{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T08:26:14Z","timestamp":1774513574611,"version":"3.50.1"},"reference-count":78,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2023,2,20]],"date-time":"2023-02-20T00:00:00Z","timestamp":1676851200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The aim of fusing hyperspectral and multispectral images is to overcome the limitation of remote sensing hyperspectral sensors by improving their spatial resolutions. This process, also known as hypersharpening, generates an unobserved high-spatial-resolution hyperspectral image. To this end, several hypersharpening methods have been developed, however most of them do not consider the spectral variability phenomenon; therefore, neglecting this phenomenon may cause errors, which leads to reducing the spatial and spectral quality of the sharpened products. Recently, new approaches have been proposed to tackle this problem, particularly those based on spectral unmixing and using parametric models. Nevertheless, the reported methods need a large number of parameters to address spectral variability, which inevitably yields a higher computation time compared to the standard hypersharpening methods. In this paper, a new hypersharpening method addressing spectral variability by considering the spectra bundles-based method, namely the Automated Extraction of Endmember Bundles (AEEB), and the sparsity-based method called Sparse Unmixing by Variable Splitting and Augmented Lagrangian (SUnSAL), is introduced. This new method called Hyperspectral Super-resolution with Spectra Bundles dealing with Spectral Variability (HSB-SV) was tested on both synthetic and real data. Experimental results showed that HSB-SV provides sharpened products with higher spectral and spatial reconstruction fidelities with a very low computational complexity compared to other methods dealing with spectral variability, which are the main contributions of the designed method.<\/jats:p>","DOI":"10.3390\/s23042341","type":"journal-article","created":{"date-parts":[[2023,2,20]],"date-time":"2023-02-20T05:57:52Z","timestamp":1676872672000},"page":"2341","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Hyperspectral and Multispectral Image Fusion with Automated Extraction of Image-Based Endmember Bundles and Sparsity-Based Unmixing to Deal with Spectral Variability"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3192-0681","authenticated-orcid":false,"given":"Salah Eddine","family":"Brezini","sequence":"first","affiliation":[{"name":"Institut de Recherche en Astrophysique et Plan\u00e9tologie (IRAP), Universit\u00e9 de Toulouse, UPS-CNRS-CNES, 31400 Toulouse, France"},{"name":"Laboratoire Signaux et Images, Universit\u00e9 des Sciences et de la Technologie d\u2019Oran Mohamed Boudiaf, Bir El Djir, Oran 31000, Algeria"}]},{"given":"Yannick","family":"Deville","sequence":"additional","affiliation":[{"name":"Institut de Recherche en Astrophysique et Plan\u00e9tologie (IRAP), Universit\u00e9 de Toulouse, UPS-CNRS-CNES, 31400 Toulouse, France"}]}],"member":"1968","published-online":{"date-parts":[[2023,2,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"354","DOI":"10.1109\/JSTARS.2012.2194696","article-title":"Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches","volume":"5","author":"Plaza","year":"2012","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1109\/JSTARS.2013.2249496","article-title":"The earth observing one (EO-1) satellite mission: Over a decade in space","volume":"6","author":"Middleton","year":"2013","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"160198","DOI":"10.1016\/j.scitotenv.2022.160198","article-title":"Drought impact on cropland use monitored with AVIRIS imagery in Central Valley, California","volume":"859","author":"Pancorbo","year":"2023","journal-title":"Sci. Total Environ."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Loizzo, R., Guarini, R., Longo, F., Scopa, T., Formaro, R., Facchinetti, C., and Varacalli, G. (2018, January 22\u201327). Prisma: The Italian Hyperspectral Mission. Proceedings of the IGARSS 2018\u20142018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain.","DOI":"10.1109\/IGARSS.2018.8518512"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Iwasaki, A., Ohgi, N., Tanii, J., Kawashima, T., and Inada, H. (2011, January 24\u201329). Hyperspectral Imager Suite (HISUI)-Japanese hyper-multi spectral radiometer. Proceedings of the IGARSS 2011\u20142011 IEEE International Geoscience and Remote Sensing Symposium, Vancouver, BC, Canada.","DOI":"10.1109\/IGARSS.2011.6049308"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1016\/j.actaastro.2007.01.033","article-title":"The EnMAP hyperspectral imager\u2014An advanced optical payload for future applications in Earth observation programmes","volume":"61","author":"Stuffler","year":"2007","journal-title":"Acta Astronaut."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Blaaberg, S., L\u00f8ke, T., Baarstad, I., Fridman, A., and Koirala, P.A. (2014, January 22\u201325). Next generation VNIR-SWIR hyperspectral camera system: HySpex ODIN-1024. Proceedings of the SPIE, Amsterdam, The Netherlands.","DOI":"10.1117\/12.2067497"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"850","DOI":"10.1016\/j.rse.2017.09.015","article-title":"Imaging spectrometer stray spectral response: In-flight characterization, correction, and validation","volume":"204","author":"Thompson","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1016\/j.rse.2014.11.014","article-title":"Advanced radiometry measurements and Earth science applications with the Airborne Prism Experiment (APEX)","volume":"158","author":"Schaepman","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1109\/MGRS.2013.2244672","article-title":"Hyperspectral Remote Sensing Data Analysis and Future Challenges","volume":"1","author":"Plaza","year":"2013","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Guillaume, M., Minghelli, A., Deville, Y., Chami, M., Juste, L., Lenot, X., Lafrance, B., Jay, S., Briottet, X., and Serfaty, V. (2020). Mapping Benthic Habitats by Extending Non-Negative Matrix Factorization to Address the Water Column and Seabed Adjacency Effects. Remote Sens., 12.","DOI":"10.3390\/rs12132072"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Deville, Y., Brezini, S.E., Benhalouche, F.Z., Karoui, M.S., Karoui, M., Lenot, X., Lafrance, B., Chami, M., Jay, S., and Minghelli, A. (August, January 28). Hyperspectral Oceanic Remote Sensing with Adjacency Effects: From Spectral-Variability-Based Modeling to Performance of Associated Blind Unmixing Methods. Proceedings of the IGARSS 2019\u20142019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan.","DOI":"10.1109\/IGARSS.2019.8898430"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Benhalouche, F.Z., Benharrats, F., Bouhlala, M.A., and Karoui, M.S. (2022). Spectral Unmixing Based Approach for Measuring Gas Flaring from VIIRS NTL Remote Sensing Data: Case of the Flare FIT-M8-101A-1U, Algeria. Remote Sens., 14.","DOI":"10.3390\/rs14102305"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Benhalouche, F.Z., Karoui, M.S., Benharrats, F., and Bouhlala, M.A. (2022, January 17\u201322). Improving Classical Approach for Flare Parameters Estimation from VIIRS NtL Remote Sensing Data by Linear and Nonlinear Spectral Unmixing Methods. Proceedings of the IGARSS 2022\u20142022 IEEE International Geoscience and Remote Sensing Symposium, Kuala Lumpur, Malaysia.","DOI":"10.1109\/IGARSS46834.2022.9884776"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Karoui, M.S., Benhalouche, F.Z., Deville, Y., Djerriri, K., Briottet, X., Houet, T., Le Bris, A., and Weber, C. (2019). Partial Linear NMF-Based Unmixing Methods for Detection and Area Estimation of Photovoltaic Panels in Urban Hyperspectral Remote Sensing Data. Remote Sens., 11.","DOI":"10.3390\/rs11182164"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Benhalouche, F.Z., Benabbou, O., Karoui, M.S., Kebir, L.W., Bennia, A., and Deville, Y. (2022, January 17\u201322). Minerals Detection and Mapping in the Southwestern Algeria Gara-Djebilet Region with a Multistage Informed NMF-Based Unmixing Approach Using Prisma Remote Sensing Hyperspectral Data. Proceedings of the IGARSS 2022\u20142022 IEEE International Geoscience and Remote Sensing Symposium, Kuala Lumpur, Malaysia.","DOI":"10.1109\/IGARSS46834.2022.9884746"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"10438","DOI":"10.1109\/TGRS.2020.3046038","article-title":"Hyperspectral Image Denoising Based on Global and Nonlocal Low-Rank Factorizations","volume":"59","author":"Zhuang","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Akhtar, N., Shafait, F., and Mian, A. (2014, January 6\u201312). Sparse spatio-spectral representation for hyperspectral image super-resolution. Proceedings of the European conference on computer vision, Zurich, Switzerland.","DOI":"10.1109\/CVPR.2015.7298986"},{"key":"ref_19","first-page":"1","article-title":"Sparsity Constrained Fusion of Hyperspectral and Multispectral Images","volume":"19","author":"Fu","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"3008","DOI":"10.1109\/JSTARS.2015.2440092","article-title":"Hyper-Sharpening: A First Approach on SIM-GA Data","volume":"8","author":"Selva","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Constans, Y., Fabre, S., Seymour, M., Crombez, V., Deville, Y., and Briottet, X. (2022). Hyperspectral Pansharpening in the Reflective Domain with a Second Panchromatic Channel in the SWIR II Spectral Domain. Remote Sens., 14.","DOI":"10.3390\/rs14010113"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1174","DOI":"10.1109\/TIP.2004.829779","article-title":"MAP estimation for hyperspectral image resolution enhancement using an auxiliary sensor","volume":"13","author":"Hardie","year":"2004","journal-title":"IEEE Trans. Image Process."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Wei, Q., Dobigeon, N., and Tourneret, J.Y. (2014, January 4\u20139). Bayesian fusion of hyperspectral and multispectral images. Proceedings of the 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Florence, Italy.","DOI":"10.1109\/ICASSP.2014.6854186"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"3658","DOI":"10.1109\/TGRS.2014.2381272","article-title":"Hyperspectral and Multispectral Image Fusion Based on a Sparse Representation","volume":"53","author":"Wei","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"987","DOI":"10.1109\/LGRS.2018.2884087","article-title":"Improving Hypersharpening for WorldView-3 Data","volume":"16","author":"Selva","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"6503","DOI":"10.1109\/TSP.2018.2876362","article-title":"Hyperspectral Super-Resolution: A Coupled Tensor Factorization Approach","volume":"66","author":"Kanatsoulis","year":"2018","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"4118","DOI":"10.1109\/TIP.2018.2836307","article-title":"Fusing Hyperspectral and Multispectral Images via Coupled Sparse Tensor Factorization","volume":"27","author":"Li","year":"2018","journal-title":"IEEE Trans. Image Process."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"702","DOI":"10.1109\/JSTSP.2021.3054338","article-title":"Coupled Tensor Decomposition for Hyperspectral and Multispectral Image Fusion With Inter-Image Variability","volume":"15","author":"Borsoi","year":"2021","journal-title":"IEEE J. Sel. Top. Signal Process."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"931","DOI":"10.1109\/TSP.2020.2965305","article-title":"Hyperspectral Super-Resolution with Coupled Tucker Approximation: Recoverability and SVD-Based Algorithms","volume":"68","author":"Usevich","year":"2020","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1137\/21M1409354","article-title":"Hyperspectral super-resolution accounting for spectral variability: Coupled tensor LL1-based recovery and blind unmixing of the unknown super-resolution image","volume":"15","author":"Borsoi","year":"2022","journal-title":"SIAM J. Imaging Sci."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"5953","DOI":"10.1109\/TGRS.2020.3018732","article-title":"SSR-NET: Spatial\u2013spectral reconstruction network for hyperspectral and multispectral image fusion","volume":"59","author":"Zhang","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"4618","DOI":"10.1109\/TGRS.2020.2964777","article-title":"HAM-MFN: Hyperspectral and multispectral image multiscale fusion network with RAP loss","volume":"58","author":"Xu","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1016\/j.inffus.2021.06.008","article-title":"Image fusion meets deep learning: A survey and perspective","volume":"76","author":"Zhang","year":"2021","journal-title":"Inf. Fusion"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1457","DOI":"10.1109\/TPAMI.2020.3015691","article-title":"MHF-Net: An interpretable deep network for multispectral and hyperspectral image fusion","volume":"44","author":"Xie","year":"2020","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Xiong, Y., Guo, S., Chen, J., Deng, X., Sun, L., Zheng, X., and Xu, W. (2020). Improved SRGAN for Remote Sensing Image Super-Resolution Across Locations and Sensors. Remote Sens., 12.","DOI":"10.3390\/rs12081263"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"107646","DOI":"10.1016\/j.patcog.2020.107646","article-title":"Tackling mode collapse in multi-generator GANs with orthogonal vectors","volume":"110","author":"Li","year":"2021","journal-title":"Pattern Recognit."},{"key":"ref_37","first-page":"1","article-title":"latent encoder coupled generative adversarial network (le-gan) for efficient hyperspectral image super-resolution","volume":"60","author":"Shi","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"528","DOI":"10.1109\/TGRS.2011.2161320","article-title":"Coupled nonnegative matrix factorization unmixing for hyperspectral and multispectral data fusion","volume":"50","author":"Yokoya","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Karoui, M.S., Deville, Y., and Kreri, S. (2013, January 26\u201328). Joint nonnegative matrix factorization for hyperspectral and multispectral remote sensing data fusion. Proceedings of the 2013 5th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), Gainesville, FL, USA.","DOI":"10.1109\/WHISPERS.2013.8080718"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1660","DOI":"10.1109\/TGRS.2016.2628889","article-title":"Hypersharpening by joint-criterion nonnegative matrix factorization","volume":"55","author":"Karoui","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"3373","DOI":"10.1109\/TGRS.2014.2375320","article-title":"A convex formulation for hyperspectral image superresolution via subspace-based regularization","volume":"53","author":"Simoes","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_42","unstructured":"Comon, P., and Jutten, C. (2010). Handbook of Blind Source Separation: Independent Component Analysis and Applications, Academic Press."},{"key":"ref_43","unstructured":"Webster, J. (2016). Wiley Encyclopedia of Electrical and Electronics Engineering, Wiley."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Cichocki, A., Zdunek, R., Phan, A.H., and Amari, S.I. (2009). Nonnegative Matrix and Tensor Factorizations: Applications to Exploratory Multi-way Data Analysis and Blind Source Separation, John Wiley and Sons.","DOI":"10.1002\/9780470747278"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1603","DOI":"10.1016\/j.rse.2011.03.003","article-title":"Endmember variability in spectral mixture analysis: A review","volume":"115","author":"Somers","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1109\/MSP.2013.2279177","article-title":"Endmember variability in hyperspectral analysis: Addressing spectral variability during spectral unmixing","volume":"31","author":"Zare","year":"2013","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1109\/MGRS.2021.3071158","article-title":"Spectral variability in hyperspectral data unmixing: A comprehensive review","volume":"9","author":"Borsoi","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Brezini, S.E., Karoui, M.S., Benhalouche, F.Z., Deville, Y., and Ouamri, A. (2019, January 9\u201312). A pixel-by-pixel NMF-based method for hyperspectral unmixing using a new linear mixing model to address additively-tuned spectral variability. Proceedings of the Image and Signal Processing for Remote Sensing SPIE, Strasbourg, France.","DOI":"10.1117\/12.2533159"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Brezini, S.E., Karoui, M.S., Benhalouche, F.Z., Deville, Y., and Ouamri, A. (2020, January 9\u201311). An NMF-Based Method For Hyperspectral Unmixing Using A Structured Additively-Tuned Linear Mixing Model To Address Spectral Variability. Proceedings of the 2020 Mediterranean and Middle-East Geoscience and Remote Sensing Symposium (M2GARSS), Tunis, Tunisia.","DOI":"10.1109\/M2GARSS47143.2020.9105265"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Brezini, S.E., Deville, Y., Karoui, M.S., Benhalouche, F.Z., and Ouamri, A. (2021, January 11\u201316). A Penalization-Based NMF Approach for Hyperspectral Unmixing Addressing Spectral Variability with an Additively-Tuned Mixing Model. Proceedings of the 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, Brussels, Belgium.","DOI":"10.1109\/IGARSS47720.2021.9553366"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Karoui, M.S., Benhalouche, F.Z., and Deville, Y. (2022, January 17\u201322). A Gradient-Based Method for the Modified Augmented Linear Mixing Model Addressing Spectral Variability for Hyperspectral Unmixing. Proceedings of the IGARSS 2022\u20142022 IEEE International Geoscience and Remote Sensing Symposium, Kuala Lumpur, Malaysia.","DOI":"10.1109\/IGARSS46834.2022.9883849"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Karoui, M.S., Benhalouche, F.Z., and Deville, Y. (2022, January 7\u20139). Hyperspectral Unmixing with a Modified Augmented Linear Mixing Model Addressing Spectral Variability. Proceedings of the 2022 IEEE Mediterranean and Middle-East Geoscience and Remote Sensing Symposium (M2GARSS), Istanbul, Turkey.","DOI":"10.1109\/M2GARSS52314.2022.9839710"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"1923","DOI":"10.1109\/TIP.2018.2878958","article-title":"An augmented linear mixing model to address spectral variability for hyperspectral unmixing","volume":"28","author":"Hong","year":"2018","journal-title":"IEEE Trans. Image Process."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1109\/MSP.2013.2279274","article-title":"Nonlinear unmixing of hyperspectral images: Models and algorithms","volume":"31","author":"Dobigeon","year":"2013","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"1822","DOI":"10.1109\/TSP.2014.2306181","article-title":"Linear-quadratic blind source separation using NMF to unmix urban hyperspectral images","volume":"62","author":"Meganem","year":"2014","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"544","DOI":"10.1109\/TGRS.2013.2242475","article-title":"Linear\u2013quadratic mixing model for reflectances in urban environments","volume":"52","author":"Meganem","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Benhalouche, F.Z., Deville, Y., Karoui, M.S., and Ouamri, A. (2021). Hyperspectral Unmixing Based on Constrained Bilinear or Linear-Quadratic Matrix Factorization. Remote Sens., 13.","DOI":"10.3390\/rs13112132"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1109\/TIP.2019.2928895","article-title":"Super-resolution for hyperspectral and multispectral image fusion accounting for seasonal spectral variability","volume":"29","author":"Borsoi","year":"2019","journal-title":"IEEE Trans. Image Process."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/LGRS.2021.3072405","article-title":"Hypersharpening by an NMF-Unmixing-Based Method Addressing Spectral Variability","volume":"19","author":"Brezini","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"1577","DOI":"10.1080\/01431161.2022.2041762","article-title":"Hyperspectral and multispectral image fusion addressing spectral variability by an augmented linear mixing model","volume":"43","author":"Camacho","year":"2022","journal-title":"Int. J. Remote Sens."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"396","DOI":"10.1109\/JSTARS.2011.2181340","article-title":"Automated extraction of image-based endmember bundles for improved spectral unmixing","volume":"5","author":"Somers","year":"2012","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"2607","DOI":"10.1109\/JSTARS.2014.2373491","article-title":"An image-based endmember bundle extraction algorithm using both spatial and spectral information","volume":"8","author":"Xu","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Bioucas-Dias, J.M., and Figueiredo, M.A. (2010, January 14\u201316). Alternating direction algorithms for constrained sparse regression: Application to hyperspectral unmixing. Proceedings of the 2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, Reykjavik, Iceland.","DOI":"10.1109\/WHISPERS.2010.5594963"},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Imbiriba, T., Borsoi, R.A., and Bermudez, J.C.M. (2018, January 15\u201320). Generalized linear mixing model accounting for endmember variability. Proceedings of the 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Calgary, AB, Canada.","DOI":"10.1109\/ICASSP.2018.8462214"},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Revel, C., Deville, Y., Achard, V., Briottet, X., and Weber, C. (2018). Inertia-constrained pixel-by-pixel nonnegative matrix factorisation: A hyperspectral unmixing method dealing with intra-class variability. Remote Sens., 10.","DOI":"10.3390\/rs10111706"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"529","DOI":"10.1109\/36.911111","article-title":"Fully constrained least squares linear spectral mixture analysis method for material quantification in hyperspectral imagery","volume":"39","author":"Heinz","year":"2001","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Karoui, M.S., Benhalouche, F.Z., Brezini, S.E., Deville, Y., and Benkouider, Y.K. (2021, January 11\u201316). Hypersharpening by a Multiplicative Joint-Criterion NMF Method Addressing Spectral Variability. Proceedings of the 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, Brussels, Belgium.","DOI":"10.1109\/IGARSS47720.2021.9553972"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"3890","DOI":"10.1109\/TIP.2016.2579259","article-title":"Blind hyperspectral unmixing using an extended linear mixing model to address spectral variability","volume":"25","author":"Drumetz","year":"2016","journal-title":"IEEE Trans. Image Process."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"2376","DOI":"10.1109\/TGRS.2005.856106","article-title":"Introduction of sensor spectral response into image fusion methods. Application to wavelet-based methods","volume":"43","author":"Otazu","year":"2005","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"3435","DOI":"10.1109\/TIP.2019.2897254","article-title":"Hyperspectral image unmixing with endmember bundles and group sparsity inducing mixed norms","volume":"28","author":"Drumetz","year":"2019","journal-title":"IEEE Trans. Image Process."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"3980","DOI":"10.1109\/TGRS.2018.2889256","article-title":"Hyperspectral unmixing with spectral variability using adaptive bundles and double sparsity","volume":"57","author":"Uezato","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"898","DOI":"10.1109\/TGRS.2005.844293","article-title":"Vertex component analysis: A fast algorithm to unmix hyperspectral data","volume":"43","author":"Nascimento","year":"2005","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"2405","DOI":"10.1109\/JSTARS.2014.2305441","article-title":"Hyperspectral and LiDAR data fusion: Outcome of the 2013 GRSS data fusion contest","volume":"7","author":"Debes","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_74","first-page":"691","article-title":"Fusion of satellite images of different spatial resolutions: Assessing the quality of resulting images","volume":"63","author":"Wald","year":"1997","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_75","unstructured":"Correa, Y.T.S., Bovolo, F., and Bruzzone, L. (2014, January 22\u201325). Change detection in very high resolution multisensor images. Proceedings of the Image and Signal Processing for Remote Sensing XX SPIE, Amsterdam, The Netherlands."},{"key":"ref_76","unstructured":"(2023, January 12). EO-1 (Earth Observing-1). Available online: https:\/\/earth.esa.int\/web\/eoportal\/satellite-missions\/e\/eo-1."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1109\/MGRS.2016.2637824","article-title":"Hyperspectral and multispectral data fusion: A comparative review of the recent literature","volume":"5","author":"Yokoya","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"193","DOI":"10.14358\/PERS.74.2.193","article-title":"Multispectral and panchromatic data fusion assessment without reference","volume":"74","author":"Alparone","year":"2008","journal-title":"Photogramm. Eng. Remote Sens."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/4\/2341\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:37:12Z","timestamp":1760121432000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/4\/2341"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2,20]]},"references-count":78,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2023,2]]}},"alternative-id":["s23042341"],"URL":"https:\/\/doi.org\/10.3390\/s23042341","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,2,20]]}}}