{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T15:46:37Z","timestamp":1775144797391,"version":"3.50.1"},"reference-count":68,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2023,10,19]],"date-time":"2023-10-19T00:00:00Z","timestamp":1697673600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62001098"],"award-info":[{"award-number":["62001098"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["23ZR1402400"],"award-info":[{"award-number":["23ZR1402400"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Natural Science Foundation of Shanghai","award":["62001098"],"award-info":[{"award-number":["62001098"]}]},{"name":"Natural Science Foundation of Shanghai","award":["23ZR1402400"],"award-info":[{"award-number":["23ZR1402400"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>In hyperspectral unmixing, dealing with nonlinear mixing effects and spectral variability (SV) is a significant challenge. Traditional linear unmixing can be seriously deteriorated by the coupled residuals of nonlinearity and SV in remote sensing scenarios. For the simplification of calculation, current unmixing studies usually separate the consideration of nonlinearity and SV. As a result, errors individually caused by the nonlinearity or SV still persist, potentially leading to overfitting and the decreased accuracy of estimated endmembers and abundances. In this paper, a novel unsupervised nonlinear unmixing method accounting for SV is proposed. First, an improved Fisher transformation scheme is constructed by combining an abundance-driven dynamic classification strategy with superpixel segmentation. It can enlarge the differences between different types of pixels and reduce the differences between pixels corresponding to the same class, thereby reducing the influence of SV. Besides, spectral similarity can be well maintained in local homogeneous regions. Second, the polynomial postnonlinear model is employed to represent observed pixels and explain nonlinear components. Regularized by a Fisher transformation operator and abundances\u2019 spatial smoothness, data reconstruction errors in the original spectral space and the transformed space are weighed to derive the unmixing problem. Finally, this problem is solved by a dimensional division-based particle swarm optimization algorithm to produce accurate unmixing results. Extensive experiments on synthetic and real hyperspectral remote sensing data demonstrate the superiority of the proposed method in comparison with state-of-the-art approaches.<\/jats:p>","DOI":"10.3390\/rs15205028","type":"journal-article","created":{"date-parts":[[2023,10,19]],"date-time":"2023-10-19T11:46:26Z","timestamp":1697715986000},"page":"5028","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Unsupervised Nonlinear Hyperspectral Unmixing with Reduced Spectral Variability via Superpixel-Based Fisher Transformation"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-2912-7427","authenticated-orcid":false,"given":"Zhangqiang","family":"Yin","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Donghua University, Shanghai 201620, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9762-0788","authenticated-orcid":false,"given":"Bin","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Donghua University, Shanghai 201620, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,10,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"5511114","DOI":"10.1109\/TGRS.2021.3081136","article-title":"Unaligned hyperspectral image fusion via registration and interpolation modeling","volume":"60","author":"Ying","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"7170","DOI":"10.1109\/TIP.2021.3101916","article-title":"Fast hyperspectral image recovery of dual-camera compressive hyperspectral imaging via non-iterative subspace-based fusion","volume":"30","author":"He","year":"2021","journal-title":"IEEE Trans. Image Process."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"5754","DOI":"10.1109\/TIP.2021.3078058","article-title":"Model-guided deep hyperspectral image super-resolution","volume":"30","author":"Dong","year":"2021","journal-title":"IEEE Trans. Image Process."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1762","DOI":"10.1109\/LGRS.2019.2953525","article-title":"Progressive band selection processing of hyperspectral image classification","volume":"17","author":"Song","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"6044","DOI":"10.1109\/TGRS.2020.3010826","article-title":"Target-constrained interference-minimized band selection for hyperspectral target detection","volume":"59","author":"Shang","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1109\/79.974727","article-title":"Spectral unmixing","volume":"19","author":"Keshava","year":"2002","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_7","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 Observ. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"6245","DOI":"10.1109\/TGRS.2018.2834567","article-title":"Hyperspectral unmixing using sparsity-constrained deep nonnegative matrix factorization with total variation","volume":"56","author":"Feng","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"6391","DOI":"10.1109\/TGRS.2020.2976799","article-title":"Graph-based blind hyperspectral unmixing via nonnegative matrix factorization","volume":"58","author":"Rathnayake","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2391","DOI":"10.1109\/TGRS.2020.3006109","article-title":"Spectral\u2013spatial joint sparse NMF for hyperspectral unmixing","volume":"59","author":"Dong","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_11","first-page":"5505713","article-title":"Spectral-spatial hyperspectral unmixing using nonnegative matrix factorization","volume":"60","author":"Zhang","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1195","DOI":"10.1109\/LGRS.2013.2288921","article-title":"Nonlinear spectral unmixing with a linear mixture of intimate mixtures model","volume":"11","author":"Heylen","year":"2014","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"4153","DOI":"10.1109\/TGRS.2010.2098414","article-title":"Nonlinear unmixing of hyperspectral images using a generalized bilinear model","volume":"49","author":"Halimi","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"3017","DOI":"10.1109\/TIP.2012.2187668","article-title":"Supervised nonlinear spectral unmixing using a postnonlinear mixing model for hyperspectral imagery","volume":"21","author":"Altmann","year":"2012","journal-title":"IEEE Trans. Image Process."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1156","DOI":"10.1109\/JSTSP.2015.2416693","article-title":"A novel approach for efficient p-linear hyperspectral unmixing","volume":"9","author":"Marinoni","year":"2015","journal-title":"IEEE J. Sel. Topics Signal Process."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"240","DOI":"10.1109\/TGRS.2015.2453915","article-title":"A multilinear mixing model for nonlinear spectral unmixing","volume":"54","author":"Heylen","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"6747","DOI":"10.1109\/TGRS.2018.2842707","article-title":"Band-wise nonlinear unmixing for hyperspectral imagery using an extended multilinear mixing model","volume":"56","author":"Yang","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"5520313","DOI":"10.1109\/TGRS.2021.3135571","article-title":"Nonlinear unmixing for hyperspectral images via kernel-transformed bilinear mixing models","volume":"60","author":"Gu","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"4338","DOI":"10.1109\/JSEN.2022.3143852","article-title":"Spatial-spectral nonlinear hyperspectral unmixing under complex noise","volume":"22","author":"Li","year":"2022","journal-title":"IEEE Sens. J."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"5506513","DOI":"10.1109\/TGRS.2021.3077833","article-title":"Unsupervised hyperspectral unmixing via nonlinear autoencoders","volume":"60","author":"Shahid","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"8615","DOI":"10.1109\/TGRS.2020.3041157","article-title":"Deep autoencoders with multitask learning for bilinear hyperspectral unmixing","volume":"59","author":"Su","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_22","first-page":"5509415","article-title":"Hyperspectral unmixing for additive nonlinear models with a 3-D-CNN autoencoder network","volume":"60","author":"Zhao","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"5536315","DOI":"10.1109\/TGRS.2022.3202490","article-title":"HapkeCNN: Blind nonlinear unmixing for intimate mixtures using hapke model and convolutional neural network","volume":"60","author":"Rasti","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_24","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_25","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1109\/MGRS.2019.2890997","article-title":"Spectral variability of remotely sensed target materials: Causes, models, and strategies for mitigation and robust exploitation","volume":"7","author":"Theiler","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"111471","DOI":"10.1016\/j.rse.2019.111471","article-title":"Assessing the impact of endmember variability on linear spectral mixture analysis (LSMA): A theoretical and simulation analysis","volume":"235","author":"Zhang","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_27","unstructured":"Haavardsholm, T.V., Skauli, T., and Kasen, I. (2007, January 23\u201328). A physics-based statistical signature model for hyperspectral target detection. Proceedings of the 2007 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Barcelona, Spain."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"2075","DOI":"10.1080\/01431160050021303","article-title":"Using vegetation reflectance variability for species level classification of hyperspectral data","volume":"21","author":"Cochrane","year":"2010","journal-title":"Int. J. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1016\/j.rse.2006.06.010","article-title":"Intra- and inter-class spectral variability of tropical tree species at la selva, costa rica: Implications for species identification using hydice imagery","volume":"105","author":"Zhang","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_30","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":"2014","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1016\/S0034-4257(98)00037-6","article-title":"Mapping chaparral in the Santa Monica Mountains using multiple endmember spectral mixture models","volume":"65","author":"Roberts","year":"1998","journal-title":"Remote Sens. Environ."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"4983","DOI":"10.1109\/TGRS.2016.2554160","article-title":"Hyperspectral unmixing with endmember variability via alternating angle minimization","volume":"54","author":"Heylen","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"525","DOI":"10.1109\/TSP.2015.2486746","article-title":"Hyperspectral unmixing with spectral variability using a perturbed linear mixing model","volume":"64","author":"Thouvenin","year":"2016","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_34","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_35","doi-asserted-by":"crossref","unstructured":"Drumetz, L., Chanussot, J., and Iwasaki, A. (2018, January 15\u201320). Endmembers as directional data for robust material variability retrieval in hyperspectral image unmixing. Proceedings of the 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Calgary, AB, Canada.","DOI":"10.1109\/ICASSP.2018.8462256"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"3638","DOI":"10.1109\/TIP.2020.2963959","article-title":"A data dependent multiscale model for hyperspectral unmixing with spectral variability","volume":"29","author":"Borsoi","year":"2020","journal-title":"IEEE Trans. Image Process."},{"key":"ref_37","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_38","doi-asserted-by":"crossref","first-page":"1351","DOI":"10.1109\/JSTSP.2018.2877497","article-title":"SULoRA: Subspace unmixing with low-rank attribute embedding for hyperspectral data analysis","volume":"12","author":"Hong","year":"2018","journal-title":"IEEE J. Sel. Top. Signal Process."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"711","DOI":"10.1109\/34.598228","article-title":"Eigenfaces vs. Fisherfaces: Recognition using class specific linear projection","volume":"19","author":"Belhumeur","year":"1997","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"699","DOI":"10.1109\/LGRS.2010.2046134","article-title":"A novel approach based on fisher discriminant null space for decomposition of mixed pixels in hyperspectral imagery","volume":"7","author":"Jin","year":"2010","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"449","DOI":"10.1109\/LGRS.2017.2648863","article-title":"An orthogonal fisher transformation-based unmixing method toward estimating fractional vegetation cover in semiarid areas","volume":"14","author":"Liu","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"111311","DOI":"10.1016\/j.rse.2019.111311","article-title":"Mapping impervious surface fractions using automated fisher transformed unmixing","volume":"232","author":"Xu","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_43","first-page":"5521914","article-title":"A 3-D-CNN framework for hyperspectral unmixing with spectral variability","volume":"60","author":"Zhao","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"5516915","DOI":"10.1109\/TGRS.2022.3168712","article-title":"Probabilistic generative model for hyperspectral unmixing accounting for endmember variability","volume":"60","author":"Shi","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"5527214","DOI":"10.1109\/TGRS.2022.3168712","article-title":"Deep generative model for spatial\u2013spectral unmixing with multiple endmember priors","volume":"60","author":"Shi","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"6518","DOI":"10.1109\/TNNLS.2021.3082289","article-title":"Endmember-guided unmixing network (EGU-net): A general deep learning framework for self-supervised hyperspectral unmixing","volume":"33","author":"Hong","year":"2022","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_47","first-page":"5529618","article-title":"Supervised nonlinear hyperspectral unmixing with automatic shadow compensation using multiswarm particle swarm optimization","volume":"60","author":"Yang","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1866","DOI":"10.1109\/LGRS.2019.2958203","article-title":"Spectral unmixing: A derivation of the extended linear mixing model from the hapke model","volume":"17","author":"Drumetz","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"3693","DOI":"10.1109\/JSTARS.2017.2682281","article-title":"Constrained nonnegative matrix factorization based on particle swarm optimization for hyperspectral unmixing","volume":"10","author":"Yang","year":"2017","journal-title":"IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"2654","DOI":"10.1109\/TGRS.2013.2264392","article-title":"Nonlinear estimation of material abundances in hyperspectral images with l1-norm spatial regularization","volume":"52","author":"Chen","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"6287","DOI":"10.1109\/TGRS.2017.2724944","article-title":"Spatial group sparsity regularized nonnegative matrix factorization for hyperspectral unmixing","volume":"55","author":"Wang","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"848","DOI":"10.1080\/00207721.2018.1424964","article-title":"Orthogonal sparse linear discriminant analysis","volume":"49","author":"Liu","year":"2018","journal-title":"Int. J. Syst. Sci."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"390","DOI":"10.1109\/TCSVT.2018.2799214","article-title":"Robust sparse linear discriminant analysis","volume":"29","author":"Wen","year":"2019","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"4484","DOI":"10.1109\/TGRS.2012.2191590","article-title":"Total variation spatial regularization for sparse hyperspectral unmixing","volume":"50","author":"Iordache","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1007\/s11721-007-0002-0","article-title":"Particle swarm optimization","volume":"1","author":"Poli","year":"2007","journal-title":"Swarm Intell."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"7872","DOI":"10.1109\/TGRS.2019.2917001","article-title":"An improved multiobjective discrete particle swarm optimization for hyperspectral endmember extraction","volume":"57","author":"Tong","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"6013605","DOI":"10.1109\/LGRS.2022.3203990","article-title":"Multilevel reweighted sparse hyperspectral unmixing using superpixel segmentation and particle swarm optimization","volume":"19","author":"Miao","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_58","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_59","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_60","doi-asserted-by":"crossref","first-page":"9858","DOI":"10.1109\/TGRS.2019.2929776","article-title":"Regularization parameter selection in minimum volume hyperspectral unmixing","volume":"57","author":"Zhuang","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"4534","DOI":"10.1109\/TGRS.2017.2693366","article-title":"Unsupervised nonlinear spectral unmixing based on a multilinear mixing model","volume":"55","author":"Wei","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"778","DOI":"10.1109\/LGRS.2013.2278993","article-title":"A bilinear\u2013bilinear nonnegative matrix factorization method for hyperspectral unmixing","volume":"11","author":"Eches","year":"2014","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"1567","DOI":"10.1109\/LSP.2017.2747478","article-title":"Relationships between nonlinear and space-variant linear models in hyperspectral image unmixing","volume":"24","author":"Drumetz","year":"2017","journal-title":"IEEE Signal Process Lett."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"608","DOI":"10.1109\/TGRS.2003.819189","article-title":"Estimation of number of spectrally distinct signal sources in hyperspectral imagery","volume":"42","author":"Chang","year":"2004","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"1174","DOI":"10.1109\/TGRS.2003.813211","article-title":"Vicarious radiometric calibration of EO-1 sensors by reference to high-reflectance ground targets","volume":"41","author":"Biggar","year":"2003","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"5502117","DOI":"10.1109\/TGRS.2023.3236818","article-title":"Cross-track illumination correction for hyperspectral pushbroom sensor images using low-rank and sparse representations","volume":"61","author":"Zhuang","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"7873","DOI":"10.1080\/01431161.2010.532175","article-title":"Evaluation of cross-track illumination in EO-1 Hyperion imagery for lithological mapping","volume":"32","author":"San","year":"2011","journal-title":"Int. J. Remote Sens."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"1246","DOI":"10.1109\/TGRS.2003.813206","article-title":"Preprocessing EO-1 Hyperion hyperspectral data to support the application of agricultural indexes","volume":"41","author":"Datt","year":"2003","journal-title":"IEEE Trans. Geosci. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/20\/5028\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T21:09:57Z","timestamp":1760130597000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/20\/5028"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,19]]},"references-count":68,"journal-issue":{"issue":"20","published-online":{"date-parts":[[2023,10]]}},"alternative-id":["rs15205028"],"URL":"https:\/\/doi.org\/10.3390\/rs15205028","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,10,19]]}}}