders}
}
@article {1528,
	title = {A geometric matched filter for hyperspectral target detection and partial unmixing},
	journal = {IEEE Geoscience and Remote Sensing letters},
	volume = {12},
	number = {3},
	year = {2015},
	pages = {661-665},
	author = {Muhamed Awais Akhter and Rob Heylen and Paul Scheunders}
}
@article {1526,
	title = {A geometric unmixing concept for the selection of optimal binary endmember combinations},
	journal = {IEEE Geoscience and Remote Sensing letters},
	volume = {12},
	number = {1},
	year = {2015},
	pages = {82-86},
	author = {Laurent Tits and Rob Heylen and Ben Somers and Paul Scheunders and Pol Coppin}
}
@inproceedings {1638,
	title = {Hyperspectral unmixing with projection onto convex sets using distance geometry},
	booktitle = {IEEE IGARSS 2015, International Geoscience and Remote Sensing Symposium, Milan, Italy, July 26-31},
	year = {2015},
	pages = {5059-5062},
	author = {Muhamed Awais Akhter and Rob Heylen and Paul Scheunders}
}
@inproceedings {1622,
	title = {Lossy Compression of Hyperspectral Images Optimizing Spectral Unmixing},
	booktitle = {IGARSS  2015},
	year = {2015},
	address = {Milan, Italy},
	author = {Azam Karami and R.Heylen and Paul Scheunders}
}
@inproceedings {1639,
	title = {Lossy Compression of hyperspectral images optimizing spectral unmixing},
	booktitle = {IEEE IGARSS 2015, International Geoscience and Remote Sensing Symposium, Milan, Italy, July 26-31},
	year = {2015},
	pages = {5031-034},
	author = {Azam Karami and Rob Heylen and Paul Scheunders}
}
@article {1529,
	title = {Nonlinear unmixing by using different metrics in a linear unmixing chain},
	journal = {IEEE-JSTARS, Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
	year = {2015},
	author = {Rob Heylen and Paul Scheunders and Paul Gader and A. Rangarajan}
}
@inproceedings {1636,
	title = {Nonlinear unmixing with a multilinear mixing model},
	booktitle = {IEEE Whispers 2015, Workshop on Hyperspectral Image and Signal Processing, June 2-5, Tokyo},
	year = {2015},
	author = {Rob Heylen and Paul Scheunders}
}
@inproceedings {1635,
	title = {Two-stage fusion of thermal hyperspectral and visible RGB image by PCA and guided filter},
	booktitle = {IEEE Whispers 2015, Workshop on Hyperspectral Image and Signal Processing, June 2-5, Tokyo},
	year = {2015},
	author = {W. Liao and F. Huang and F. v. Coillie and Guy Thoonen and Aleksandra Pizurica and Paul Scheunders and Wilfried Philips}
}
@inproceedings {1533,
	title = {Automated Social behaviour Recognition At Low Resolution},
	booktitle = {ICPR14, International Conference on Pattern Recognition, Stockholm, Sweden},
	year = {2014},
	author = {Tanmay, Nath and G. Liu and Bassem Hassan and Barbara Weyn and Steve De Backer and Paul Scheunders}
}
@inproceedings {1453,
	title = {Automated Social behaviour Recognition At Low Resolution},
	booktitle = {22nd IEEE - International Conference of Pattern Recognition (ICPR)},
	year = {2014},
	author = {Tanmay Nath and G.Liu and Barbara Weyn and Bassem Hassan and Steve De Backer and Paul Scheunders}
}
@article {1525,
	title = {A distance geometric framework for non-linear hyperspectral unmixing},
	journal = {IEEE-JSTARS, Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
	volume = {7},
	number = {6},
	year = {2014},
	pages = {1879-1888},
	author = {Rob Heylen and Paul Scheunders}
}
@inproceedings {1531,
	title = {Geometric matched filter for hyperspectral partial unmixing},
	booktitle = {IEEE-Whispers 2014, Workshop on Hyperspectral Image and Signal Processing, Lausanne, Suisse},
	year = {2014},
	author = {Muhamed Awais Akhter and Rob Heylen and Paul Scheunders}
}
@inproceedings {1569,
	title = {Hyperspectral Image Noise Reduction and its Effect on  Spectral Unmixing},
	booktitle = {IEEE-Whispers 2014, Workshop on Hyperspectral Image and Signal Processing},
	year = {2014},
	address = { Lausanne, Suisse},
	author = {Azam Karami and Rob Heylen and Paul Scheunders}
}
@inproceedings {1532,
	title = {Hyperspectral Image Noise Reduction and its Effect on  Spectral Unmixing},
	booktitle = {IEEE-Whispers 2014, Workshop on Hyperspectral Image and Signal Processing, Lausanne, Suisse},
	year = {2014},
	author = {Azam Karami and Rob Heylen and Paul Scheunders}
}
@inproceedings {1534,
	title = {Hyperspectral unmixing using an active set algorithm},
	booktitle = {IEEE ICIP 2014, International Conference on Image Processing, October 27-30, Paris, France },
	year = {2014},
	author = {Rob Heylen and Paul Scheunders}
}
@inproceedings {1530,
	title = {Nonlinear unmixing by using non-Euclidean metrics in a linear unmixing chain},
	booktitle = {IEEE-Whispers 2014, Workshop on Hyperspectral Image and Signal Processing, Lausanne, Suisse},
	year = {2014},
	author = {Rob Heylen and Paul Scheunders and A. Rangarajan and Paul Gader}
}
@inproceedings {1521,
	title = {Spectral adaptation of hyperspectral flight lines using VHR contextual information},
	booktitle = {Geoscience and Remote Sensing Symposium (IGARSS), 2014 IEEE International},
	year = {2014},
	pages = {2953-2956},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Quebec City, QC, Canada},
	abstract = {Due to technological constraints, hyperspectral earth observation imagery are often a mosaic of overlapping flight lines collected in different passes over the area of interest. This causes variations in aqcuisition conditions such that the reflected spectrum can vary significantly between these flight lines. Partly, this problem is solved by atmospherical correction, but residual spectral differences often remain. A probabilistic domain adaptation framework based on graph matching using Hidden Markov Random Fields was recently proposed for transforming hyperspectral data from one image to better correspond to the other. This paper investigates the use of scale and angle invariant textural features for improving the performance of the used Hidden Markov Random Field matching framework in the case of hyperspectral flight lines. These textural features are derived from the filtering of VHR optical imagery with a bank of Gabor filters with varying orientation, scale and frequency and subsequently rendering them invariant to scale and frequency by applying the 2D DFT on the filter responses in the scale and frequency space.},
	doi = {10.1109/IGARSS.2014.6947096},
	url = {http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6947096},
	author = {Jan-Pieter Jacobs and Guy Thoonen and Devis Tuia and Gustavo Camps-Valls and Pieter Kempeneers and Paul Scheunders}
}
@article {1527,
	title = {A spectral-unmixing approach to estimate water-mass concentrations in case-II waters},
	journal = {IEEE-JSTARS, Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
	volume = {7},
	number = {8},
	year = {2014},
	author = {Dzevdet Burazerovic and Rob Heylen and Dries Raymaekers and Els Knaeps and Paul Scheunders}
}
@proceedings {1907,
	title = {ACIVS 2013, Advanced Concepts for Intelligent Vision Systems},
	volume = {8129},
	year = {2013},
	author = {J Blanc-Talon and A Kazinski and Wilfried Philips and D Popescu and Paul Scheunders}
}
@article {1317,
	title = {Classification of heathland vegetation in a hierarchical contextual framework},
	journal = {International Journal of Remote Sensing},
	volume = {34},
	year = {2013},
	month = {2013},
	pages = {96 - 111},
	abstract = {Heathlands in Western Europe have shown dramatic declines over the last century and therefore have been given a high conservation priority in the Habitats Directive of the European Union (EU). Accurate surveying and monitoring of heathland habitats is essential for appropriate conservation management, but the large heterogeneity of vegetation types within habitats as well as the occurrence of similar vegetation across habitat types hinders a straightforward, automated mapping based on aerial images. In such a case, a context-dependent classification algorithm is expected to be superior to traditional classification techniques. This article presents a novel approach to map the conservation status of heathland vegetation by using a hierarchical classification scheme that describes the structural dependencies in the field between the basic vegetation and the land-cover types that habitats are composed of. These dependency relationships are included as contextual information in the classification process, using a tree-structured Markov random field (TS-MRF) technique with a tree that reflects the hierarchy of the classification scheme. Results of this approach for a heathland area in Belgium were compared with results from more conventional classification approaches. Validation of the results showed that the structure of the scheme contained important spatial relationships, which were further reinforced by using the contextual classification strategy, especially for the most detailed level of the classification scheme. Accuracy increased and the classification results were more suitable for visual interpretation.},
	issn = {0143-1161},
	doi = {10.1080/01431161.2012.708061},
	author = {Guy Thoonen and Toon Spanhove and Jeroen Vanden Borre and Paul Scheunders}
}
@article {1379,
	title = {Contextual Subpixel Mapping of Hyperspectral Images Making Use of a High Resolution Color Image},
	journal = {IEEE JSTARS, Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
	volume = {6},
	year = {2013},
	month = {2013},
	pages = {779 - 791},
	abstract = {This paper describes a hyperspectral image classification method to obtain classification maps at a finer resolution than the image{\textquoteright}s original resolution. We assume that a complementary color image of high spatial resolution is available. The proposed methodology consists of a soft classification procedure to obtain landcover fractions, followed by a subpixel mapping of these fractions. While the main contribution of this article is in fact the complete multisource framework for obtaining a subpixel map, the major novelty of this subpixel mapping approach is the inclusion of contextual information, obtained from the color image. Experiments, conducted on two hyperspectral images and one real multi source data set, show excellent results, when compared to classification of the hyperspectral data only. The advantage of the contextual approach, compared to conventional subpixel mapping approaches, is clearly demonstrated.},
	keywords = {fusion, hyperspectral data, spectral unmixing, subpixel mapping, superresolution},
	issn = {1939-1404},
	doi = {10.1109/JSTARS.2012.2236539},
	author = {Zahid Mahmood and Muhamed Awais Akhter and Guy Thoonen and Paul Scheunders}
}
@article {1409,
	title = {Detecting the adjacency effect in hyperspectral imagery with spectral unmixing techniques},
	journal = {IEEE JSTARS, Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
	volume = {6},
	year = {2013},
	pages = {1070-1078},
	author = {Dzevdet Burazerovic and Bert Geens and Rob Heylen and S Sterckx and Paul Scheunders}
}
@inproceedings {Jacobs13,
	title = {Domain adaptation with Hidden Markov Random Fields},
	booktitle = {Geoscience and Remote Sensing Symposium (IGARSS), 2013 IEEE International},
	year = {2013},
	month = {July},
	pages = {3112-3115},
	address = {Melbourne, VIC, Australia},
	abstract = {In this paper, we propose a method to match multitemporal sequences of hyperspectral images using Hidden Markov Random Fields. Based on the matching of the data manifold, the algorithm matches the reflectance spectra of the classes, thus allowing the reuse of labeled examples acquired on one image to classify the other. This allows valorization of spectra collected in situ to other acquisitions than the one they were acquired for, without user supervision, prior knowledge of the class reflectance in the new domain or global information about atmospheric conditions.},
	keywords = {domain adaptation, graph matching, Hidden Markov Random Fields, Multitemporal classification},
	issn = {2153-6996},
	doi = {10.1109/IGARSS.2013.6723485},
	author = {Jan-Pieter Jacobs and Guy Thoonen and Devis Tuia and Gustavo Camps-Valls and Birgen Haest and Paul Scheunders}
}
@inproceedings {1523,
	title = {Handling spectral variability with alternating angle minimization},
	booktitle = {IEEE-WHISPERS 2013, Workshop on Hyperspectral Image and Signal Processing, Gainesville, Florida, June 25-28},
	year = {2013},
	author = {Rob Heylen and Paul Scheunders and Paul Gader}
}
@inproceedings {1508,
	title = {Hyperspectral image subpixel mapping using Getis index},
	booktitle = {IEEE-WHISPERS 2013, Workshop on Hyperspectral Image and Signal Processing, Gainesville, Florida, June 25-28, 2013},
	year = {2013},
	author = {Muhamed Awais Akhter and Zahid Mahmood and Paul Scheunders}
}
@article {1408,
	title = {Hyperspectral intrinsic dimensionality estimation with nearest-neighbor distance ratio{\textquoteright}s},
	journal = {IEEE JSTARS, Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
	volume = {6},
	year = {2013},
	pages = {570-579},
	author = {Rob Heylen and Paul Scheunders}
}
@article {1410,
	title = {Hyperspectral remote sensing data analysis and future challenges},
	journal = {IEEE Geoscience and Remote Sensing Magazine},
	volume = {1},
	year = {2013},
	pages = {6-36},
	author = {Jose Bioucas-Dias and Antonio Plaza and Gustavo Camps-Valls and Paul Scheunders and Nasser Nasrabadi and Jocedlyn Chanussot}
}
@inproceedings {1524,
	title = {Improving the efficiency of MESMA through geometric unmixing principles},
	booktitle = {SPIE Remote Sensing, Drseden, Germany, 23-26 September },
	volume = {8892},
	number = {UNSP 88920Q},
	year = {2013},
	author = {Laurent Tits and Ben Somers and Rob Heylen and Paul Scheunders and Pol Coppin}
}
@inproceedings {1522,
	title = {Multi-dimensional Pixel Purity Index},
	booktitle = {IEEE-WHISPERS 2013, Workshop on Hyperspectral Image and Signal Processing, Gainesville, Florida, June 25-28},
	year = {2013},
	author = {Rob Heylen and Paul Scheunders}
}
@article {1371,
	title = {Multi-dimensional pixel purity index for convex hull estimation and endmember extraction},
	journal = {IEEE Transactions on Geoscience and Remote Sensing},
	volume = {51},
	year = {2013},
	pages = {4059-4069},
	author = {Rob Heylen and Paul Scheunders}
}
@article {1347,
	title = {Semi-supervised local discriminant analysis for feature extraction in hyperspectral images},
	journal = {IEEE Transactions on Geoscience and Remote Sensing},
	volume = {51},
	year = {2013},
	pages = {184-198},
	author = {W. Liao and Aleksandra Pizurica and Paul Scheunders and Wilfried Philips and Y Pi}
}
@inproceedings {1509,
	title = {Solving the Hyperspectral Unmixing Problem with Projection Onto Convex Sets},
	booktitle = {21st European Signal Processing Conference (EUSIPCO), September 2013, Marrakech, Morocco},
	year = {2013},
	author = {Rob Heylen and Muhamed Awais Akhter and Paul Scheunders}
}
@inproceedings {tanmay2013tracking,
	title = {Tracking for Quantifying Social Network of Drosophila Melanogaster},
	booktitle = {15th Computer Analysis of Images and Patterns (CAIP)},
	volume = {8048},
	year = {2013},
	pages = {539{\textendash}545},
	publisher = {Springer},
	organization = {Springer},
	author = {Tanmay Nath and Liu, Guangda and Barbara Weyn and Bassem Hassan and Steve De Backer and Paul Scheunders}
}
@article {1505,
	title = {On using projection onto convex sets for solving the hyperspectral unmixing problem},
	journal = {IEEE Geoscience and Remote Sensing Letters},
	year = {2013},
	author = {Rob Heylen and Muhamed Awais Akhter and Paul Scheunders}
}
@article {ThoonenHufkensBorreSpanhovepscheund2011,
	title = {Accuracy assessment of contextual classification results for vegetation mapping},
	journal = {International Journal of Applied Earth Observation and Geoinformation},
	volume = {15},
	year = {2012},
	pages = {7 - 15},
	abstract = {A new procedure for quantitatively assessing the geometric accuracy of thematic maps, obtained from classifying hyperspectral remote sensing data, is presented. More specifically, the methodology is aimed at the comparison between results from any of the currently popular contextual classification strategies. The proposed procedure characterises the shapes of all objects in a classified image by defining an appropriate reference and a new quality measure. The results from the proposed procedure are represented in an intuitive way, by means of an error matrix, analogous to the confusion matrix used in traditional thematic accuracy representation. A suitable application for the methodology is vegetation mapping, where lots of closely related and spatially connected land cover types are to be distinguished. Consequently, the procedure is tested on a heathland vegetation mapping problem, related to Natura 2000 habitat monitoring. Object-based mapping and Markov Random Field classification results are compared, showing that the selected Markov Random Fields approach is more suitable for the fine-scale problem at hand, which is confirmed by the proposed procedure.},
	keywords = {Accuracy assessment, Confusion matrix, Contextual classification, Natura 2000},
	issn = {0303-2434},
	doi = {10.1016/j.jag.2011.05.013},
	url = {http://www.sciencedirect.com/science/article/pii/S0303243411000766},
	author = {Guy Thoonen and Koen Hufkens and Jeroen Vanden Borre and Toon Spanhove and Paul Scheunders}
}
@proceedings {1373,
	title = {ACIVS 2012, Advanced Concepts for Intelligent Vision Systems},
	volume = {7517},
	year = {2012},
	publisher = {Springer},
	author = {J Blanc-Talon and Wilfried Philips and D Popescu and Paul Scheunders and P. Zemcik}
}
@inproceedings {1352,
	title = {Automatic threshold selection for morphological attribute profiles},
	booktitle = {IEEE IGARSS2012, International Geoscience and Remote Sensing Symposium, Munich, July 22-27},
	year = {2012},
	pages = {4946-4949},
	author = {Zahid Mahmood and Guy Thoonen and Paul Scheunders}
}
@article {1318,
	title = {A Bayesian Restoration Approach for Hyperspectral Images},
	journal = {IEEE Transactions on Geoscience and Remote Sensing},
	volume = {50},
	year = {2012},
	pages = {3453-3462},
	author = {Y. Zhang and A. Duijster and Paul Scheunders}
}
@article {1316,
	title = {Calculation of geodesic distances in non-linear mixing models: demonstration on the generalized bilinear model},
	journal = {IEEE Geoscience and Remote Sensing letters},
	volume = {9},
	year = {2012},
	pages = {644-648},
	author = {Rob Heylen and Paul Scheunders}
}
@mastersthesis {1333,
	title = {Contextual classification of hyperspectral remote sensing images - Application in vegetation monitoring},
	year = {2012},
	pages = {156},
	school = {University of Antwerp},
	type = {PhD thesis},
	address = {Antwerp, Belgium},
	author = {Guy Thoonen}
}
@inproceedings {1351,
	title = {Estimating the number of endmembers in hyperspectral imagery with nearest neighbor distances},
	booktitle = {IEEE IGARSS2012, International Geoscience and Remote Sensing Symposium, Munich, July 22-27},
	year = {2012},
	pages = {1377-1380},
	author = {Rob Heylen and Paul Scheunders}
}
@inproceedings {1354,
	title = {A fast geometric algorithm for solving the inversion problem in spectral unmixing},
	booktitle = {IEEE-WHISPERS 2012, Workshop on Hyperspectral Image and Signal Processing, Shanghai, June 4-7},
	year = {2012},
	author = {Rob Heylen and Paul Scheunders}
}
@article {Verdoolaegepscheund2011,
	title = {On the geometry of Multivariate Generalized Gaussian models},
	journal = {Journal of Mathematical Imaging and Vision},
	volume = {43},
	year = {2012},
	pages = {180-193},
	author = {G. Verdoolaege and Paul Scheunders}
}
@inproceedings {1356,
	title = {Imrpoving PLCA-based score-informed source separation with invertible constant-Q transform},
	booktitle = {EUSIPCO 2012, 20th European Signal Processing Conference, Bucharest, Romania, august 27-31 },
	year = {2012},
	pages = {2634-2638},
	author = {Joachim Ganseman and Paul Scheunders and S. Dixon}
}
@inproceedings {1357,
	title = {A jump start for NMF with N-Findr and NNLS},
	booktitle = {DAFx-12, 15-th Int. Conference on Digital Audio Effects, York, UK, september 17-21},
	year = {2012},
	author = {Joachim Ganseman and Paul Scheunders}
}
@article {ThoonenMahmoodPeeterspscheund2012,
	title = {Multisource classification of color and hyperspectral images using color attribute profiles and composite decision fusion},
	journal = {Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of},
	volume = {5},
	number = {2},
	year = {2012},
	pages = {510 - 521},
	abstract = {In this work, we treat the problem of combined classification of a high spatial resolution color image and a lower spatial resolution hyperspectral image of the same scene. The problem is particularly challenging, since we aim for classification maps at the spatial resolution of the color image. Contextual information is obtained from the color image by introducing Color Attribute Profiles (CAPs). Instead of treating the {\textquoteleft}R{\textquoteright}, {\textquoteleft}G{\textquoteright}, and {\textquoteleft}B{\textquoteright} bands separately, the color image is transformed into CIE-Lab space. In this color space, attribute profiles are extracted from the {\textquoteleft}L{\textquoteright} band, which corresponds to the Luminance, while the {\textquoteleft}a{\textquoteright} and {\textquoteleft}b{\textquoteright} bands, which contain the color information, are kept intact, and the resulting images are transformed back into RGB space. The spectral information is obtained from the hyperspectral image. A Composite Decision Fusion (CDF) strategy is proposed, combining a state-of-the-art kernel-based decision fusion technique with the popular composite kernel classification approach. Experiments are conducted, using simulated data and a real multisource dataset containing airborne hyperspectral data and orthophotographic data from a suburban area in Belgium. These experiments show that our CAPs perform well with respect to other approaches for extracting attribute profiles from high resolution color images, and that the proposed CDF strategy produces meaningful results with respect to concatenation and the highlighted state-of-the-art approaches for combining multisource data.},
	keywords = {Color, image classification, morphological operations, multiresolution techniques, multisensor systems},
	issn = {1939-1404},
	doi = {10.1109/JSTARS.2011.2168317},
	author = {Guy Thoonen and Zahid Mahmood and Stijn Peeters and Paul Scheunders}
}
@inproceedings {1507,
	title = {Subpixel mapping of hyperspectral data using high resolution color images},
	booktitle = {IEEE-WHISPERS 2012, Workshop on Hyperspectral Image and Signal Processing, Shanghai, June 4-7},
	year = {2012},
	author = {Zahid Mahmood and Guy Thoonen and Muhamed Awais Akhter and Paul Scheunders}
}
@inproceedings {1353,
	title = {Unmixing for detection and quantification of adjacency effects},
	booktitle = {IEEE IGARSS2012, International Geoscience and Remote Sensing Symposium, Munich, July 22-27},
	year = {2012},
	pages = {3090-3093},
	author = {Dzevdet Burazerovic and Bert Geens and Rob Heylen and S Sterckx and Paul Scheunders}
}
@inbook {1257,
	title = {Wavelet-based Multi/Hyperspectral Image Restoration and Fusion},
	booktitle = {Signal and Image Processing for Remote Sensing},
	year = {2012},
	pages = {505-523},
	publisher = {Taylor and Francis},
	organization = {Taylor and Francis},
	chapter = {25},
	author = {Paul Scheunders and A. Duijster and Y. Zhang},
	editor = {C.H. Chen}
}
@proceedings {1235,
	title = {ACIVS 2011, Advanced Concepts for Intelligent Vision Systems},
	volume = {6915},
	year = {2011},
	publisher = {Springer},
	author = {J Blanc-Talon and Wilfried Philips and D Popescu and R. Kleihorst and Paul Scheunders}
}
@article {zmaiR.jbatenbumverhoyepscheundjsijbers2011,
	title = {Bias field reduction by localized Lloyd-Max quantization},
	journal = {Magnetic Resonance Imaging},
	volume = {29},
	year = {2011},
	pages = {536-545},
	doi = {http://dx.doi.org/10.1016/j.mri.2010.10.015},
	author = {Zhenhua Mai and Rudolf Hanel and Kees Joost Batenburg and Marleen Verhoye and Paul Scheunders and Jan Sijbers}
}
@inproceedings {WitteThoonenpscheundPizuricaPhilips2011,
	title = {Classification of multi-source images using color mathematical morphological profiles},
	booktitle = {Geoscience and Remote Sensing Symposium (IGARSS), 2011 IEEE International},
	year = {2011},
	pages = {3919 - 3922},
	address = {Vancouver, BC, Canada},
	abstract = {In the remote sensing domain data from many different sources are often available. Each of these data sources are characterized by their own sensor- and platform-specific properties, i.e. spectral range, or spatial and spectral resolution. In this paper we consider a low spatial, but high spectral resolution satellite image, together with its high spatial resolution RGB color image, e.g. obtained by UAV. Spatial features are extracted from the color image by combining the three color bands R, G and B, ordering these color vectors, and presenting color mathematical morphological profiles accordingly. This way the spatial information contained in the correlation between the different bands is completely taken into account and thus also totally preserved in the feature extraction. In a classification experiment these color morphological profiles are combined with the spectral features of the hyperspectral image, and we show that the spatial characterization of the color image is improved.},
	keywords = {Classification, Color, Morphological profiles, Multisource images, remote sensing},
	isbn = {978-1-4577-1003-2},
	issn = {2153-6996},
	doi = {10.1109/IGARSS.2011.6050088},
	url = {http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=\&arnumber=6050088\&isnumber=6048881},
	author = {Val{\'e}rie De Witte and Guy Thoonen and Paul Scheunders and Aleksandra Pizurica and Wilfried Philips}
}
@article {Mahmoodpscheund2011,
	title = {Enhanced visualization of hyperspectral images},
	journal = {IEEE Geoscience and Remote Sensing letters},
	volume = {8},
	number = {5},
	year = {2011},
	pages = {869-873},
	author = {Zahid Mahmood and Paul Scheunders}
}
@article {HeylenBurazerovicpscheund2011,
	title = {Fully constrained least-squares spectral unmixing by simplex projection},
	journal = {IEEE Transactions on Geoscience and Remote Sensing},
	volume = {49},
	year = {2011},
	pages = {4112-4122},
	author = {Rob Heylen and Dzevdet Burazerovic and Paul Scheunders}
}
@article {Verdoolaegepscheund2011,
	title = {Geodesics on the Manifold of Multivariate Generalized Gaussian Distributions With an Application to Multicomponent Texture Discrimination},
	journal = {International Journal of Computer Vision},
	volume = {95},
	number = {3},
	year = {2011},
	pages = {265-286},
	author = {G. Verdoolaege and Paul Scheunders}
}
@inproceedings {MahmoodWittepscheund2011,
	title = {Multi-source image classification using color attribute profiles},
	booktitle = {3rd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)},
	year = {2011},
	pages = {1 - 4},
	address = {Lisbon, Portugal},
	abstract = {This work introduces a method to extract attribute profiles from RGB color images with high spatial resolution, for instance images acquired from Unmanned Aerial Vehicles (UAV). The resulting Color Attribute Profiles (CAP) are intended to improve the classification of low spatial resolution hyperspectral images by merging the attribute features with the spectral features of the hyperspectral image. Instead of treating the R, G and B bands separately, the color image is transformed into CIE-Lab space. In this color space, attribute profiles are extracted from the {\textquoteleft}L{\textquoteright} band, while the {\textquoteleft}a{\textquoteright} and {\textquoteleft}b{\textquoteright} bands are kept intact, and the resulting images are transformed back into RGB space. In our experiments, classification results using this methodology are compared to classification results using other strategies for extracting attribute profiles in CIE-Lab space, as well as regular grayscale attribute profiles.},
	keywords = {Attributes, Classification, Color, Hyperspectral, Morphology},
	isbn = {978-1-4577-2202-8},
	issn = {2158-6268},
	doi = {10.1109/WHISPERS.2011.6080859},
	url = {http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=\&arnumber=6080859\&isnumber=6080842},
	author = {Zahid Mahmood and Guy Thoonen and Val{\'e}rie De Witte and Paul Scheunders}
}
@inproceedings {Heylenpscheund2011,
	title = {Non-linear fully-constrained spectral unmixing},
	booktitle = {IEEE IGARSS2011, IEEE International Geoscience and Remote Sensing Symposium, Vancouver, July 25-29},
	year = {2011},
	author = {Rob Heylen and Paul Scheunders}
}
@article {HeylenBurazerovicpscheund2011,
	title = {Nonlinear spectral unmixing by geodesic simplex volume maximization},
	journal = {IEEE Journal of Selected Topics in Signal Processing},
	volume = {5},
	number = {3},
	year = {2011},
	pages = {534-542},
	author = {Rob Heylen and Dzevdet Burazerovic and Paul Scheunders}
}
@inproceedings {Heylenpscheund2011,
	title = {Spectral unmixing using distance geometry},
	booktitle = {IEEE-WHISPERS 2011, Workshop on Hperspectral Image and Signal Processing, Lisbon, Portugal, 6-9 June},
	year = {2011},
	author = {Rob Heylen and Paul Scheunders}
}
@inproceedings {BurazerovicHeylenpscheund2011,
	title = {Towards streaming hyperspectral endmember extraction},
	booktitle = {IEEE IGARSS2011, IEEE International Geoscience and Remote Sensing Symposium , Vancouver, July 25-29},
	year = {2011},
	author = {Dzevdet Burazerovic and Rob Heylen and Paul Scheunders}
}
@proceedings {1236,
	title = {ACIVS 2010, Advanced Concepts for Intelligent Vision Systems},
	volume = {6474 \& 6475},
	year = {2010},
	publisher = {Springer},
	author = {J Blanc-Talon and D. Bone and Wilfried Philips and D Popescu and Paul Scheunders}
}
@inproceedings {5652813,
	title = {Enhanced visualization of hyperspectral images},
	booktitle = {Geoscience and Remote Sensing Symposium (IGARSS), 2010 IEEE International},
	year = {2010},
	month = {july},
	pages = {991 -994},
	keywords = {color matching, data visualisation, geophysical image processing, gradient methods, human vision system, hyperspectral image, hyperspectral visualization algorithm, image colour analysis, image matching, image resolution, multiband gradient information, multiresolution framework, multiscale fundamental form representation, wavelet transforms, wavelets},
	issn = {2153-6996},
	doi = {10.1109/IGARSS.2010.5652813},
	author = {Zahid Mahmood and Paul Scheunders}
}
@inproceedings {GansemanMysoreScheundersAbel2010,
	title = {Evaluation of a score-informed source separation system},
	booktitle = {Proc. 11th International Society for Music Information Retrieval Conference (ISMIR 2010), Utrecht, the Netherlands, August 2010},
	year = {2010},
	pages = {219-224},
	author = {Joachim Ganseman and Gautham J. Mysore and Paul Scheunders and Jonathan S. Abel}
}
@inproceedings {HeylenBurazerovicScheunders2010,
	title = {A graph-based method for non-linear unmixing of hyperspectral imagery},
	booktitle = {IEEE IGARSS2010, IEEE International Geoscience and Remote Sensing Symposium, Honolulu, Haway, July 25-30},
	year = {2010},
	pages = {197-200},
	author = {Rob Heylen and Dzevdet Burazerovic and Paul Scheunders}
}
@inproceedings {Thoonen10,
	title = {Habitat mapping and quality assessment of heathlands using a modified kernel-based reclassification technique},
	booktitle = {Geoscience and Remote Sensing Symposium (IGARSS), 2010 IEEE International},
	year = {2010},
	month = {July},
	pages = {2707 - 2710},
	address = {Honolulu, HI, USA},
	abstract = {This article presents a method for acquiring habitat maps, intended for monitoring and evaluating the conservation status of heathland vegetation, starting from thematic land cover maps. The procedure is a modified kernel-based reclassification technique, that fits into a complete habitat quality assessment framework. Part one of the procedure shifts a small square kernel over the land cover map and assigns a habitat type to each position that complies with a single set of expert rules, related to the land cover composition in that position. Part two fills the gaps, by assigning a habitat type to any of the map positions that don{\textquoteright}t conform to any of the rules, or to more than one set of rules, by using a distance measure. The technique is tested on real data from a heathland site and shows some promising results.},
	keywords = {Belgium, environmental factors, geophysical image processing, geophysical techniques, habitat mapping, heathland vegetation, image classification, Kalmthoutse Heide, kernel-based reclassification technique, quality assessment, terrain mapping, thematic land cover maps, vegetation mapping},
	isbn = {978-1-4244-9565-8},
	issn = {2153-6996},
	doi = {10.1109/IGARSS.2010.5649240},
	author = {Guy Thoonen and Toon Spanhove and Haest, B. and Jeroen Vanden Borre and Paul Scheunders}
}
@article {Hufkens10,
	title = {Habitat reporting of a heathland site: Classification probabilities as additional information, a case study},
	journal = {Ecological Informatics},
	volume = {5},
	number = {4},
	year = {2010},
	pages = {248 - 255},
	abstract = {Heathlands are man-made habitats and their decline during the last century can be contributed to shifts in both agricultural and management practices as well as to hydrological and atmospheric changes. As a result, many heathland sites, including the Kalmthoutse Heide in Belgium, were included in the European Natura 2000 program, a network of protected areas across the European Union. To assure an accurate mapping of the Kalmthoutse Heide and other Natura 2000 sites in Belgium a classification framework for habitat status reporting with remote sensing data and in particular high resolution hyperspectral imagery was started. In this study we propose a simple and fast context based method for mapping heathland heterogeneity using the intermediate, otherwise redundant, classification probabilities as generated by a hard classification algorithm. Our study proved to be successful in using intermediate classification probabilities as a valuable source of ecological information. The delineated areas have been shown to be statistically sound and robust compared to a neutral model. The technique is not limited to a particular hard classification technique and can easily be adopted into current vegetation monitoring efforts. The resulting maps provided accessible maps which can support management of the protected site and enhance the accuracy of EU reportage as required by the habitat directive.},
	keywords = {Heterogeneity, Hyperspectral, Patch, remote sensing, Uncertainty, Vegetation},
	issn = {1574-9541},
	doi = {10.1016/j.ecoinf.2009.09.002},
	url = {http://www.sciencedirect.com/science/article/pii/S1574954109000740},
	author = {Koen Hufkens and Guy Thoonen and Jeroen Vanden Borre and Paul Scheunders and Reinhart Ceulemans}
}
@inproceedings {Heylenpscheund2010,
	title = {Nonlinear barycentric dimensionality reduction},
	booktitle = {IEEE ICIP10, IEEE International Conference on Image Processing, Hong Kong, september 26-29},
	year = {2010},
	pages = {1341-1344},
	author = {Rob Heylen and Paul Scheunders}
}
@inproceedings {Haest10,
	title = {An object-based approach to quantity and quality assessment of heathland habitats in the framework of NATURA 2000 using hyperspectral airborne AHS images.},
	booktitle = {Proceedings of GEOBIA 2010, the Geographic Object-Based Image Analysis Conference},
	volume = {XXXVIII-4/C7},
	year = {2010},
	month = {July},
	abstract = {Straightforward mapping of detailed heathland habitat patches and their quality using remote sensing is hampered by (1) the intrinsic property of a high heterogeneity in habitat species composition (i.e. high intra-variability), and (2) the occurrence of the same species in multiple habitat types (i.e. low inter-variability). Mapping accuracy of detailed habitat objects can however be improved by using an advanced approach that specifically takes into account and exploits these inherent patch characteristics. To demonstrate the idea, we developed and applied a multi-step mapping framework on a protected semi-natural heathland area in the north of Belgium. The method consecutively consists of (1) a 4-level hierarchical land cover classification of hyperspectral airborne AHS image data, and (2) a kernel-based structural re-classification algorithm in combination with habitat patch object composition definitions. Detailed land cover composition data were collected in 1325 field plots. Multi-variate analysis (Wards clustering; TWINSPAN) of these data led to the design of meaningful land cover classes in a dedicated classification scheme. Subsequently, the data were used as reference for the classification of hyperspectral AHS image data. Linear Discriminant Analysis in combination with Sequential-Floating-Forward-Selection (SFFS-LDA) was applied to classify the hyperspectral images. Classification accuracies of these maps are in the order of 74-93\% (Kappa= 0.81-0.92) depending on the classification detail. To subsequently obtain habitat patch (object) maps, the land cover classifications were used as input for a kernel-based spatial re-classification process, in combination with a rule-set that relates specific Natura 2000 habitats with a composition range of the land cover classes. The resulting habitat patch maps illustrate the methodologys potential for detailed heathland habitat characterization using hyperspectral image data, and hence contribute to the improved mapping and understanding of heathland habitat, essential for the EU member states reporting obligations under the Habitats Directive.},
	keywords = {Application, Classification, Contextual, Ecosystem, Hyper spectral, Landscape, Object, Vegetation},
	url = {http://www.isprs.org/proceedings/XXXVIII/4-C7/papers\%20proceedings/Haest_211_An_object-based_approach_to_quantity_and_quality_assessment_of_heath_land_habitats.pdf},
	author = {Haest, B. and Guy Thoonen and Jeroen Vanden Borre and Toon Spanhove and Stephanie Delalieux and L. Bertels and Kooistra, L. and C A M{\"u}cher and Paul Scheunders}
}
@inproceedings {GansemanMysoreScheundersAbel2010,
	title = {Source separation by score synthesis},
	booktitle = {Proc. International Computer Music Conference (ICMC 2010), New York, NY, June 1-5},
	year = {2010},
	pages = {462-465},
	author = {Joachim Ganseman and Gautham J. Mysore and Paul Scheunders and Jonathan S. Abel}
}
@inproceedings {Thoonen10b,
	title = {Using patch metrics as validation for contextual classification of heathland vegetation.},
	booktitle = {Proceedings of GEOBIA 2010, the Geographic Object-Based Image Analysis Conference},
	volume = {XXXVIII-4/C7},
	year = {2010},
	month = {July},
	abstract = {This article presents a method to assess the accuracy of vegetation maps for which contextual information has been included in the classification process. It is well known that land use classification may benefit from combining spatial and spectral information. Consequently, many classification techniques incorporating spatial information have been implemented. To compare various contextual classification techniques, the shape of vegetation patches, in the spatially enhanced maps, are statistically linked to their counterparts in the spectral classification result, on which these spatial enhancements are applied. To this end, measures for the change in shape of patches are introduced. The shape of any patch is characterized by the edges between the patch and its neighbors. Therefore, patch shape can be represented by an edge map in which each pixel gets the value of the number of classes that are different from the class label of the central pixel in a four-adjacency neighborhood. Rather than defining a single metric for the edge map difference, an error matrix is used to depict not only how many edges have changed with respect to the reference, but also by how much they have changed. The method is tested on contextual classification results of heathland vegetation.},
	keywords = {Accuracy, Classification, Contextual, Hierarchical, Hyper spectral, Quality, Spatial, Vegetation},
	url = {http://www.isprs.org/proceedings/XXXVIII/4-C7/papers\%20proceedings/Thoonen_44_UsingPatchMetricsAsValidationForContextualClassificationOfHeathlandVegetation.pdf},
	author = {Guy Thoonen and Koen Hufkens and Jeroen Vanden Borre and Paul Scheunders}
}
@proceedings {1237,
	title = {ACIVS 2009, Advanced Concepts for Intelligent Vision Systems},
	volume = {5807},
	year = {2009},
	publisher = {Springer},
	author = {J Blanc-Talon and Wilfried Philips and D Popescu and Paul Scheunders}
}
@inproceedings {Thoonen09,
	title = {Assessing the quality of heathland vegetation by classification of hyperspectral data using spatial information},
	booktitle = {Geoscience and Remote Sensing Symposium,2009 IEEE International,IGARSS 2009},
	volume = {4},
	year = {2009},
	month = {July},
	pages = {IV-330 - IV-333},
	address = {Cape Town, South Africa},
	abstract = {This article deals with a method for acquiring vegetation maps, suitable for monitoring and evaluating the conservation status of heathland vegetation from hyperspectral data. The applied method is a recursive supervised segmentation algorithm based on a Tree-structured Markov Random Field (TS-MRF), capable of incorporating structural dependencies in the classification process. To this end, a tree structure is used that is built upon structural dependencies that are present in the field. The classification results from this TS-MRF with extended tree are compared to pixel-based classification results, results from a simple smoothing post-processing, and the result from the original binary TS-MRF technique.},
	keywords = {geophysical signal processing, heathland vegetation conservation, heathland vegetation quality, hyperspectral data classification, image segmentation, Markov processes, pixel based classification comparison, recursive supervised segmentation algorithm, smoothing post processing comparison, spatial information, tree structured Markov random field, trees (mathematics), TS-MRF, vegetation mapping, vegetation maps},
	isbn = {978-1-4244-3394-0},
	doi = {10.1109/IGARSS.2009.5417380},
	author = {Guy Thoonen and Jeroen Vanden Borre and Steve De Backer and Paul Scheunders}
}
@inproceedings {pscheund2009,
	title = {Bayesian techniques for multi/hyperspectral image processing},
	booktitle = {4th IEEE Conference on Industrial Electronics and Applications, Xi{\textquoteright}an, China, May 25-27},
	year = {2009},
	pages = {1-10},
	author = {Paul Scheunders}
}
@inproceedings {aduijstepscheund2009,
	title = {A combined hyperspectral image restoration and fusion approach},
	booktitle = {IEEE International Geoscience and Remote Sensing Symposium, Cape Town, South Africa, July, 13-17},
	number = {3},
	year = {2009},
	pages = {995-998},
	author = {Y. Zhang and A. Duijster and Paul Scheunders}
}
@article {HufkensCeulemanspscheund2009,
	title = {Ecotones in vegetation ecology: methodology and definitions revisited},
	journal = {Ecological Research},
	volume = {24},
	year = {2009},
	pages = {977-986},
	author = {Koen Hufkens and Reinhart Ceulemans and Paul Scheunders}
}
@inproceedings {Zhangaduijstepscheund2009,
	title = {A Hyperspectral Image Restoration technique},
	booktitle = {IEEE International Conference on Image Processing, Cairo, Egypt, November 7-11},
	year = {2009},
	pages = {2873-2876},
	author = {Y. Zhang and A. Duijster and Paul Scheunders}
}
@article {Zhangsdbackerpscheund2009,
	title = {Noise-resistant wavelet-based Bayesian fusion of multispectral and hyperspectral images},
	journal = {IEEE Transactions on Geoscience and Remote Sensing},
	volume = {47},
	number = {11},
	year = {2009},
	pages = {3834-3843},
	author = {Y. Zhang and Steve De Backer and Paul Scheunders}
}
@inproceedings {Driesen09,
	title = {Spatial hyperspectral image classification by prior segmentation},
	booktitle = {Geoscience and Remote Sensing Symposium,2009 IEEE International,IGARSS 2009},
	volume = {3},
	year = {2009},
	month = {July},
	pages = {III-709 - III-712},
	address = {Cape Town, South Africa},
	abstract = {In this paper, we propose a technique to incorporate spatial features in the classification of hyperspectral data by means of a prior segmentation of the dataset. The key idea of the technique is that each pixel is not classified individually, but that the regions obtained from the prior segmentation are classified as a whole. The proposed technique is validated on a hyperspectral dataset of a heathland area in Belgium. Experimental results show that we can achieve larger and spatially smoothed regions, while the overall classification success rate is comparable to the pure spectral classification results.},
	keywords = {Belgium, geophysical image processing, heathland area, hyperspectral data classification, image classification, image segmentation, prior segmentation, remote sensing, spatial hyperspectral image classification, spatially smoothed regions, spectral classification},
	isbn = {978-1-4244-3394-0},
	doi = {10.1109/IGARSS.2009.5417861},
	author = {J. Driesen and Guy Thoonen and Paul Scheunders}
}
@article {wvdbroekVerbeeckSchryverssdbackerpscheund2009,
	title = {Tomographic Spectroscopic Imaging; an experimental proof of concept},
	journal = {Ultramicroscopy},
	volume = {109},
	year = {2009},
	pages = {296-303},
	author = {Wouter Van den Broek and Jo Verbeeck and D. Schryvers and Steve De Backer and Paul Scheunders}
}
@inproceedings {Gansemanpscheundwdhaes2009,
	title = {Using XML-formatted scores in real-time applications},
	booktitle = {10-th International Society for Music Information Retrieval Conference, pp. 663-668, Kobe, Japan, October 26-30},
	year = {2009},
	pages = {663-668},
	author = {Joachim Ganseman and Paul Scheunders and W. D{\textquoteright}haes}
}
@article {HufkenspscheundCeulemans2009,
	title = {Validation of the sigmoid wave curve fitting algorithm on a forest-tundra ecotone in the Northwest Territories, Canada},
	journal = {Ecological Informatics},
	volume = {4},
	year = {2009},
	pages = {1-7},
	author = {Koen Hufkens and Paul Scheunders and Reinhart Ceulemans}
}
@inproceedings {VerdoolaegeLambrechtspscheund2009,
	title = {Wavelet-Based colour texture retrieval using the KL Divergence between bivariate Generalized Gaussian Models},
	booktitle = {IEEE International Conference on Image Processing, Cairo, Egypt, November 7-11},
	year = {2009},
	pages = {265-268},
	author = {G. Verdoolaege and M. Lambrechts and Paul Scheunders}
}
@article {aduijstepscheundsdbacker2009,
	title = {Wavelet-Based EM Algorithm for Multispectral-Image Restoration},
	journal = {IEEE Transactions on Geoscience and Remote Sensing},
	volume = {47},
	number = {11},
	year = {2009},
	month = {November},
	pages = {3892-3898},
	author = {A. Duijster and Paul Scheunders and Steve De Backer}
}
@proceedings {1238,
	title = {ACIVS 2008, Advanced Concepts for Intelligent Vision Systems},
	volume = {5259},
	year = {2008},
	author = {J Blanc-Talon and S. Bourenanne and Wilfried Philips and D Popescu and Paul Scheunders}
}
@inproceedings {Zhangsdbackerpscheund2008,
	title = {Bayesian fusion of multispectral and hyperspectral image in wavelet domain},
	booktitle = {IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2008, Boston, United States, 6-11 July},
	year = {2008},
	pages = {69-72},
	publisher = {IEEE International},
	organization = {IEEE International},
	author = {Y. Zhang and Steve De Backer and Paul Scheunders},
	editor = {t.b.a.}
}
@article {sdbackerPizuricaHuysmansPhilipspscheund2008,
	title = {Denoising of Multicomponent Images Using Wavelet Least-Squares Estimators},
	journal = {Image and Vision Computing},
	volume = {26},
	number = {7},
	year = {2008},
	month = {July},
	pages = {1038-1051},
	author = {Steve De Backer and Aleksandra Pizurica and B. Huysmans and Wilfried Philips and Paul Scheunders}
}
@article {HufkensCeulemanspscheund2008,
	title = {Estimating the ecotone width in patchy ecotones using a sigmoid wave approach},
	journal = {Ecological Informatics},
	volume = {3},
	year = {2008},
	pages = {97-104},
	author = {Koen Hufkens and Reinhart Ceulemans and Paul Scheunders}
}
@article {KempeneersZarco-TejadaNorthsdbackerDelalieuxSepulcre-CantoMoralesAardtSagardoyCoppinpscheund2008,
	title = {Model inversion for chlorophyll estimation in open canopies from hyperspectral imagery},
	journal = {International Journal of Remote Sensing},
	volume = {29},
	number = {17},
	year = {2008},
	pages = {5093-5111},
	author = {Pieter Kempeneers and P.J. Zarco-Tejada and P.R.J. North and Steve De Backer and Stephanie Delalieux and G. Sepulcre-Canto and F. Morales and Jan van Aardt and Ruth Sagardoy and P. Coppin and Paul Scheunders}
}
@article {sdbackerCornelissenLemeireNuydensMeertSchelkenspscheund2008,
	title = {Mosiacing of Fibered Fluorescence Microscopy Video},
	journal = {Lecture notes in Computer Science},
	volume = {5259},
	year = {2008},
	month = {October},
	pages = {915-923},
	author = {Steve De Backer and F. Cornelissen and J. Lemeire and R. Nuydens and T. Meert and P. Schelkens and Paul Scheunders}
}
@article {jdriesenpscheund2008,
	title = {A Multicomponent Image Segmentation Framework},
	journal = {Lecture Notes in Computer Science},
	volume = {5259},
	year = {2008},
	pages = {589-600},
	author = {J. Driesen and Paul Scheunders}
}
@inproceedings {Verdoolaegesdbackerpscheund2008,
	title = {Multiscale colour texture retrieval using the geodesic distance between multivariate generalized Gaussian models},
	booktitle = {IEEE, International Conference on Image Processing, San Diego, CA, October 12-15},
	year = {2008},
	pages = {169-172},
	author = {G. Verdoolaege and Steve De Backer and Paul Scheunders}
}
@inproceedings {Thoonen08,
	title = {Spatial Classification of Hyperspectral Data of Dune Vegetation along the Belgian Coast},
	booktitle = {Geoscience and Remote Sensing Symposium, 2008. IGARSS 2008. IEEE International},
	volume = {3},
	year = {2008},
	month = {July},
	pages = {III-483 - III-486},
	address = {Boston, MA, USA},
	abstract = {This work evaluates a classification method, including spatial information, for dune vegetation along the Belgian coastline. The used method is a recursive supervised segmentation algorithm based on a tree-structured Markov Random Field. This technique describes a K-ary field as a sequence of binary Markov Random Fields, each of which is represented by a node in the tree. The obtained classification results were compared to results with the same data set, for a purely spectral classification and a spectral classification, followed by spatial smoothing.},
	keywords = {airborne data, airborne radar, Belgian Coast, binary Markov Random Field, dune environment, dune vegetation mapping, Europe, hyperspectral data, image classification, image segmentation, Markov processes, spatial classification, spatial smoothing, supervised segmentation algorithm, tree-structured Markov Random Field, trees (mathematics), TS-MRF model, vegetation mapping},
	isbn = {978-1-4244-2807-6},
	doi = {10.1109/IGARSS.2008.4779389},
	author = {Guy Thoonen and Steve De Backer and S. Provoost and Pieter Kempeneers and Paul Scheunders}
}
@inproceedings {pscheundwdhaes2008,
	title = {Using MusicXML in real-time applications},
	booktitle = {Proceedings of the 34th Euromicro conference on Software Engineering and Advanced Applications, Parma, Italy, September 3-5},
	year = {2008},
	pages = {29-30},
	author = {Joachim Ganseman and Paul Scheunders and W. D{\textquoteright}haes}
}
@inproceedings {Gansemanpscheundwdhaes2008,
	title = {Using XQUERY on MusicXML databases for musicological analysis},
	booktitle = {ISMIR 2008, 9-th International Conference on Music Information Retrieval, Philadephia, USA, 14-18 september},
	year = {2008},
	pages = {433-438},
	author = {Joachim Ganseman and Paul Scheunders and W. D{\textquoteright}haes}
}
@inproceedings {aduijstesdbackerpscheund2008,
	title = {Wavelet-based Multispectral Image Restoration},
	booktitle = {IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium},
	volume = {3},
	year = {2008},
	pages = {79-82},
	publisher = {IEEE International},
	organization = {IEEE International},
	author = {A. Duijster and Steve De Backer and Paul Scheunders}
}
@proceedings {-Blanc-TalonPhilipsPopescupscheund2007,
	title = {ACIVS 2007, Advanced Concepts for Intelligent Vision Systems},
	volume = {4678},
	year = {2007},
	pages = {1-1095},
	publisher = {Springer},
	author = {J Blanc-Talon and Wilfried Philips and D Popescu and Paul Scheunders}
}
@inproceedings {pscheundsdbackerPizuricaHuysmansPhilips2007,
	title = {Bayesian Wavelet-based Denoising of Multicomponent Images},
	booktitle = {Proceedings Wavelet Applications in Industrial Processing V, part of SPIE Optics East, Boston, MA, 9-12 september, Vol. 6763-OK (12 pages)},
	year = {2007},
	author = {Paul Scheunders and Steve De Backer and Aleksandra Pizurica and B. Huysmans and Wilfried Philips}
}
@mastersthesis {1291,
	title = {Data-driven methods for the analysis of time-resolved mental chronometry fMRI data sets},
	year = {2007},
	type = {PhD thesis},
	author = {Alain Smolders}
}
@article {asmolderMartinoStaerenpscheundjsijbersGoebelFormisano2007,
	title = {Dissecting cognitive stages with time-resolved fMRI data: a comparison of fuzzy clustering and independent component analysis},
	journal = {Magnetic Resonance Imaging},
	volume = {25},
	number = {6},
	year = {2007},
	month = {July},
	pages = {860-868},
	doi = {doi:10.1016/j.mri.2007.02.018},
	author = {Alain Smolders and F. De Martino and N. Staeren and Paul Scheunders and Jan Sijbers and R. Goebel and E. Formisano}
}
@article {aduijstesdbackerpscheund2007,
	title = {Multicomponent image restoration, an experimental study},
	journal = {Lecture Notes in Computer Science},
	volume = {4633},
	year = {2007},
	pages = {58-68},
	author = {A. Duijster and Steve De Backer and Paul Scheunders}
}
@article {pscheundsdbacker2007,
	title = {Wavelet denoising of multicomponent images, using Gaussian Scale Mixture models and a noise-free image as priors},
	journal = {IEEE Transactions on Image Processing},
	volume = {16},
	number = {7},
	year = {2007},
	month = {July},
	pages = {1865-1872},
	author = {Paul Scheunders and Steve De Backer}
}
@inproceedings {aduijstesdbackerpscheund2007,
	title = {Wavelet-based Multicomponent Image Restoration},
	booktitle = {Wavelet Applications in Industrial Processing V, part of SPIE Optics East, Boston, MA, United States, September 9-12},
	series = {Presented at the Society of Photo-Optical Instrumentation Engineers (SPIE) Conference},
	volume = {6763},
	year = {2007},
	month = {October},
	author = {A. Duijster and Steve De Backer and Paul Scheunders},
	editor = {F. Truchetet and O. Laligant}
}
@proceedings {-Blanc-TalonPhilipsPopescupscheund2006,
	title = {ACIVS 2006, Advanced Concepts for Intelligent Vision Systems},
	volume = {4179},
	year = {2006},
	pages = {1-1224},
	publisher = {Springer},
	author = {J Blanc-Talon and Wilfried Philips and D Popescu and Paul Scheunders}
}
@article {wvdbroekVerbeecksdbackerpscheundSchryvers2006,
	title = {Acquisition of the EELS data cube by tomographic reconstruction},
	journal = {Ultramicroscopy},
	volume = {106},
	number = {3},
	year = {2006},
	month = {March},
	pages = {269-276},
	author = {Wouter Van den Broek and Jo Verbeeck and Steve De Backer and Paul Scheunders and D. Schryvers}
}
@inproceedings {KempeneerssdbackerZarco-TejadaDelalieuxSepulcre-Cant{\'o}MoralesSagardoyAardtCoppinpscheund2006,
	title = {Chlorophyll retrieval from canopy reflectance over orchards using hyperspectral techniques},
	booktitle = {2nd International Symposium on Recent Advances in Quantitative Remote Sensing, 25-29 September 2006, Torrent (Valencia), Spain},
	year = {2006},
	author = {Pieter Kempeneers and Steve De Backer and P.J. Zarco-Tejada and Stephanie Delalieux and G. Sepulcre-Cant{\'o} and F. Morales and Ruth Sagardoy and Jan van Aardt and P. Coppin and Paul Scheunders}
}
@inproceedings {sdbackerpscheund2006,
	title = {Enhancement of fMRI image series with the aid of an anatomical image},
	booktitle = {ISBI{\textquoteright}06, IEEE International Symposium on Biomedical Imaging, Arlington, Virginia, april 6-9},
	year = {2006},
	pages = {1052-1055},
	author = {Steve De Backer and Paul Scheunders}
}
@mastersthesis {1272,
	title = {Reconstructie van Transversaal Getrunceerde Cone Beam Projecties in Micro-Tomografie},
	year = {2006},
	school = {University of Antwerp},
	type = {PhD thesis},
	address = {Antwerp},
	author = {G. Tisson}
}
@inproceedings {KempeneerssdbackerTejadaDelalieuxCantoIribasAardtCoppinpscheund2006,
	title = {Stress detection in orchards with hyperspectral remote sensing data},
	booktitle = {SPIE Remote Sensing, Stockholm, Sweden, september 11-14},
	volume = {6359},
	number = {38},
	year = {2006},
	month = {September},
	author = {Pieter Kempeneers and Steve De Backer and P J Zarco Tejada and Stephanie Delalieux and G Sepulcre Canto and F Morales Iribas and Jan van Aardt and P. Coppin and Paul Scheunders}
}
@inproceedings {jdriesenpscheund2006,
	title = {Wavelet based segmentation of multi-component images},
	booktitle = {IEEE BENELUX/DSP Valley Signal Processing Symposium (SPS-DARTS) March 28-29, Antwerp, Belgium},
	year = {2006},
	pages = {151-154},
	author = {J. Driesen and Paul Scheunders}
}
@inproceedings {pscheundsdbacker2006,
	title = {Wavelet Denoising of multicomponent images using a Gaussian Scale Mixture model},
	booktitle = {ICPR{\textquoteright}06, International Conference on Pattern Recognition, Hong Kong, august 20-24},
	volume = {3},
	year = {2006},
	pages = {754-757},
	publisher = {IEEE Computer Society},
	organization = {IEEE Computer Society},
	author = {Paul Scheunders and Steve De Backer}
}
@inproceedings {pscheundsdbacker2006,
	title = {Wavelet Denoising of multicomponent images using a noise-free image},
	booktitle = {ICIP{\textquoteright}06, IEEE International Conference on Image Processing, Atlanta, Georgia, October 8-11},
	year = {2006},
	pages = {2619-2622},
	publisher = {IEEE Signal Processing Society},
	organization = {IEEE Signal Processing Society},
	author = {Paul Scheunders and Steve De Backer}
}
@proceedings {-Blanc-TalonPhilipsPopescupscheund2005,
	title = {ACIVS 2005, Advanced Concepts for Intelligent Vision Systems},
	volume = {3708},
	year = {2005},
	pages = {1-725},
	publisher = {Springer},
	author = {J Blanc-Talon and Wilfried Philips and D Popescu and Paul Scheunders}
}
@article {sdbackerKempeneersDebruynpscheund2005,
	title = {A Band Selection Technique for Spectral Classification},
	journal = {IEEE Geoscience and Remote Sensing Letters},
	volume = {2},
	number = {3},
	year = {2005},
	month = {July},
	pages = {319-323},
	author = {Steve De Backer and Pieter Kempeneers and W. Debruyn and Paul Scheunders}
}
@article {KempeneerssdbackerDebruynpscheund2005,
	title = {Generic Wavelet-Based Hyperspectral Classification Applied to Vegetation Stress Detection},
	journal = {IEEE Transactions on Geoscience and Remote Sensing},
	volume = {43},
	year = {2005},
	month = {March},
	pages = {610-614},
	author = {Pieter Kempeneers and Steve De Backer and W. Debruyn and Paul Scheunders}
}
@inproceedings {KempeneerssdbackerDebruynProvoostpscheund2005,
	title = {Hyperspectral classification applied to the Belgian coastline},
	booktitle = {SPIE Remote Sensing, Bruges, Belgium, 19-22 september},
	volume = {5982},
	number = {15},
	year = {2005},
	author = {Pieter Kempeneers and Steve De Backer and W. Debruyn and S. Provoost and Paul Scheunders}
}
@inproceedings {KempeneersSterckxDebruynParkRuddicksdbackerpscheund2005,
	title = {Retrieval of oceanic constituents from ocean color using simulated annealing},
	booktitle = {IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2005, Seoul, Korea, 25-29 July},
	volume = {8},
	year = {2005},
	pages = {5651-5654},
	author = {Pieter Kempeneers and S Sterckx and W. Debruyn and Y. Park and K. Ruddick and Steve De Backer and Paul Scheunders}
}
@article {MeirBoumansGroofAudekerkeasmolderpscheundjsijbersmverhoyeBalthazartavdlinde2005,
	title = {Spatiotemporal properties of the BOLD response in the songbirds auditory circuit during a variety of listening tasks},
	journal = {NeuroImage},
	volume = {25},
	year = {2005},
	pages = {1242-1255},
	author = {V. Van Meir and T. Boumans and G. De Groof and Johan Van Audekerke and Alain Smolders and Paul Scheunders and Jan Sijbers and Marleen Verhoye and J. Balthazart and Annemie Van Der Linden}
}
@inproceedings {jdriesenpscheund2005,
	title = {Wavelet based segmentation of multivalued images},
	booktitle = {SPIE Optics East, 23-26 October, Boston, Massachusetts USA},
	volume = {6001},
	number = {2},
	year = {2005},
	pages = {13-22},
	author = {J. Driesen and Paul Scheunders}
}
@inproceedings {PizuricaHuysmanspscheundPhilips2005,
	title = {Wavelet domain denoising of multispectral remote sensing imagery adapted to the local spatial and spectral context},
	booktitle = {IEEE International Geoscience and Remote Sensing Symp. IGARSS 2005, Seoul, Korea, 25-29 July},
	volume = {6},
	year = {2005},
	pages = {4260-4263},
	address = {Seoul, Korea},
	author = {Aleksandra Pizurica and B. Huysmans and Paul Scheunders and Wilfried Philips}
}
@inproceedings {PizuricaPhilipspscheund2005,
	title = {Wavelet domain denoising of single-band and multi-band images adapted to the probability of the presence of features of interes},
	booktitle = {SPIE Wavelets XI, San Diego, California, USA, 31 July {\textendash} 4 Aug},
	volume = {5914},
	year = {2005},
	pages = {508-521},
	author = {Aleksandra Pizurica and Wilfried Philips and Paul Scheunders}
}
@inproceedings {pscheundsdbacker2005,
	title = {Wavelet-based enhancement of remote sensing and biomedical image series using an auxiliary image},
	booktitle = {SPIE Optics East, 23-26 October, Boston, Massachusetts USA},
	volume = {6001},
	number = {5},
	year = {2005},
	pages = {41-52},
	author = {Paul Scheunders and Steve De Backer}
}
@inproceedings {gtissonpscheunddvandyck2004,
	title = {3D region of interest X-ray CT for geometric magnification from multiresolution acquisitions},
	booktitle = {Proc. ISBI04, IEEE International Symposium on Biomedical Imaging, 15-18 april 2004, Arlington, VA},
	year = {2004},
	pages = {567-570},
	author = {G. Tisson and Paul Scheunders and Dirk Van Dyck}
}
@inproceedings {sdbackerKempeneersDebruynpscheund2004,
	title = {Classification of Dune Vegetation from Remotely Sensed Hyperspectral Images},
	booktitle = {Image Analysis and Recognition Proc. of International Conference on Image Analysis and Recognition},
	series = {Lecture Notes in Computer Science},
	year = {2004},
	pages = {497-503},
	publisher = {Springer},
	organization = {Springer},
	address = {Porto, Portugal},
	author = {Steve De Backer and Pieter Kempeneers and W. Debruyn and Paul Scheunders},
	editor = {A. Campilho and M. Kamel}
}
@inproceedings {KempeneerssdbackerDerondeBertelsDebruynpscheund2004,
	title = {Classifying hyperspectral airborne imagery for vegetation survey along coastlines},
	booktitle = {IEEE International Geoscience and Remote Sensing Symposium, IGARSS, Anchorage, USA, 20-24 september},
	volume = {2},
	year = {2004},
	pages = {1475-1478},
	publisher = {IEEE International},
	organization = {IEEE International},
	address = {Anchorage, USA},
	author = {Pieter Kempeneers and Steve De Backer and B. Deronde and L. Bertels and W. Debruyn and Paul Scheunders}
}
@inproceedings {asmolderpscheundjsijbersMeirmverhoyeavdlinde2004,
	title = {Clustering of hemodynamic response functions from fMRI-data of a starling},
	booktitle = {13th European Microscopy Congress},
	year = {2004},
	month = {January},
	address = {Antwerp, Belgium},
	author = {Alain Smolders and Paul Scheunders and Jan Sijbers and V. Van Meir and Marleen Verhoye and Annemie Van Der Linden}
}
@inproceedings {MeirBoumansGroofAudekerkeasmolderpscheundjsijbersmverhoyeBalthazartavdlinde2004,
	title = {Functional Magnetic resonance Imaging meets animal vocal learner},
	booktitle = {Third One-Day Symposium of Young Belgian Magnetic Resonance Scientists},
	year = {2004},
	month = {November},
	address = {Brussels, Belgium},
	author = {V. Van Meir and T. Boumans and G. De Groof and Johan Van Audekerke and Alain Smolders and Paul Scheunders and Jan Sijbers and Marleen Verhoye and J. Balthazart and Annemie Van Der Linden}
}
@inproceedings {pscheundjdriesen2004,
	title = {Least squares interband denoising of color and multispectral images},
	booktitle = {Proc. ICIP2004, IEEE International Conference on Image Processing, 24-27 october, Singapore},
	year = {2004},
	pages = {985-988},
	author = {Paul Scheunders and J. Driesen}
}
@inproceedings {PizuricapscheundPhilips2004,
	title = {Multiresolution multispectral image denoising based on probability of presence of features of interest},
	booktitle = {Advanced Concepts in Image and Vision Systems, Brussels},
	year = {2004},
	pages = {357-364},
	author = {Aleksandra Pizurica and Paul Scheunders and Wilfried Philips}
}
@inproceedings {gtissonpscheunddvandyck2004,
	title = {ROI Cone-Beam CT on a Circular Orbit for Geometric Magnification Using Reprojection},
	booktitle = {Proc. IEEE Medical Imaging Conference, Rome, 16-22 october},
	year = {2004},
	author = {G. Tisson and Paul Scheunders and Dirk Van Dyck}
}
@inproceedings {KempeneerssdbackerDelalieuxSterckxDebruynCoppinpscheund2004,
	title = {Upscaling of spectroradiometer data for stress detection in orchards with remote sensing},
	booktitle = {SPIE Remote Sensing, Gran Canaria, 13-16 september},
	volume = {127},
	year = {2004},
	pages = {37-45},
	publisher = {SPIE},
	organization = {SPIE},
	address = {Maspalomas, Spain},
	author = {Pieter Kempeneers and Steve De Backer and Stephanie Delalieux and S Sterckx and W. Debruyn and P. Coppin and Paul Scheunders},
	editor = {Manfred Owe, Guido D{\textquoteright}Urso, Ben T. Gouweleeuw, Anne M. Jochum}
}
@article {HungpscheundPhamSuColeman2004,
	title = {Using Intelligent Optimization Techniques in the K-means Algorithm for Multispectral Image Classification},
	journal = {International Journal of Fuzzy Systems},
	volume = {6},
	number = {3},
	year = {2004},
	pages = {107-117},
	author = {C.C. Hung and Paul Scheunders and M. Pham and M.C. Su and T. Coleman}
}
@inproceedings {jdriesenpscheund2004,
	title = {Wavelet based Filter Array Demosaicking},
	booktitle = {Proc. ICIP2004, IEEE International Conference on Image Processing, 24-27 october, Singapore},
	year = {2004},
	pages = {3311-3314},
	author = {J. Driesen and Paul Scheunders}
}
@inproceedings {sdbackerKempeneersDebruynpscheund2004,
	title = {Wavelet Based Hyperspectral Data Analysis for Vegetation Stress Classification},
	booktitle = {Proc. of Advanced Concepts for Intelligent Vision Systems},
	year = {2004},
	pages = {387-391},
	address = {Brussels, Belgium},
	author = {Steve De Backer and Pieter Kempeneers and W. Debruyn and Paul Scheunders}
}
@article {pscheund2004,
	title = {Wavelet thresholding of multivalued images},
	journal = {IEEE Transactions on Image Processing},
	volume = {13},
	number = {4},
	year = {2004},
	pages = {475-483},
	author = {Paul Scheunders}
}
@inproceedings {slivensAnthonisMahypscheund2003,
	title = {Cross-media tonal mapping model obtained from psychometric experiments},
	booktitle = {Proc. SPIE 5008: Color Imaging, Device- Independent Color, Color Hardcopy, and Graphic Arts VII , Santa Clara, CA},
	year = {2003},
	pages = {14-23},
	author = {S. Livens and A. Anthonis and M. Mahy and Paul Scheunders}
}
@inproceedings {pscheund2003,
	title = {Denoising of multispectral images using wavelet thresholding},
	booktitle = {Proc. SPIE conference on Image and Signal Processing for Remote Sensing IX, part of the International Symposium on Remote Sensing},
	year = {2003},
	month = {September},
	pages = {28-35},
	address = {Barcelona, Spain},
	author = {Paul Scheunders}
}
@mastersthesis {1286,
	title = {Feature Selection and Classifier Ensembles: A Study on Hyperspectral Remote Sensing Data},
	year = {2003},
	school = {University of Antwerp},
	type = {PhD thesis},
	address = {Antwerp},
	author = {Shixin Yu}
}
@inproceedings {SterckxCoppinsdbackerDebruynKempeneersMeulemanNackaertsReusenpscheund2003,
	title = {Information extraction techniques for monitoring of stress symptoms in orchards},
	booktitle = {Proc. Earsel 2003, Imaging Spectroscopy workshop Oberpfaffenhofen},
	year = {2003},
	pages = {278-283},
	author = {S Sterckx and P. Coppin and Steve De Backer and W. Debruyn and Pieter Kempeneers and K. Meuleman and K. Nackaerts and I. Reusen and Paul Scheunders}
}
@inproceedings {MeulemanCoppinsdbackerDebruynNackaertspscheundStercks2003,
	title = {Optimal hyperspectral indicators for stress detection in orchards},
	booktitle = {Proc. Earsel 2003, Imaging Spectroscopy workshop Oberpfaffenhofen},
	year = {2003},
	pages = {534-541},
	author = {K. Meuleman and P. Coppin and Steve De Backer and W. Debruyn and K. Nackaerts and Paul Scheunders and S Sterckx}
}
@article {pscheund2003,
	title = {An orthogonal wavelet representation of multivalued images},
	journal = {IEEE Transactions on Image Processing},
	volume = {12},
	number = {6},
	year = {2003},
	pages = {718-725},
	author = {Paul Scheunders}
}
@inproceedings {KempeneerssdbackerDebruynpscheund2003,
	title = {Wavelet Based Feature Extraction for Hyperspectral Vegetation Monitoring},
	booktitle = {SPIE International Symposium on Remote Sensing, Image and Signal Processing for Remote Sensing IX},
	year = {2003},
	pages = {297-305},
	address = {Barcelona, Spain},
	author = {Pieter Kempeneers and Steve De Backer and W. Debruyn and Paul Scheunders},
	editor = {L. Bruzzone}
}
@inbook {pscheund2003,
	title = {A wavelet representation of multispectral images; in: Frontiers of Remote Sensing Information Processing},
	year = {2003},
	pages = {197-224},
	publisher = {World Scientific},
	organization = {World Scientific},
	chapter = {9},
	author = {Paul Scheunders},
	editor = {C.H. Chen}
}
@inproceedings {pscheund2003,
	title = {Wavelet representations of multivalued images},
	booktitle = {Wavelet Applications in Industrial Processing, part of the International Symposium on Photonics Technologies for Robotics, Automation, and Manufacturing , 27-31 October 2003, Providence, RI USA},
	year = {2003},
	pages = {203-215},
	author = {Paul Scheunders}
}
@inproceedings {pscheund2003,
	title = {Wavelet thresholding of multispectral images},
	booktitle = {Proc. ACIVS03, Andvanced Concepts for intelligent vision systems , Ghent},
	year = {2003},
	pages = {255-259},
	author = {Paul Scheunders}
}
@article {Yusdbackerpscheund2002,
	title = {Genetic feature selection combined with composite fuzzy nearest neighbor classifiers for hyperspectral satellite imagery},
	journal = {Pattern Recognition Letters},
	volume = {23},
	number = {1},
	year = {2002},
	pages = {183-190},
	author = {Shixin Yu and Steve De Backer and Paul Scheunders}
}
@inproceedings {pscheundjsijbers2002,
	title = {Multiscale watershed segmentation of multivalued images},
	booktitle = {Proc. ICPR02, International Conference on Pattern Recognition},
	volume = {3},
	year = {2002},
	pages = {855-858},
	address = {Quebec, Canada},
	author = {Paul Scheunders and Jan Sijbers}
}
@article {pscheund2002,
	title = {A multivalued image wavelet representation based on multiscale fundamental forms},
	journal = {IEEE Transactions on Image Processing},
	volume = {11},
	number = {5},
	year = {2002},
	pages = {568-575},
	author = {Paul Scheunders}
}
@inproceedings {pscheund2002,
	title = {An orthogonal wavelet representation of multivalued images},
	booktitle = {Proc. ACIVS02, Andvanced Concepts for intelligent vision systems, Ghent},
	year = {2002},
	author = {Paul Scheunders}
}
@mastersthesis {1278,
	title = {Unsupervised Pattern Recognition - Dimensionality Reduction and Classification},
	year = {2002},
	school = {University of Antwerp},
	type = {PhD thesis},
	address = {Antwerp},
	author = {Steve De Backer}
}
@inproceedings {pscheund2002,
	title = {Wavelet-based enhancement and denoising using multiscale structure tensor},
	booktitle = {Proceedings of the IEEE International Conference on Image Processing},
	number = {3},
	year = {2002},
	pages = {569-572},
	address = {Rochester NY},
	author = {Paul Scheunders}
}
@inproceedings {Yupscheund2001,
	title = {Feature selection for high-dimensional remote sensing data by a maximum entropy principal based optimization},
	booktitle = {Proc. Geoscience and Remote Sensing Symposium},
	year = {2001},
	pages = {3303-3305},
	author = {Shixin Yu and Paul Scheunders}
}
@article {pscheundsdbacker2001,
	title = {Fusion and merging of multispectral images using multiscale fundamental forms},
	journal = {Journal of the Optical Society of America A},
	volume = {18},
	number = {10},
	year = {2001},
	pages = {2468-2477},
	author = {Paul Scheunders and Steve De Backer}
}
@inproceedings {Yupscheund2001,
	title = {Fuzzy markov chains approach to feature selection for high dimensional remote sensing data},
	booktitle = {Geoscience and Remote Sensing Symposium},
	year = {2001},
	pages = {3306-3308},
	author = {Shixin Yu and Paul Scheunders}
}
@article {pscheund2001,
	title = {Local mapping for multispectral image visualisation},
	journal = {Image and Vision Computing},
	volume = {19},
	number = {13},
	year = {2001},
	pages = {971-978},
	author = {Paul Scheunders}
}
@inproceedings {pscheundjsijbers2001,
	title = {Multiscale anisotropic filtering of color images},
	booktitle = {Proceedings of the IEEE International Conference on Image Processing},
	volume = {3},
	year = {2001},
	pages = {170-173},
	address = {Thessaloniki, Greece},
	author = {Paul Scheunders and Jan Sijbers}
}
@inproceedings {pscheund2001,
	title = {Multiscale fundamental forms: a multimodal image wavelet representation},
	booktitle = {Proc. ICIAP, International Conference on Image Analysis and Processing, Palermo, Italy},
	year = {2001},
	pages = {179-184},
	author = {Paul Scheunders}
}
@inproceedings {pscheundsdbacker2001,
	title = {Multispectral image fusion and merging using multiscale fundamental forms},
	booktitle = {Proc. ICIP01, IEEE International Conference on Image Processing},
	year = {2001},
	pages = {902-905},
	address = {Thessaloniki, Greece},
	author = {Paul Scheunders and Steve De Backer}
}
@inproceedings {pscheund2001,
	title = {Multivalued image segmentation based on first fundamental form},
	booktitle = {Proc. ICIAP, International Conference on Image Analysis and Processing, Palermo, Italy},
	year = {2001},
	pages = {185-190},
	author = {Paul Scheunders}
}
@article {sdbackerpscheund2001,
	title = {Texture segmentation by frequency-sensitive elliptical competitive learning},
	journal = {Image and Vision Computing},
	volume = {19},
	number = {9},
	year = {2001},
	pages = {639-648},
	author = {Steve De Backer and Paul Scheunders}
}
@article {gvdwouweWeynpscheundJacobMarckdvandyck2000,
	title = {Automated chromatin-texture based diagnosis of carcinoma nuclei},
	journal = {Journal of Microscopy},
	volume = {197},
	year = {2000},
	pages = {25-35},
	author = {G. Van de Wouwer and Barbara Weyn and Paul Scheunders and W. Jacob and E. Van Marck and Dirk Van Dyck}
}
@inproceedings {bdboeckpscheunddvandyck2000,
	title = {From inductive inference to the fundamental equations of measuerement},
	booktitle = {Proc. First International Conference on Complex Systems},
	year = {2000},
	pages = {115-122},
	publisher = {Perseus Books},
	organization = {Perseus Books},
	author = {Bart De Boeck and Paul Scheunders and Dirk Van Dyck},
	editor = {Y. Bar-Yam}
}
@inproceedings {Yupscheundsdbacker2000,
	title = {Genetic Feature selection combined with composite fuzzy nearest neighbor classifers for high-dimensional remote sensing data},
	booktitle = {Proc. IEEE Intern. Conf. on Systems, Man \& Cybernetics, October 8-11, Nashville,TN},
	year = {2000},
	pages = {1912-1916},
	author = {Shixin Yu and Paul Scheunders and Steve De Backer}
}
@inproceedings {Yusdbackerpscheund2000,
	title = {Genetic Feature Selection Combined with Fuzzy K-NN for Hyperspectral Satellite Imagery},
	booktitle = {Intelligent Techniques and Soft Computing in Nuclear Science and Engineering},
	year = {2000},
	pages = {281-288},
	publisher = {World Scientific},
	organization = {World Scientific},
	author = {Shixin Yu and Steve De Backer and Paul Scheunders},
	editor = {d. Ruan, H.A. Abderrahim, P. D{\textquoteright}Hondt, E. Kerre}
}
@inproceedings {Yusdbackerpscheund2000,
	title = {Genetic feature selection combined with fuzzy kNN for hyperspectral satellite imagery},
	booktitle = {Geoscience and Remote Sensing Symposium, 2000. Proceedings. IGARSS 2000. IEEE 2000 International},
	year = {2000},
	pages = {702-704},
	author = {Shixin Yu and Steve De Backer and Paul Scheunders}
}
@inproceedings {pscheund2000,
	title = {Multiscale Edge Representation Applied to Image Fusion},
	booktitle = {Proc. Wavelet Applications in Signal and Image Processing VIII},
	year = {2000},
	pages = {894-901},
	author = {Paul Scheunders}
}
@inproceedings {pscheund2000,
	title = {Multispectral image fusion using local mapping techniques},
	booktitle = {Proc. ICPR2000, International Conference on Pattern Recognition, Barcelona, Spain, september 3-7},
	year = {2000},
	pages = {311-314},
	author = {Paul Scheunders}
}
@article {WeynTjalamgvdwouweDaelepscheundJacobMarckdvandyck2000,
	title = {Validation of nuclear texture density, morphometry and tissue syntactic structure analysis as prognosticators of cervical carcinoma},
	journal = {Analytical and Quantitative Cytology and Histology},
	volume = {22},
	number = {5},
	year = {2000},
	pages = {373-382},
	author = {Barbara Weyn and W. Tjalam and G. Van de Wouwer and A. Van Daele and Paul Scheunders and W. Jacob and E. Van Marck and Dirk Van Dyck}
}
@article {gvdwouwepscheund2000,
	title = {Wavelet-based texture classification},
	journal = {Recent Research in the Development of Pattern Recognition},
	volume = {1},
	year = {2000},
	pages = {77-87},
	author = {G. Van de Wouwer and Paul Scheunders}
}
@article {sdbackerpscheund1999,
	title = {A competitive elliptical clustering algorithm},
	journal = {Pattern Recognition Letters},
	volume = {20},
	number = {11},
	year = {1999},
	pages = {1141-1147},
	author = {Steve De Backer and Paul Scheunders}
}
@article {WeyngvdwouweSamirDaelepscheundMarckJacob1999,
	title = {Computer-assisted differential diagnosis of malignant mesothelioma based on syntactic structure analysis},
	journal = {Cytometry},
	volume = {35},
	year = {1999},
	pages = {23-29},
	author = {Barbara Weyn and G. Van de Wouwer and G.K. Samir and A. Van Daele and Paul Scheunders and E. Van Marck and W. Jacob}
}
@article {pscheundsdbacker1999,
	title = {High-dimensional clustering using frequency sensitive competitive learning},
	journal = {Pattern Recognition},
	volume = {32},
	number = {2},
	year = {1999},
	pages = {193-202},
	author = {Paul Scheunders and Steve De Backer}
}
@article {gvdwouwepscheunddvandyck1999,
	title = {Statistical texture characterization from discrete wavelet representations},
	journal = {IEEE Transactions on Image Processing},
	volume = {8},
	number = {4},
	year = {1999},
	pages = {592-598},
	author = {G. Van de Wouwer and Paul Scheunders and Dirk Van Dyck}
}
@inproceedings {sdbackerpscheund1999,
	title = {Texture segmentation by frequency-sensitive elliptical competitive learning},
	booktitle = {Proc. ICIAP99, International Conference on Image Analysis and Processing , Venice, Italy, september 27-29},
	year = {1999},
	pages = {64-69},
	author = {Steve De Backer and Paul Scheunders}
}
@article {WeyngvdwouweKoprowskiDaeleDhaenepscheundJacobMarck1999,
	title = {Value of morphometry, texture analysis, densitometry and histometry in the differential diagnosis and prognosis of malignant mesothelioma},
	journal = {Journal of Pathology},
	volume = {4},
	number = {189},
	year = {1999},
	pages = {581-589},
	author = {Barbara Weyn and G. Van de Wouwer and M. Koprowski and A. Van Daele and K. Dhaene and Paul Scheunders and W. Jacob and E. Van Marck}
}
@article {gvdwouwepscheundslivensdvandyck1999,
	title = {Wavelet correlation signatures for color texture characterization},
	journal = {pattern Recognition},
	volume = {32},
	number = {3},
	year = {1999},
	pages = {443-451},
	author = {G. Van de Wouwer and Paul Scheunders and S. Livens and Dirk Van Dyck}
}
@article {WeyngvdwouweDaelepscheunddvandyckMarckJacob1998,
	title = {Automated breast tumour diagnosis and grading based on wavelet chromatin texture description},
	journal = {Cytometry},
	volume = {33},
	year = {1998},
	pages = {32-40},
	author = {Barbara Weyn and G. Van de Wouwer and A. Van Daele and Paul Scheunders and Dirk Van Dyck and E. Van Marck and W. Jacob}
}
@inproceedings {dmeersmpscheunddvandyck1998,
	title = {Classification of microcalcifications using texture-based features},
	booktitle = {Digital Mammography},
	year = {1998},
	pages = {233-236},
	author = {D. Meersman and Paul Scheunders and Dirk Van Dyck},
	editor = {N Karssemeijer and L. Van Erning}
}
@inproceedings {dmeersmpscheunddvandyck1998,
	title = {Detection of microcalcifications using non-linear filtering},
	booktitle = {Proc. EUSIPCO{\textquoteright}98, European Signal Processing Conference},
	year = {1998},
	pages = {2465-2468},
	author = {D. Meersman and Paul Scheunders and Dirk Van Dyck}
}
@article {Wolfpscheund1998,
	title = {Evaluation of the swimming activity of Daphnia magna by image analysis after administration of sublethal Cadmium concentrations},
	journal = {Comparative Biochemistry and Physiology A},
	volume = {120},
	year = {1998},
	pages = {99-105},
	author = {G. Wolf and Paul Scheunders}
}
@inproceedings {HungColemanpscheund1998,
	title = {The genetic algorithm approach and K-means clustering: their role in unsupervised training in image classification},
	booktitle = {Proc. IASTED International Conf. On Computer Graphics and Imaging , Halifax, Canada, june 1-3},
	year = {1998},
	pages = {103-106},
	author = {C.C. Hung and T. Coleman and Paul Scheunders}
}
@mastersthesis {1281,
	title = {Image Analysis for Material Characterisation},
	year = {1998},
	school = {University of Antwerp},
	type = {PhD thesis},
	address = {Antwerp},
	author = {S. Livens}
}
@article {pscheund1998,
	title = {Joint quantization and error-diffusion of color images using competitive learning},
	journal = {Journal of the IEE Proceedings, Vision, Image and Signal Processing},
	volume = {14},
	number = {2},
	year = {1998},
	pages = {137-140},
	author = {Paul Scheunders}
}
@article {jsijbersajdendekpscheunddvandyck1998,
	title = {Maximum Likelihood estimation of Rician distribution parameters},
	journal = {IEEE Transactions on Medical Imaging},
	volume = {17},
	number = {3},
	year = {1998},
	pages = {357-361},
	doi = {10.1109/42.712125},
	author = {Jan Sijbers and Arnold Jan den Dekker and Paul Scheunders and Dirk Van Dyck}
}
@article {sdbackerNaudpscheund1998,
	title = {Non-linear Dimensionality Reduction Techniques for Unsupervised Feature Extraction},
	journal = {Pattern Recognition Letters},
	volume = {19},
	year = {1998},
	pages = {711-720},
	author = {Steve De Backer and A. Naud and Paul Scheunders}
}
@article {pscheundsdbackerNaud1998,
	title = {Non-linear mapping for feature extraction},
	journal = {Lecture Notes in Computer Science},
	volume = {1451},
	year = {1998},
	pages = {823-830},
	author = {Paul Scheunders and Steve De Backer and A. Naud}
}
@article {Baillieuxpscheund1998,
	title = {On-line determination of the velocity of simultaneously moving organisms by image analysis for the detection of sublethal toxicity},
	journal = {Water Research},
	volume = {32},
	number = {4},
	year = {1998},
	pages = {1027-1034},
	author = {M. Baillieux and Paul Scheunders}
}
@inproceedings {irtsangijtsangpscheunddvandyck1998,
	title = {Pattern Recognition using neighborhood coding},
	booktitle = {Proc. CVPRIP{\textquoteright}98, Intern. Workshop on Computer Vision, Pattern Recognition and Image Processing , North Carolina, october 23-28},
	year = {1998},
	pages = {250-253},
	author = {Ing Ren Tsang and Ing Jyh Tsang and Paul Scheunders and Dirk Van Dyck}
}
@inproceedings {gvdwouwepscheunddvandyck1998,
	title = {Rotation-Invariant Texture Characterization using Isotropic Wavelet Frames},
	booktitle = {Proc. ICPR98, International Conference on Pattern Recognition , Brisbane, Australia, 17-20 august},
	year = {1998},
	pages = {814-816},
	author = {G. Van de Wouwer and Paul Scheunders and Dirk Van Dyck}
}
@inproceedings {slivensRoostpscheunddvandyck1998,
	title = {Tabular silver bromide micro-crystal characterisation using optical microscopy and colour-image analysis},
	booktitle = {Proc. ICPS98, International Conference on Imaging Science , Antwerp, Belgium, 7-11/9},
	year = {1998},
	author = {S. Livens and C. Van Roost and Paul Scheunders and Dirk Van Dyck}
}
@inproceedings {HungColemanpscheund1998,
	title = {Using genetic differential competitive learning for unsupervised training in multispectral image classification systems},
	booktitle = {Proceedings IEEE International Conference on Systems, Man, and Cybernetics , San Diego, California, October 11-14},
	year = {1998},
	pages = {4482-4485},
	author = {C.C. Hung and T. Coleman and Paul Scheunders}
}
@article {pscheundslivensgvdwouweVautrotdvandyck1998,
	title = {Wavelet-based texture analysis},
	journal = {Intern. Journal on Computer Science and Information Management},
	volume = {1},
	number = {2},
	year = {1998},
	pages = {22-34},
	author = {Paul Scheunders and S. Livens and G. Van de Wouwer and P. Vautrot and Dirk Van Dyck}
}
@inproceedings {gvdwouweslivenspscheunddvandyck1997,
	title = {Color texture classification by wavelet energy-correlation signatures},
	booktitle = {Proc. ICIAP Intern. Conf. On Computer Analysis and Image Processing , Florence, Italy, 17-19 september},
	year = {1997},
	pages = {327-334},
	author = {G. Van de Wouwer and S. Livens and Paul Scheunders and Dirk Van Dyck}
}
@article {pscheund1997,
	title = {Comparison of clustering algorithms applied to colour image quantization},
	journal = {Pattern Recognition Letters},
	volume = {18},
	number = {11},
	year = {1997},
	pages = {1379-1384},
	author = {Paul Scheunders}
}
@article {ebettenspscheunddvandyckMoensOsta1997,
	title = {Computer Analysis of Two-Dimensional Electrophoresis Gels : A New Segmentation and Modeling Algorithm},
	journal = {Electrophoresis},
	volume = {18},
	year = {1997},
	pages = {792-798},
	doi = {10.1002/elps.1150180523},
	author = {E. Bettens and Paul Scheunders and Dirk Van Dyck and L. Moens and P. Van Osta}
}
@article {pscheund1997,
	title = {A genetic c-means clustering algorithm applied to color image quantization},
	journal = {Pattern Recognition},
	volume = {30},
	number = {6},
	year = {1997},
	pages = {859-866},
	author = {Paul Scheunders}
}
@inproceedings {slivensRoostpscheunddvandyck1997,
	title = {Granulometric segmentation using a gradient convergence map},
	booktitle = {Proc. Scandinavian Conference on Image Analysis , Lappeenranta, Finland, 9-11/6},
	year = {1997},
	pages = {389-396},
	author = {S. Livens and C. Van Roost and Paul Scheunders and Dirk Van Dyck}
}
@inproceedings {mverhoyejsijberspscheunddvandyckReyniersKooyWillemsCrasOostraavdlinde1997,
	title = {In vivo assessment of cerebellum volume in transgenic fragile X knockout mice using MTI microscopy at 7T},
	booktitle = {Proceedings of the European Society for Magnetic Resonance in Medicine and Biology: 14th annual meeting},
	volume = {2},
	number = {2},
	year = {1997},
	month = {September},
	pages = {45-46},
	address = {Brussels, Belgium},
	author = {Marleen Verhoye and Jan Sijbers and Paul Scheunders and Dirk Van Dyck and E. Reyniers and R.F. Kooy and P. J. Willems and P. Cras and B. A. Oostra and Annemie Van Der Linden}
}
@inproceedings {pscheundsdbacker1997,
	title = {Joint quantization and error-diffusion of color images using competitive learning},
	booktitle = {Proc. IEEE Conference on Image Processing , Santa Barbara, 26-29 october},
	year = {1997},
	pages = {811-814},
	author = {Paul Scheunders and Steve De Backer}
}
@inproceedings {jsijberspscheunddvandyckeraman1997,
	title = {Noise quantification prior to image restoration},
	booktitle = {Meeting of the Dutch Society for Pattern Recognition and Image Processing},
	year = {1997},
	month = {May},
	address = {Best, The Netherlands},
	author = {Jan Sijbers and Paul Scheunders and Dirk Van Dyck and Erik Raman}
}
@inproceedings {gvdwouweVautrotpscheundslivensdvandyckBonnet1997,
	title = {Rotation-invariant texture segmentation using continuous wavelets},
	booktitle = {Proc. 2-nd IEEE Symposium on applications of time-frequency and time-scale methods , Coventry, UK, 27-29/9},
	year = {1997},
	author = {G. Van de Wouwer and P. Vautrot and Paul Scheunders and S. Livens and Dirk Van Dyck and N. Bonnet}
}
@inproceedings {jsijbersajdendekpscheunderamandvandyck1997,
	title = {Unbiased signal estimation in magnitude MR images},
	booktitle = {Proceedings of the European Society for Magnetic Resonance in Medicine and Biology},
	volume = {2},
	number = {2},
	year = {1997},
	month = {September},
	pages = {174},
	address = {Brussels, Belgium},
	author = {Jan Sijbers and Arnold Jan den Dekker and Paul Scheunders and Erik Raman and Dirk Van Dyck}
}
@article {gvdwouwepscheunddvandyckWuytsHeyning1997,
	title = {Voice recognition from spectrograms: a wavelet based approach},
	journal = {Fractals},
	volume = {5},
	year = {1997},
	pages = {165-172},
	author = {G. Van de Wouwer and Paul Scheunders and Dirk Van Dyck and Floris L Wuyts and Paul H Van de Heyning}
}
@article {jsijbersmverhoyepscheundavdlindedvandyckeraman1997,
	title = {Watershed based segmentation of 3D MR data for volume quantization},
	journal = {Magnetic Resonance Imaging},
	volume = {15},
	number = {6},
	year = {1997},
	pages = {679-688},
	doi = {10.1016/S0730-725X(97)00033-7},
	author = {Jan Sijbers and Marleen Verhoye and Paul Scheunders and Annemie Van Der Linden and Dirk Van Dyck and Erik Raman}
}
@inproceedings {slivenspscheundgvdwouweVautrotdvandyck1997,
	title = {Wavelets for texture analysis, an overview},
	booktitle = {Proc. IEE International Conference on Image Processing and Applications , Dublin, 14-17 july},
	year = {1997},
	pages = {581-585},
	author = {S. Livens and Paul Scheunders and G. Van de Wouwer and P. Vautrot and Dirk Van Dyck}
}
@inproceedings {ebettenspscheundjsijbersdvandyckMoens1996,
	title = {Automatic segmentation and modeling of two-dimensional electrophoresis gels},
	booktitle = {Proceedings of IEEE International Conference on Image Processing},
	volume = {2},
	year = {1996},
	month = {September},
	pages = {665-668},
	address = {Lausanne, Switserland},
	author = {E. Bettens and Paul Scheunders and Jan Sijbers and Dirk Van Dyck and L. Moens}
}
@inproceedings {dmeersmpscheunddvandyck1996,
	title = {Detection of microcalcifications using neural networks},
	booktitle = {Digital Mammography},
	series = {ICS},
	year = {1996},
	pages = {287-290},
	author = {D. Meersman and Paul Scheunders and Dirk Van Dyck},
	editor = {K. Doi}
}
@inproceedings {pscheund1996,
	title = {A genetic approach towards optimal color image quantization},
	booktitle = {Proc. ICIP{\textquoteright}96, IEEE Internat. Conference on Image Processing , III, Lausanne, september 16-19},
	year = {1996},
	pages = {1031-1034},
	author = {Paul Scheunders}
}
@article {pscheund1996,
	title = {A genetic Lloyd-Max image quantization algorithm},
	journal = {Pattern Recognition Letters},
	volume = {17},
	number = {5},
	year = {1996},
	pages = {547-556},
	author = {Paul Scheunders}
}
@inproceedings {mverhoyeavdlindejsijberspscheunddvandyckReyniersKooyWillemsCrasOostra1996,
	title = {High resolution MRI study of the cerebellum of mice as a function of age, in a mouse model for fragile X mental retardation},
	booktitle = {Proceedings of the European Society for Magnetic Resonance in Medicine and Biology},
	volume = {2},
	number = {2},
	year = {1996},
	month = {September},
	pages = {168},
	address = {Prague, Czech Republic},
	author = {Marleen Verhoye and Annemie Van Der Linden and Jan Sijbers and Paul Scheunders and Dirk Van Dyck and E. Reyniers and R.F. Kooy and P. J. Willems and P. Cras and B. A. Oostra}
}
@inproceedings {pscheundHoveslivens1996,
	title = {On the local optimality of image quantizers},
	booktitle = {Proc. ICPR{\textquoteright}96 IEEE Internat. Conference on Pattern Recognition,, D, pp. 664-668, Vienna, august 25-30},
	year = {1996},
	pages = {664-668},
	author = {Paul Scheunders and H. Van Hove and S. Livens}
}
@article {jsijberspscheundBonnetdvandyckeraman1996,
	title = {Quantification and improvement of the signal-to-noise ratio in a magnetic resonance image acquisition procedure},
	journal = {Magnetic Resonance Imaging},
	volume = {14},
	number = {10},
	year = {1996},
	pages = {1157-1163},
	doi = {10.1016/S0730-725X(96)00219-6},
	author = {Jan Sijbers and Paul Scheunders and N. Bonnet and Dirk Van Dyck and Erik Raman}
}
@inproceedings {jsijbersmverhoyepscheundavdlindeAudekerkedvandyckeraman1996,
	title = {Segmentation Scheme for Volume Quantization from 3D Microscopic MR images},
	booktitle = {Proceedings of the Joint Meeting NVvM \& BVM},
	year = {1996},
	month = {December},
	pages = {168-169},
	address = {Gent},
	author = {Jan Sijbers and Marleen Verhoye and Paul Scheunders and Annemie Van Der Linden and Johan Van Audekerke and Dirk Van Dyck and Erik Raman}
}
@inproceedings {jsijbersavdlindepscheundAudekerkeeramandvandyck1996,
	title = {Semi-automatic mouse cerebellum extraction from 3D magnetic resonance data},
	booktitle = {Proceedings of the International Society for Magnetic Resonance in Medicine},
	volume = {3},
	year = {1996},
	month = {April},
	pages = {1585},
	address = {New York, USA},
	author = {Jan Sijbers and Annemie Van Der Linden and Paul Scheunders and Johan Van Audekerke and Erik Raman and Dirk Van Dyck}
}
@article {slivenspscheundgvdwouwedvandyckSmetsWinkelmansBogaerts1996,
	title = {A texture analysis approach to corrosion image classification},
	journal = {Microscopy, Microanalysis, Microstructures},
	volume = {7},
	year = {1996},
	pages = {1-10},
	author = {S. Livens and Paul Scheunders and G. Van de Wouwer and Dirk Van Dyck and H. Smets and J. Winkelmans and W. Bogaerts}
}
@inproceedings {gvdwouwepscheunddvandyckWuytsHeyning1996,
	title = {Voice classification by wavelet transforms and fuzzy interpreted LVQ networks},
	booktitle = {Proceedings International Symposium on Soft Computing and Intelligent Industrial Automation, Reading, UK, March 26-28},
	year = {1996},
	pages = {171-177},
	author = {G. Van de Wouwer and Paul Scheunders and Dirk Van Dyck and Floris L Wuyts and Paul H Van de Heyning}
}
@inproceedings {jsijbersavdlindepscheundAudekerkedvandyckeraman1996,
	title = {Volume quantization of the mouse cerebellum by semi-automatic 3D segmentation of MR images},
	booktitle = {Proceedings of SPIE Medical Imaging},
	volume = {2710},
	year = {1996},
	month = {February},
	pages = {553-560},
	address = {Newport Beach CA, USA},
	author = {Jan Sijbers and Annemie Van Der Linden and Paul Scheunders and Johan Van Audekerke and Dirk Van Dyck and Erik Raman},
	editor = {H. Loew and Kenneth M. Hanson}
}
@inproceedings {gvdwouwepscheunddvandyckBodtWuytsHeyning1996,
	title = {Wavelet-FILVQ classifier for speech analysis},
	booktitle = {Proc. ICPR{\textquoteright}96 Internat. Conference on Pattern Recognition , D, pp. 214-218, Vienna, august 25-30},
	year = {1996},
	pages = {214-218},
	author = {G. Van de Wouwer and Paul Scheunders and Dirk Van Dyck and M. De Bodt and Floris L Wuyts and Paul H Van de Heyning}
}
@article {slivenspscheundgvdwouwedvandyckSmetsWinkelmansBogaerts1995,
	title = {Classification of corrosion images by wavelet signatures and LVQ networks},
	journal = {Lecture Notes in Computer Science},
	volume = {970},
	year = {1995},
	pages = {538-543},
	author = {S. Livens and Paul Scheunders and G. Van de Wouwer and Dirk Van Dyck and H. Smets and J. Winkelmans and W. Bogaerts}
}
@inproceedings {pscheund1995,
	title = {Genetic optimal quantization of gray-level and color images},
	booktitle = {Proceedings ACCV{\textquoteright}95, Second Asian Conference on Computer Vision, Singapore, 5-8 december},
	number = {2},
	year = {1995},
	pages = {94-98},
	author = {Paul Scheunders}
}
@article {pscheundClerckWille1994,
	title = {Computerized image storage and analysis of contracting cardiac myocytes},
	journal = {Journal of Computer Assisted Microscopy},
	volume = {6},
	number = {2},
	year = {1994},
	pages = {77-83},
	author = {Paul Scheunders and N. De Clerck and J. Wille}
}
@inproceedings {jsijberspscheunddvandyckeraman1994,
	title = {Proceedings of the Royal Microscopical Society},
	booktitle = {Optimization of the SNR in NMR images using image sequences},
	volume = {29},
	number = {4},
	year = {1994},
	month = {September},
	pages = {232},
	address = {London, UK},
	author = {Jan Sijbers and Paul Scheunders and Dirk Van Dyck and Erik Raman}
}
@inproceedings {jsijberspscheunddvandyckeraman1994,
	title = {The use of two NMR realizations for determining and improving of the image SNR},
	booktitle = {Proceedings of the Joint Meeting of the Belgian and Dutch Societies for Electron Microscopy},
	year = {1994},
	month = {May},
	pages = {65},
	address = {Arnhem, Papendal, The Netherlands},
	author = {Jan Sijbers and Paul Scheunders and Dirk Van Dyck and Erik Raman}
}
