@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}
}
@inproceedings {2315,
	title = {Blind Nonlinear Unmixing For Intimate Mixtures Using Hapke Model And CNN},
	booktitle = {Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS)},
	year = {2022},
	month = {22 November 2022},
	pages = {1-5},
	abstract = {We propose a blind nonlinear unmixing technique for intimate mixtures. We use the Hapke model and a fully convolutional neural networks (HapkeCNN). The proposed loss function contains 1) A quadratic term based on the Hapke model, 2) reconstruction error, and 3) a minimum volume term. The first term captures the nonlinearity, the second ensures the fidelity of the reconstructed reflectance, and the latter term exploits the geometrical information. The proposed method is evaluated using a simulated and a real datasets. We compare the results of endmember and abundance estimation with bilinear, multilinear, nonlinear, and projection-based linear unmixing techniques. The experimental results confirm that HapkeCNN considerably outperforms the state-of-the-art nonlinear approaches in terms of spectral angle distance and root mean square error. HapkeCNN was implemented in Python (3.9) using PyTorch as the platform for the deep network and is available online: https://github.com/BehnoodRasti/HapkeCNN.},
	doi = {10.1109/WHISPERS56178.2022.9955081},
	author = {Behnood Rasti and Bikram Koirala}
}
@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 {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 {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}
}
@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 {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}
}
@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 {2308,
	title = {SUnCNN: Sparse Unmixing Using Unsupervised Convolutional Neural Network},
	journal = {IEEE Geoscience and Remote Sensing Letters},
	volume = {19},
	year = {2021},
	month = {06 August 2021},
	pages = {1-5},
	abstract = {In this letter, we propose a sparse unmixing technique using a convolutional neural network (SUnCNN) for hyperspectral images. SUnCNN is the first deep learning-based technique proposed for sparse unmixing. It uses a deep convolutional encoder{\textendash}decoder to generate the abundances relying on a spectral library. We reformulate the sparse unmixing into an optimization over the deep network{\textquoteright}s parameters. Therefore, the deep network learns in an unsupervised manner to map a fixed input into the sparse optimum abundances. Additionally, SUnCNN holds the sum-to-one constraint using a softmax activation layer. The proposed method is compared with the state-of-the-art using two synthetic datasets and one real hyperspectral dataset. The overall results confirm that the proposed method outperforms the other ones in terms of signal to reconstruction error (SRE). Additionally, SUnCNN shows visual superiority for both real and synthetic datasets compared with the competing techniques. 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/SUnCNN .},
	doi = {10.1109/LGRS.2021.3100992},
	author = {Behnood Rasti and Bikram Koirala}
}
@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}
}
