{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T10:35:27Z","timestamp":1780914927878,"version":"3.54.1"},"reference-count":113,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2023,8,1]],"date-time":"2023-08-01T00:00:00Z","timestamp":1690848000000},"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>Hyperspectral data analysis is being utilized as an effective and compelling tool for image processing, providing unprecedented levels of information and insights for various applications. In this manuscript, we have compiled and presented a comprehensive overview of recent advances in hyperspectral data analysis that can provide assistance for the development of customized techniques for hyperspectral document images. We review the fundamental concepts of hyperspectral imaging, discuss various techniques for data acquisition, and examine state-of-the-art approaches to the preprocessing, feature extraction, and classification of hyperspectral data by taking into consideration the complexities of document images. We also explore the possibility of utilizing hyperspectral imaging for addressing critical challenges in document analysis, including document forgery, ink age estimation, and text extraction from degraded or damaged documents. Finally, we discuss the current limitations of hyperspectral imaging and identify future research directions in this rapidly evolving field. Our review provides a valuable resource for researchers and practitioners working on document image processing and highlights the potential of hyperspectral imaging for addressing complex challenges in this domain.<\/jats:p>","DOI":"10.3390\/s23156845","type":"journal-article","created":{"date-parts":[[2023,8,1]],"date-time":"2023-08-01T09:32:35Z","timestamp":1690882355000},"page":"6845","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["Analysis of Hyperspectral Data to Develop an Approach for Document Images"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-1230-903X","authenticated-orcid":false,"given":"Zainab","family":"Zaman","sequence":"first","affiliation":[{"name":"School of Electrical Engineering and Computer Science, National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0631-9368","authenticated-orcid":false,"given":"Saad Bin","family":"Ahmed","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Faculty of Science and Environmental Studies, Lakehead University, Thunder Bay, ON P7B 5E1, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Muhammad Imran","family":"Malik","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering and Computer Science, National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan"},{"name":"National Center of Artificial Intelligence, National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,8,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/j.patcog.2019.01.026","article-title":"Hyperspectral document image processing: Applications, challenges and future prospects","volume":"90","author":"Qureshi","year":"2019","journal-title":"Pattern Recognit."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1016\/j.aca.2015.09.030","article-title":"Hyperspectral image analysis. A tutorial","volume":"896","author":"Amigo","year":"2015","journal-title":"Anal. Chim. Acta"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"508","DOI":"10.1109\/LSP.2021.3059204","article-title":"Object detection in hyperspectral images","volume":"28","author":"Yan","year":"2021","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_4","first-page":"833","article-title":"Hyperspectral image processing and analysis","volume":"108","author":"Mohan","year":"2015","journal-title":"Curr. Sci."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1454","DOI":"10.1016\/j.jas.2012.11.001","article-title":"Integration of geophysical surveys, ground hyperspectral measurements, aerial and satellite imagery for archaeological prospection of prehistoric sites: The case study of V\u00e9szt\u0151-M\u00e1gor Tell, Hungary","volume":"40","author":"Sarris","year":"2013","journal-title":"J. Archaeol. Sci."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"412","DOI":"10.1016\/j.microc.2016.10.024","article-title":"Projection pursuit and PCA associated with near and middle infrared hyperspectral images to investigate forensic cases of fraudulent documents","volume":"130","author":"Pereira","year":"2017","journal-title":"Microchem. J."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1016\/j.forsciint.2012.09.012","article-title":"Hyperspectral imaging for non-contact analysis of forensic traces","volume":"223","author":"Edelman","year":"2012","journal-title":"Forensic Sci. Int."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"102192","DOI":"10.1016\/j.jksus.2022.102192","article-title":"Detecting hydrocarbon micro-seepage and related contamination, probable prospect areas, deduced from a comparative analysis of multispectral and hyperspectral satellite images","volume":"34","author":"Alshehri","year":"2022","journal-title":"J. King Saud Univ. Sci."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Butt, U.M., Ahmad, S., Shafait, F., Nansen, C., Mian, A.S., and Malik, M.I. (2016, January 23\u201326). Automatic signature segmentation using hyper-spectral imaging. Proceedings of the 2016 15th International Conference on Frontiers in Handwriting Recognition (ICFHR), Shenzhen, China.","DOI":"10.1109\/ICFHR.2016.0017"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1461","DOI":"10.1016\/j.patcog.2010.12.019","article-title":"Visual enhancement of old documents with hyperspectral imaging","volume":"44","author":"Kim","year":"2011","journal-title":"Pattern Recognit."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"053001","DOI":"10.1117\/1.JEI.27.5.053001","article-title":"Deep learning for automated forgery detection in hyperspectral document images","volume":"27","author":"Khan","year":"2018","journal-title":"J. Electron. Imaging"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Lu, B., Dao, P.D., Liu, J., He, Y., and Shang, J. (2020). Recent advances of hyperspectral imaging technology and applications in agriculture. Remote Sens., 12.","DOI":"10.3390\/rs12162659"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"5205","DOI":"10.1007\/s10462-021-10018-y","article-title":"A review of deep learning used in the hyperspectral image analysis for agriculture","volume":"54","author":"Wang","year":"2021","journal-title":"Artif. Intell. Rev."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Petersson, H., Gustafsson, D., and Bergstrom, D. (2016, January 12\u201315). Hyperspectral image analysis using deep learning\u2014A review. Proceedings of the 2016 Sixth International Conference on Image Processing Theory, Tools and Applications (IPTA), Oulu, Finland.","DOI":"10.1109\/IPTA.2016.7820963"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"597","DOI":"10.1007\/s10043-019-00528-0","article-title":"A novel two-stage deep learning-based small-object detection using hyperspectral images","volume":"26","author":"Yan","year":"2019","journal-title":"Opt. Rev."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Rosario-Torres, S., and V\u00e9lez-Reyes, M. (2005, January 1). An algorithm for fully constrained abundance estimation in hyperspectral unmixing. Proceedings of the Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XI. SPIE, Orlando, FL, USA.","DOI":"10.1117\/12.605670"},{"key":"ref_17","unstructured":"Torres, S.R. (2004). Iterative Algorithms for Abundance Estimation on Unmixing of Hyperspectral Imagery, University of Puerto Rico."},{"key":"ref_18","unstructured":"Veganzones, M.A., and Grana, M. (2008, January 3\u20135). Endmember extraction methods: A short review. Proceedings of the International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, Zagreb, Croatia."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1016\/B978-0-444-63638-6.00006-1","article-title":"Linear and nonlinear unmixing in hyperspectral imaging","volume":"Volume 30","author":"Dobigeon","year":"2016","journal-title":"Data Handling in Science and Technology"},{"key":"ref_20","unstructured":"Clevers, J., and Zurita-Milla, R. (2008). Image Fusion: Algorithms and Applications, Elsevier."},{"key":"ref_21","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_22","doi-asserted-by":"crossref","first-page":"3276","DOI":"10.1109\/IGARSS.2004.1370401","article-title":"Solving adundance estimation in hyperspectral unmixing as a least distance problem","volume":"Volume 5","author":"Rosario","year":"2004","journal-title":"Proceedings of the IGARSS 2004. 2004 IEEE International Geoscience and Remote Sensing Symposium"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Chang, C.I. (2016). Real-Time Progressive Hyperspectral Image Processing, Springer.","DOI":"10.1007\/978-1-4419-6187-7"},{"key":"ref_24","first-page":"1","article-title":"Fast Orthogonal Projection for Hyperspectral Unmixing","volume":"60","author":"Tao","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1850045","DOI":"10.1142\/S0218126618500457","article-title":"Fast FPGA implementation for computing the pixel purity index of hyperspectral images","volume":"27","author":"Guo","year":"2018","journal-title":"J. Circuits, Syst. Comput."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Gu, J., Wu, Z., Li, Y., Chen, Y., Wei, Z., and Wang, W. (November, January 30). Parallel optimization of pixel purity index algorithm for hyperspectral unmixing based on spark. Proceedings of the 2015 Third International Conference on Advanced Cloud and Big Data, Yangzhou, China.","DOI":"10.1109\/CBD.2015.34"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1464","DOI":"10.1109\/LGRS.2017.2710219","article-title":"Progressive band processing of fast iterative pixel purity index for finding endmembers","volume":"14","author":"Chang","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"S\u00e1nchez, S., and Plaza, A. (2010, January 20\u201324). GPU implementation of the pixel purity index algorithm for hyperspectral image analysis. Proceedings of the 2010 IEEE International Conference On Cluster Computing Workshops and Posters (CLUSTER WORKSHOPS), Heraklion, Greece.","DOI":"10.1109\/CLUSTERWKSP.2010.5613110"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"969806","DOI":"10.1155\/2010\/969806","article-title":"FPGA implementation of the pixel purity index algorithm for remotely sensed hyperspectral image analysis","volume":"2010","author":"Resano","year":"2010","journal-title":"Eurasip J. Adv. Signal Process."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"955","DOI":"10.1109\/LGRS.2013.2283214","article-title":"Real-time implementation of the pixel purity index algorithm for endmember identification on GPUs","volume":"11","author":"Wu","year":"2013","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Valencia, D., and Plaza, A. (2006, January 28\u201331). FPGA-based hyperspectral data compression using spectral unmixing and the pixel purity index algorithm. Proceedings of the Computational Science\u2013ICCS 2006: 6th International Conference, Reading, UK. Proceedings, Part I 6.","DOI":"10.1007\/11758501_130"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"316","DOI":"10.1080\/22797254.2020.1850179","article-title":"Improving hyperspectral sub-pixel target detection in multiple target signatures using a revised replacement signal model","volume":"53","author":"Hasanlou","year":"2020","journal-title":"Eur. J. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"4915","DOI":"10.1109\/JSTARS.2021.3068983","article-title":"Orthogonal subspace projection target detector for hyperspectral anomaly detection","volume":"14","author":"Chang","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_34","unstructured":"Sierra-Pajuelo, F., Paz-Gallardo, A., and Plaza, A. (2015, January 24\u201327). Perfomance optimizations for an automatic target generation process in hyperspectral analysis. Proceedings of the ARCS 2015-The 28th International Conference on Architecture of Computing Systems, Porto, Portugal. Proceedings."},{"key":"ref_35","first-page":"589","article-title":"Parallel implementation of target and anomaly detection algorithms for hyperspectral imagery","volume":"Volume 2","author":"Paz","year":"2008","journal-title":"Proceedings of the IGARSS 2008-2008 IEEE International Geoscience and Remote Sensing Symposium"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Yadav, P.P., Bobate, N., Shetty, A., Raghavendra, B., and Narasimhadhan, A. (2022, January 17\u201320). ATGP based Change Detection in Hyperspectral Images. Proceedings of the IECON 2022\u201348th Annual Conference of the IEEE Industrial Electronics Society, Brussels, Belgium.","DOI":"10.1109\/IECON49645.2022.9969049"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"5081","DOI":"10.1109\/TGRS.2016.2553845","article-title":"Recursive band processing of automatic target generation process for finding unsupervised targets in hyperspectral imagery","volume":"54","author":"Chang","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"073523","DOI":"10.1117\/1.JRS.7.073523","article-title":"Analysis of end member detection and subpixel classification algorithms on hyperspectral imagery for tropical mangrove species discrimination in the Sunderbans Delta, India","volume":"7","author":"Chakravortty","year":"2013","journal-title":"J. Appl. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Pollino, M., Cappucci, S., Giordano, L., Iantosca, D., De Cecco, L., Bersan, D., Rosato, V., and Borfecchia, F. (2020). Assessing earthquake-induced urban rubble by means of multiplatform remotely sensed data. Isprs Int. J. Geo Inf., 9.","DOI":"10.3390\/ijgi9040262"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Milewski, R., Chabrillat, S., and Bookhagen, B. (2020). Analyses of Namibian seasonal salt pan crust dynamics and climatic drivers using Landsat 8 time-series and ground data. Remote Sens., 12.","DOI":"10.3390\/rs12030474"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"2933","DOI":"10.1007\/s11053-020-09639-x","article-title":"Sub-pixel mapping of copper-and iron-bearing metamorphic rocks using ASTER data: A case study of Toutak and Surian complexes, NE Shiraz, Iran","volume":"29","author":"Esmaeili","year":"2020","journal-title":"Nat. Resour. Res."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1130","DOI":"10.1109\/JSTQE.2011.2164239","article-title":"Absorption-based hyperspectral imaging and analysis of single erythrocytes","volume":"18","author":"Lee","year":"2011","journal-title":"IEEE J. Sel. Top. Quantum Electron."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1109\/JSTARS.2010.2051535","article-title":"Mapping impervious cover using multi-temporal MODIS NDVI data","volume":"4","author":"Knight","year":"2010","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1007\/s10661-022-10576-w","article-title":"Evaluating automated endmember extraction for classifying hyperspectral data and deriving spectral parameters for monitoring forest vegetation health","volume":"195","author":"Singh","year":"2023","journal-title":"Environ. Monit. Assess."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"849","DOI":"10.1109\/LGRS.2018.2888574","article-title":"Cofactor-based efficient endmember extraction for green algae area estimation","volume":"16","author":"Tao","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"545","DOI":"10.1109\/JSTARS.2011.2119466","article-title":"Fast algorithms to implement N-FINDR for hyperspectral endmember extraction","volume":"4","author":"Xiong","year":"2011","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"2148","DOI":"10.1080\/01431161.2015.1034895","article-title":"Modified N-FINDR endmember extraction algorithm for remote-sensing imagery","volume":"36","author":"Ji","year":"2015","journal-title":"Int. J. Remote Sens."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"2593","DOI":"10.3390\/s150202593","article-title":"Automatic extraction of optimal endmembers from airborne hyperspectral imagery using iterative error analysis (IEA) and spectral discrimination measurements","volume":"15","author":"Song","year":"2015","journal-title":"Sensors"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Ayma Quirita, V.A., da Costa, G.A.O.P., and Beltr\u00e1n, C. (2022). A Distributed N-FINDR Cloud Computing-Based Solution for Endmembers Extraction on Large-Scale Hyperspectral Remote Sensing Data. Remote Sens., 14.","DOI":"10.3390\/rs14092153"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"2369","DOI":"10.1109\/IGARSS.1994.399740","article-title":"Geometric mixture analysis of imaging spectrometry data","volume":"Vol. 4","author":"Boardman","year":"1994","journal-title":"Proceedings of the Proceedings of IGARSS\u201994-1994 IEEE International Geoscience and Remote Sensing Symposium"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Plaza, A., and Chang, C.I. (2005, January 1). Fast implementation of pixel purity index algorithm. Proceedings of the Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XI. SPIE, Orlando, FL, USA.","DOI":"10.1117\/12.602374"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1232","DOI":"10.1109\/TAES.2003.1261124","article-title":"Automatic spectral target recognition in hyperspectral imagery","volume":"39","author":"Ren","year":"2003","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_53","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_54","doi-asserted-by":"crossref","unstructured":"Winter, M.E. (1999, January 27). N-FINDR: An algorithm for fast autonomous spectral end-member determination in hyperspectral data. Proceedings of the Imaging Spectrometry V. SPIE, Denver, CO, USA.","DOI":"10.1117\/12.366289"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"1401","DOI":"10.1109\/IGARSS.1999.774644","article-title":"Fully constrained least-squares based linear unmixing [hyperspectral image classification]","volume":"Volume 2","author":"Heinz","year":"1999","journal-title":"Proceedings of the IEEE 1999 International Geoscience and Remote Sensing Symposium. IGARSS\u201999 (Cat. No. 99CH36293)"},{"key":"ref_56","first-page":"465","article-title":"Least squares methods","volume":"1","year":"1990","journal-title":"Handb. Numer. Anal."},{"key":"ref_57","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_58","first-page":"269","article-title":"Automatic signature extraction from document images using hyperspectral unmixing: Automatic signature extraction using hyperspectral unmixing","volume":"54","author":"Iqbal","year":"2017","journal-title":"Proc. Pak. Acad. Sci. Phys. Comput. Sci."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"1484","DOI":"10.1364\/JOSAA.19.001484","article-title":"Statistics of spatial cone-excitation ratios in natural scenes","volume":"19","author":"Nascimento","year":"2002","journal-title":"JOSA A"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1017\/S0952523804213335","article-title":"Information limits on neural identification of colored surfaces in natural scenes","volume":"21","author":"Foster","year":"2004","journal-title":"Vis. Neurosci."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Chakrabarti, A., and Zickler, T. (2011, January 20\u201325). Statistics of real-world hyperspectral images. Proceedings of the CVPR 2011, Colorado Springs, CO, USA.","DOI":"10.1109\/CVPR.2011.5995660"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Liang, J., Zhou, J., Bai, X., and Qian, Y. (2013, January 15\u201318). Salient object detection in hyperspectral imagery. Proceedings of the 2013 IEEE International Conference on Image Processing, Melbourne, VIC, Australia.","DOI":"10.1109\/ICIP.2013.6738493"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Ke, C. (2017, January 15\u201317). Military object detection using multiple information extracted from hyperspectral imagery. Proceedings of the 2017 International Conference on Progress in Informatics and Computing (PIC), Nanjing, China.","DOI":"10.1109\/PIC.2017.8359527"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"4843","DOI":"10.1109\/TIP.2017.2725580","article-title":"Going deeper with contextual CNN for hyperspectral image classification","volume":"26","author":"Lee","year":"2017","journal-title":"IEEE Trans. Image Process."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"6690","DOI":"10.1109\/TGRS.2019.2907932","article-title":"Deep learning for hyperspectral image classification: An overview","volume":"57","author":"Li","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Zhu, G., Zheng, Y., Doermann, D., and Jaeger, S. (2007, January 17\u201322). Multi-scale structural saliency for signature detection. Proceedings of the 2007 IEEE Conference on Computer Vision and Pattern Recognition, Minneapolis, MN, USA.","DOI":"10.1109\/CVPR.2007.383255"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1016\/j.isprsjprs.2019.09.006","article-title":"Deep learning classifiers for hyperspectral imaging: A review","volume":"158","author":"Paoletti","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_68","unstructured":"Doetsch, P., Golik, P., and Ney, H. (2017). A comprehensive study of batch construction strategies for recurrent neural networks in mxnet. arXiv."},{"key":"ref_69","unstructured":"Dowd, K., and Severance, C. (2010). High Performance Computing, OpenStax CNX."},{"key":"ref_70","unstructured":"Quinn, M.J. (1994). Parallel Computing Theory and Practice, McGraw-Hill, Inc."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1145\/1562764.1562783","article-title":"A view of the parallel computing landscape","volume":"52","author":"Asanovic","year":"2009","journal-title":"Commun. ACM"},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Signoroni, A., Savardi, M., Baronio, A., and Benini, S. (2019). Deep learning meets hyperspectral image analysis: A multidisciplinary review. J. Imaging, 5.","DOI":"10.3390\/jimaging5050052"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"433","DOI":"10.1002\/wics.101","article-title":"Principal component analysis","volume":"2","author":"Abdi","year":"2010","journal-title":"Wiley Interdiscip. Rev. Comput. Stat."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/S1364-6613(00)01813-1","article-title":"Independent component analysis: An introduction","volume":"6","author":"Stone","year":"2002","journal-title":"Trends Cogn. Sci."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"4823","DOI":"10.1109\/TGRS.2019.2893180","article-title":"Learning compact and discriminative stacked autoencoder for hyperspectral image classification","volume":"57","author":"Zhou","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_76","unstructured":"Worrall, D., and Welling, M. (2019). Deep scale-spaces: Equivariance over scale. Adv. Neural Inf. Process. Syst., 32."},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Nusrat, I., and Jang, S.B. (2018). A comparison of regularization techniques in deep neural networks. Symmetry, 10.","DOI":"10.3390\/sym10110648"},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Ajit, A., Acharya, K., and Samanta, A. (2020, January 24\u201325). A review of convolutional neural networks. Proceedings of the 2020 International Conference on Emerging Trends in Information Technology and Engineering (ic-ETITE), Vellore, India.","DOI":"10.1109\/ic-ETITE47903.2020.049"},{"key":"ref_79","unstructured":"Tschannen, M., Bachem, O., and Lucic, M. (2018). Recent advances in autoencoder-based representation learning. arXiv."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1109\/MSP.2017.2765202","article-title":"Generative adversarial networks: An overview","volume":"35","author":"Creswell","year":"2018","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_81","first-page":"64","article-title":"Recurrent neural networks","volume":"5","author":"Medsker","year":"2001","journal-title":"Des. Appl."},{"key":"ref_82","doi-asserted-by":"crossref","unstructured":"Chen, X., Xiang, S., Liu, C.L., and Pan, C.H. (2013, January 5\u20138). Vehicle detection in satellite images by parallel deep convolutional neural networks. Proceedings of the 2013 2nd IAPR Asian Conference on Pattern Recognition, Naha, Japan.","DOI":"10.1109\/ACPR.2013.33"},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"1797","DOI":"10.1109\/LGRS.2014.2309695","article-title":"Vehicle detection in satellite images by hybrid deep convolutional neural networks","volume":"11","author":"Chen","year":"2014","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_84","doi-asserted-by":"crossref","unstructured":"Md Noor, S.S., Michael, K., Marshall, S., and Ren, J. (2017). Hyperspectral image enhancement and mixture deep-learning classification of corneal epithelium injuries. Sensors, 17.","DOI":"10.3390\/s17112644"},{"key":"ref_85","doi-asserted-by":"crossref","unstructured":"Yang, J., Zhao, Y., Chan, J.C.W., and Yi, C. (2016, January 10\u201315). Hyperspectral image classification using two-channel deep convolutional neural network. Proceedings of the 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Beijing, China.","DOI":"10.1109\/IGARSS.2016.7730324"},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"3997","DOI":"10.1109\/TGRS.2017.2686450","article-title":"Hyperspectral and LiDAR fusion using extinction profiles and total variation component analysis","volume":"55","author":"Rasti","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_87","doi-asserted-by":"crossref","unstructured":"Li, H., Ghamisi, P., Soergel, U., and Zhu, X.X. (2018). Hyperspectral and LiDAR fusion using deep three-stream convolutional neural networks. Remote Sens., 10.","DOI":"10.3390\/rs10101649"},{"key":"ref_88","doi-asserted-by":"crossref","unstructured":"Feng, Q., Zhu, D., Yang, J., and Li, B. (2019). Multisource hyperspectral and lidar data fusion for urban land-use mapping based on a modified two-branch convolutional neural network. ISPRS Int. J. Geo Inf., 8.","DOI":"10.3390\/ijgi8010028"},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"810","DOI":"10.1109\/TCYB.2018.2874166","article-title":"Adaptive Control of Nonlinear Semi-Markovian Jump TS Fuzzy Systems with Immeasurable Premise Variables via Sliding Mode Observer","volume":"50","author":"Jiang","year":"2018","journal-title":"IEEE Trans. Cybern."},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"667","DOI":"10.1109\/TGRS.2018.2859203","article-title":"HSI-DeNet: Hyperspectral image restoration via convolutional neural network","volume":"57","author":"Chang","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_91","doi-asserted-by":"crossref","unstructured":"Luo, Y., Zou, J., Yao, C., Zhao, X., Li, T., and Bai, G. (2018, January 16\u201317). HSI-CNN: A novel convolution neural network for hyperspectral image. Proceedings of the 2018 International Conference on Audio, Language and Image Processing (ICALIP), Shanghai, China.","DOI":"10.1109\/ICALIP.2018.8455251"},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"7048","DOI":"10.1109\/TGRS.2019.2910603","article-title":"Automatic design of convolutional neural network for hyperspectral image classification","volume":"57","author":"Chen","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"5701","DOI":"10.1109\/TGRS.2019.2901737","article-title":"A 3-D atrous convolution neural network for hyperspectral image denoising","volume":"57","author":"Liu","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"1559","DOI":"10.1109\/TIP.2022.3144017","article-title":"Weighted feature fusion of convolutional neural network and graph attention network for hyperspectral image classification","volume":"31","author":"Dong","year":"2022","journal-title":"IEEE Trans. Image Process."},{"key":"ref_95","unstructured":"Lin, Z., Chen, Y., Zhao, X., and Wang, G. (2013, January 10\u201313). Spectral-spatial classification of hyperspectral image using autoencoders. Proceedings of the 2013 9th international conference on information, Communications & Signal Processing, Tainan, Taiwan."},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"487","DOI":"10.1109\/TMM.2019.2928491","article-title":"Multiscale superpixel-based hyperspectral image classification using recurrent neural networks with stacked autoencoders","volume":"22","author":"Shi","year":"2019","journal-title":"IEEE Trans. Multimed."},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1080\/22797254.2017.1274566","article-title":"Spectral-spatial classification of hyperspectral imagery based on stacked sparse autoencoder and random forest","volume":"50","author":"Zhao","year":"2017","journal-title":"Eur. J. Remote Sens."},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1109\/LGRS.2020.2967815","article-title":"Band selection of hyperspectral images using attention-based autoencoders","volume":"18","author":"Dou","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"695","DOI":"10.1007\/s11042-021-11422-w","article-title":"A shallow network for hyperspectral image classification using an autoencoder with convolutional neural network","volume":"81","author":"Patel","year":"2022","journal-title":"Multimed. Tools Appl."},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"3318","DOI":"10.1109\/TCYB.2019.2915094","article-title":"Generative adversarial networks and conditional random fields for hyperspectral image classification","volume":"50","author":"Zhong","year":"2019","journal-title":"IEEE Trans. Cybern."},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"5046","DOI":"10.1109\/TGRS.2018.2805286","article-title":"Generative adversarial networks for hyperspectral image classification","volume":"56","author":"Zhu","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"6053","DOI":"10.1109\/JSTARS.2022.3192127","article-title":"HyperViTGAN: Semisupervised Generative Adversarial Network With Transformer for Hyperspectral Image Classification","volume":"15","author":"He","year":"2022","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"1424","DOI":"10.1109\/TGRS.2020.3003341","article-title":"Classification of hyperspectral images via multitask generative adversarial networks","volume":"59","author":"Hang","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_104","doi-asserted-by":"crossref","first-page":"4141","DOI":"10.1109\/JSTARS.2018.2844873","article-title":"Spatial sequential recurrent neural network for hyperspectral image classification","volume":"11","author":"Zhang","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_105","doi-asserted-by":"crossref","first-page":"5384","DOI":"10.1109\/TGRS.2019.2899129","article-title":"Cascaded recurrent neural networks for hyperspectral image classification","volume":"57","author":"Hang","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_106","doi-asserted-by":"crossref","first-page":"3639","DOI":"10.1109\/TGRS.2016.2636241","article-title":"Deep recurrent neural networks for hyperspectral image classification","volume":"55","author":"Mou","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_107","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1016\/j.neucom.2018.03.012","article-title":"Multi-scale hierarchical recurrent neural networks for hyperspectral image classification","volume":"294","author":"Shi","year":"2018","journal-title":"Neurocomputing"},{"key":"ref_108","doi-asserted-by":"crossref","first-page":"8866","DOI":"10.1007\/s11227-020-03187-0","article-title":"Scalable recurrent neural network for hyperspectral image classification","volume":"76","author":"Paoletti","year":"2020","journal-title":"J. Supercomput."},{"key":"ref_109","first-page":"7","article-title":"Structural similarity measure for color images","volume":"43","author":"Hassan","year":"2012","journal-title":"Int. J. Comput. Appl."},{"key":"ref_110","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1049\/iet-ipr.2012.0489","article-title":"Is there a relationship between peak-signal-to-noise ratio and structural similarity index measure?","volume":"7","author":"Ziou","year":"2013","journal-title":"IET Image Process."},{"key":"ref_111","first-page":"8","article-title":"Introduction to hyperspectral image analysis","volume":"2","author":"Shippert","year":"2003","journal-title":"Online J. Space Commun."},{"key":"ref_112","unstructured":"Girouard, G., Bannari, A., El Harti, A., and Desrochers, A. (2004, January 12\u201323). Validated spectral angle mapper algorithm for geological mapping: Comparative study between QuickBird and Landsat-TM. Proceedings of the XXth ISPRS Congress, Geo-Imagery Bridging Continents, Istanbul, Turkey."},{"key":"ref_113","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1007\/s10851-011-0276-0","article-title":"On endmember identification in hyperspectral images without pure pixels: A comparison of algorithms","volume":"42","author":"Plaza","year":"2012","journal-title":"J. Math. Imaging Vis."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/15\/6845\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:23:57Z","timestamp":1760127837000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/15\/6845"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,1]]},"references-count":113,"journal-issue":{"issue":"15","published-online":{"date-parts":[[2023,8]]}},"alternative-id":["s23156845"],"URL":"https:\/\/doi.org\/10.3390\/s23156845","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,8,1]]}}}