@article {2479,
	title = {MPGU-Net: Multi-Model Physics-Guided Network for Nonlinear Hyperspectral Unmixing},
	journal = {IEEE Transactions on Geoscience and Remote Sensing},
	year = {2026},
	pages = {1-1},
	keywords = {Estimation, Grounding, linearizing space transformation, Materials, Modeling, Multi-model physics-guided unmixing network, multilinear mixing model, Noise, Pixel, post-nonlinear mixing model, Ray tracing, Roads, Trees (botanical), Vegetation},
	doi = {10.1109/TGRS.2026.3705097},
	author = {Tao, Xuanwen and Koirala, Bikram and Rasti, Behnood and Plaza, Antonio and Scheunders, Paul}
}
@inproceedings {2477,
	title = {A Benchmark Linear Unmixing Dataset With Spectral Variability and Ground Truth},
	booktitle = {2025 15th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS)},
	year = {2025},
	pages = {1-5},
	keywords = {Communication systems, Computer networks, Digital images, endmember extraction, Ground truth, HTTP, Hyperspectral unmixing, IP networks, Network architecture, Pixel, Protocols, Radio access networks, spectral variability, Telecommunications},
	doi = {10.1109/WHISPERS69515.2025.11501633},
	author = {Haijen, Xander and Koirala, Bikram and Tao, Xuanwen and Scheunders, Paul}
}
@inproceedings {2473,
	title = {Estimation of Mortar Brick Composition from Spectral Reflectance},
	booktitle = {2025 15th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS)},
	year = {2025},
	pages = {1-5},
	keywords = {Artificial intelligence, Artificial neural networks, Construction materials, Convolutional neural networks, Cultural heritage conservation, Digital images, HTTP, Hyperspectral imaging, Hypertext systems, Mortar characterization, Pixel, Protocols, Self-organizing feature maps, spectral unmixing, Wireless Access in Vehicular Environments},
	doi = {10.1109/WHISPERS69515.2025.11501610},
	author = {Bnoulkacem, Zakaria and Koirala, Bikram and Soubrier, Philippe and Hu, Xueqing and Vlaminck, Michiel and Luong, Hiep and Van Den Bossche, Nathan and Scheunders, Paul}
}
@inproceedings {2474,
	title = {Leaf Water Thickness Estimation from Spectral Reflectance},
	booktitle = {2025 15th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS)},
	year = {2025},
	pages = {1-5},
	keywords = {Artificial intelligence, Computer networks, HTTP, Hypertext systems, Leaf water thickness, Learning (artificial intelligence), Learning systems, machine learning, polynomial regression, PROSPECT, Protocols, Storage area networks, XML},
	doi = {10.1109/WHISPERS69515.2025.11501631},
	author = {Jambhali, Ketaki Vinay and Koirala, Bikram and Scheunders, Paul}
}
@inproceedings {2475,
	title = {Moisture Content Estimation of Heterogenous Porous Stones Using Hyperspectral Imaging},
	booktitle = {2025 15th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS)},
	year = {2025},
	pages = {1-5},
	keywords = {Artificial intelligence, Broadcasting, Digital images, HTTP, Hypertext systems, machine learning, Moisture content, Motion pictures, Petrographic Analysis, Pixel, Protocols, Sub-pixel Moisture Heterogeneity, SWIR Hyperspectral Imaging, Wireless Access in Vehicular Environments},
	doi = {10.1109/WHISPERS69515.2025.11501647},
	author = {Chaghdo, Danish Ali and Koirala, Bikram and Fontaine, Laurent and Kock, Tim De and Hayen, Roald and Scheunders, Paul}
}
@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}
}
@inproceedings {2476,
	title = {A Two-Step Linear Mixing Model for Unmixing Under Hyperspectral Variability},
	booktitle = {IGARSS 2025 - 2025 IEEE International Geoscience and Remote Sensing Symposium},
	year = {2025},
	pages = {8707-8711},
	keywords = {extended linear mixing model, Geoscience and remote sensing, hyperspectral image processing, Hyperspectral imaging, Hyperspectral unmixing, Image processing, MATLAB, Mixture models, Optimization, scaled linear mixing model, spectral variability, two-step linear mixing model},
	doi = {10.1109/IGARSS55030.2025.11243310},
	author = {Haijen, Xander and Koirala, Bikram and Tao, Xuanwen and Scheunders, Paul}
}
@article {2370,
	title = {Determination of volumetric abundance of intimate mixture using Bayesian MCMC},
	journal = {IEEE Sensors Journal},
	year = {2024},
	pages = {1-1},
	keywords = {Grain size, Hyperspectral, intimate mixture, Iron, Minerals, Powders, radiative transfer, Reflectivity, Sensors, Uncertainty},
	doi = {10.1109/JSEN.2024.3463401},
	author = {Schmidt, Fr{\'e}d{\'e}ric and Koirala, Bikram and Andrieu, Fran{\c c}ois}
}
@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 {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}
}
@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}
}
@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 {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}
}
