{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T12:19:14Z","timestamp":1784204354498,"version":"3.55.0"},"reference-count":137,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2026,1,6]],"date-time":"2026-01-06T00:00:00Z","timestamp":1767657600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Hyperspectral imaging (HSI) captures rich spectral information across a wide range of wavelengths, enabling advanced applications in remote sensing, environmental monitoring, medical diagnosis, and related domains. However, the high dimensionality, spectral variability, and inherent noise of HSI data present significant challenges for efficient processing and reliable analysis. In recent years, Generative Adversarial Networks (GANs) have emerged as transformative deep learning paradigms, demonstrating strong capabilities in data generation, augmentation, feature learning, and representation modeling. Consequently, the integration of GANs into HSI analysis has gained substantial research attention, resulting in a diverse range of architectures tailored to HSI-specific tasks. Despite these advances, existing survey studies often focus on isolated problems or individual application domains, limiting a comprehensive understanding of the broader GAN\u2013HSI landscape. To address this gap, this paper presents a comprehensive review of GAN-based hyperspectral imaging research. The review systematically examines the evolution of GAN\u2013HSI integration, categorizes representative GAN architectures, analyzes domain-specific applications, and discusses commonly adopted hyperparameter tuning strategies. Furthermore, key research challenges and open issues are identified, and promising future research directions are outlined. This synergy addresses critical hyperspectral data analysis challenges while unlocking transformative innovations across multiple sectors.<\/jats:p>","DOI":"10.3390\/rs18020196","type":"journal-article","created":{"date-parts":[[2026,1,7]],"date-time":"2026-01-07T08:26:56Z","timestamp":1767774416000},"page":"196","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["A Dive into Generative Adversarial Networks in the World of Hyperspectral Imaging: A Survey of the State of the Art"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7830-5678","authenticated-orcid":false,"given":"Pallavi","family":"Ranjan","sequence":"first","affiliation":[{"name":"School of IT, Murdoch University Dubai, Dubai P.O. Box 500700, United Arab Emirates"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ankur","family":"Nandal","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, National Institute of Technology, Delhi 110036, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3836-2595","authenticated-orcid":false,"given":"Saurabh","family":"Agarwal","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Yeungnam University, Gyeongsan 38541, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5000-7644","authenticated-orcid":false,"given":"Rajeev","family":"Kumar","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Delhi Technological University, Delhi 110042, India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,1,6]]},"reference":[{"key":"ref_1","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_2","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_3","doi-asserted-by":"crossref","first-page":"120452","DOI":"10.1016\/j.ins.2024.120452","article-title":"Robust hyperspectral image classification using generative adversarial networks","volume":"666","author":"Yu","year":"2024","journal-title":"Inf. Sci."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"5329","DOI":"10.1109\/TGRS.2019.2899057","article-title":"Classification of hyperspectral images based on multiclass spatial\u2013spectral generative adversarial networks","volume":"57","author":"Feng","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"3330","DOI":"10.1109\/JSTARS.2021.3063911","article-title":"HSIGAN: A conditional hyperspectral image synthesis method with auxiliary classifier","volume":"14","author":"Liu","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Chen, C., Wang, Y., Zhang, N., Zhang, Y., and Zhao, Z. (2023). A review of hyperspectral image super-resolution based on deep learning. Remote Sens., 15.","DOI":"10.3390\/rs15112853"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Feng, H., Wang, Y., Li, Z., Zhang, N., Zhang, Y., and Gao, Y. (2023). Information leakage in deep learning-based hyperspectral image classification: A survey. Remote Sens., 15.","DOI":"10.3390\/rs15153793"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"107208","DOI":"10.1016\/j.compag.2022.107208","article-title":"Generative adversarial networks (GANs) for image augmentation in agriculture: A systematic review","volume":"200","author":"Lu","year":"2022","journal-title":"Comput. Electron. Agric."},{"key":"ref_9","first-page":"5524617","article-title":"Unsupervised spatial-spectral cnn-based feature learning for hyperspectral image classification","volume":"60","author":"Zhang","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1016\/j.neucom.2023.03.025","article-title":"Land use and land cover classification with hyperspectral data: A comprehensive review of methods, challenges and future directions","volume":"536","author":"Moharram","year":"2023","journal-title":"Neurocomputing"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"7303","DOI":"10.1109\/TNNLS.2021.3084745","article-title":"Generative dual-adversarial network with spectral fidelity and spatial enhancement for hyperspectral pansharpening","volume":"33","author":"Dong","year":"2021","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"7336","DOI":"10.1109\/ACCESS.2022.3232152","article-title":"Two-branch generative adversarial network with multiscale connections for hyperspectral image classification","volume":"11","author":"Song","year":"2022","journal-title":"IEEE Access"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"10017","DOI":"10.1109\/JSTARS.2021.3115971","article-title":"Spectral\u2013spatial attention feature extraction for hyperspectral image classification based on generative adversarial network","volume":"14","author":"Liang","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"100658","DOI":"10.1016\/j.cosrev.2024.100658","article-title":"Deep learning for hyperspectral image classification: A survey","volume":"53","author":"Kumar","year":"2024","journal-title":"Comput. Sci. Rev."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"5531115","DOI":"10.1109\/TGRS.2023.3332176","article-title":"QIS-GAN: A lightweight adversarial network with quadtree implicit sampling for multispectral and hyperspectral image fusion","volume":"61","author":"Zhu","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"5251","DOI":"10.1007\/s12145-024-01451-y","article-title":"A novel spectral-spatial 3D auxiliary conditional GAN integrated convolutional LSTM for hyperspectral image classification","volume":"17","author":"Ranjan","year":"2024","journal-title":"Earth Sci. Inform."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"6221","DOI":"10.1080\/01431161.2022.2133579","article-title":"A comprehensive systematic review of deep learning methods for hyperspectral images classification","volume":"43","author":"Ranjan","year":"2022","journal-title":"Int. J. Remote Sens."},{"key":"ref_18","first-page":"5505732","article-title":"Transfer Learning of Spatial Features from High-resolution RGB Images for Large-scale and Robust Hyperspectral Remote Sensing Target Detection","volume":"62","author":"Wu","year":"2024","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"8335","DOI":"10.1007\/s00521-024-09527-y","article-title":"A 3D-convolutional-autoencoder embedded Siamese-attention-network for classification of hyperspectral images","volume":"36","author":"Ranjan","year":"2024","journal-title":"Neural Comput. Appl."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"5509314","DOI":"10.1109\/TGRS.2024.3363159","article-title":"Feature Dimensionality Reduction with L2,p-Norm-Based Robust Embedding Regression for Classification of Hyperspectral Images","volume":"62","author":"Deng","year":"2024","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_21","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_22","doi-asserted-by":"crossref","first-page":"4851","DOI":"10.1109\/TAI.2024.3404910","article-title":"Transformer-based generative adversarial networks in computer vision: A comprehensive survey","volume":"5","author":"Dubey","year":"2024","journal-title":"IEEE Trans. Artif. Intell."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"22019","DOI":"10.1038\/s41598-023-49239-2","article-title":"Semisupervised hyperspectral image classification based on generative adversarial networks and spectral angle distance","volume":"13","author":"Zhan","year":"2023","journal-title":"Sci. Rep."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"110168","DOI":"10.1016\/j.foodcont.2023.110168","article-title":"An improved DCGAN model: Data augmentation of hyperspectral image for identification pesticide residues of Hami melon","volume":"157","author":"Tan","year":"2024","journal-title":"Food Control"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"5509617","DOI":"10.1109\/TGRS.2024.3367765","article-title":"Class-aligned and class-balancing generative domain adaptation for hyperspectral image classification","volume":"62","author":"Feng","year":"2024","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"2501","DOI":"10.1007\/s11042-023-15444-4","article-title":"Deep Siamese network with handcrafted feature extraction for hyperspectral image classification","volume":"83","author":"Ranjan","year":"2024","journal-title":"Multimed. Tools Appl."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"5204","DOI":"10.1080\/01431161.2022.2130727","article-title":"Xcep-Dense: A novel lightweight extreme inception model for hyperspectral image classification","volume":"43","author":"Ranjan","year":"2022","journal-title":"Int. J. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1991","DOI":"10.1007\/s12524-023-01734-9","article-title":"A Cross-Domain Semi-Supervised Zero-Shot Learning Model for the Classification of Hyperspectral Images","volume":"51","author":"Ranjan","year":"2023","journal-title":"J. Indian. Soc. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"100062","DOI":"10.1016\/j.ophoto.2024.100062","article-title":"Deep learning techniques for hyperspectral image analysis in agriculture: A review","volume":"3","author":"Guerri","year":"2024","journal-title":"ISPRS Open J. Photogramm. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"345","DOI":"10.1080\/10095020.2024.2332638","article-title":"Land use\/land cover (LULC) classification using hyperspectral images: A review","volume":"28","author":"Lou","year":"2024","journal-title":"Geo-Spatial Inf. Sci."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"108577","DOI":"10.1016\/j.compag.2023.108577","article-title":"A research review on deep learning combined with hyperspectral Imaging in multiscale agricultural sensing","volume":"217","author":"Shuai","year":"2024","journal-title":"Comput. Electron. Agric."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"179912","DOI":"10.1109\/ACCESS.2024.3505989","article-title":"Generative Adversarial Networks (GANs) for Image Augmentation in Farming: A Review","volume":"12","author":"Asaari","year":"2024","journal-title":"IEEE Access"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s12559-024-10291-3","article-title":"Advancing Medical Imaging Through Generative Adversarial Networks: A Comprehensive Review and Future Prospects","volume":"16","author":"Mamo","year":"2024","journal-title":"Cognit. Comput."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Wang, X., Sun, L., Chehri, A., and Song, Y. (2023). A review of GAN-based super-resolution reconstruction for optical remote sensing images. Remote Sens., 15.","DOI":"10.3390\/rs15205062"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1975","DOI":"10.1007\/s12145-023-01040-5","article-title":"A comprehensive review: Active learning for hyperspectral image classifications","volume":"16","author":"Patel","year":"2023","journal-title":"Earth Sci. Inform."},{"key":"ref_36","first-page":"102734","article-title":"A review and meta-analysis of generative adversarial networks and their applications in remote sensing","volume":"108","author":"Jozdani","year":"2022","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"153822","DOI":"10.1109\/ACCESS.2024.3482280","article-title":"Efficient Mapping of Tissue Oxygen Saturation using Hyperspectral Imaging and GAN","volume":"12","author":"Chang","year":"2024","journal-title":"IEEE Access"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"4064","DOI":"10.1109\/TMI.2024.3412033","article-title":"R2D2-GAN: Robust Dual Discriminator Generative Adversarial Network for Microscopy Hyperspectral Image Super-Resolution","volume":"43","author":"Liu","year":"2024","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"125086","DOI":"10.1016\/j.saa.2024.125086","article-title":"An innovative variant based on generative adversarial network (GAN): Regression GAN combined with hyperspectral imaging to predict pesticide residue content of Hami melon","volume":"325","author":"Tan","year":"2025","journal-title":"Spectrochim. Acta Part. A Mol. Biomol. Spectrosc."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"3683","DOI":"10.1109\/TCE.2024.3470846","article-title":"AGANet: Attention-Guided Generative Adversarial Network for Corn Hyperspectral Images Augmentation","volume":"71","author":"Zhang","year":"2024","journal-title":"IEEE Trans. Consum. Electron."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"4454","DOI":"10.3390\/rs13214454","article-title":"Hyperspectral target detection with an auxiliary generative adversarial network","volume":"13","author":"Gao","year":"2021","journal-title":"Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"108473","DOI":"10.1016\/j.compag.2023.108473","article-title":"SAM-GAN: An improved DCGAN for rice seed viability determination using near-infrared hyperspectral imaging","volume":"216","author":"Qi","year":"2024","journal-title":"Comput. Electron. Agric."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"116585","DOI":"10.1016\/j.lwt.2024.116585","article-title":"Utilizing wasserstein generative adversarial networks for enhanced hyperspectral imaging: A novel approach to predict soluble sugar content in cherry tomatoes","volume":"206","author":"Cui","year":"2024","journal-title":"LWT"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"120722","DOI":"10.1016\/j.saa.2021.120722","article-title":"Discrimination of unsound wheat kernels based on deep convolutional generative adversarial network and near-infrared hyperspectral imaging technology","volume":"268","author":"Li","year":"2022","journal-title":"Spectrochim. Acta Part. A Mol. Biomol. Spectrosc."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"8863","DOI":"10.1109\/JSTARS.2024.3389641","article-title":"Decentralized Federated GAN for Hyperspectral Change Detection in Edge Computing","volume":"17","author":"Xie","year":"2024","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"5734","DOI":"10.1109\/JSTARS.2024.3368286","article-title":"Pixel-to-Abundance Translation: Conditional Generative Adversarial Networks Based on Patch Transformer for Hyperspectral Unmixing","volume":"17","author":"Wang","year":"2024","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_47","first-page":"5528215","article-title":"Physics-informed hyperspectral remote sensing image synthesis with deep conditional generative adversarial networks","volume":"60","author":"Liu","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"174","DOI":"10.3390\/photonics11020174","article-title":"Single-Pixel Infrared Hyperspectral Imaging via Physics-Guided Generative Adversarial Networks","volume":"11","author":"Wang","year":"2024","journal-title":"Photonics"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"112885","DOI":"10.1016\/j.rse.2021.112885","article-title":"Aboveground biomass of salt-marsh vegetation in coastal wetlands: Sample expansion of in situ hyperspectral and Sentinel-2 data using a generative adversarial network","volume":"270","author":"Chen","year":"2022","journal-title":"Remote Sens. Environ."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"505","DOI":"10.1109\/TCI.2021.3079818","article-title":"SHS-GAN: Synthetic enhancement of a natural hyperspectral database","volume":"7","author":"Hauser","year":"2021","journal-title":"IEEE Trans. Comput. Imaging"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"4961","DOI":"10.1080\/01431161.2024.2370500","article-title":"Transformer-inspired stacked-GAN for hyperspectral target detection","volume":"45","author":"Li","year":"2024","journal-title":"Int. J. Remote Sens."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"122058","DOI":"10.1016\/j.eswa.2023.122058","article-title":"Dual-discriminator conditional generative adversarial network optimized with hybrid manta ray foraging optimization and volcano eruption algorithm for hyperspectral anomaly detection","volume":"238","author":"Shanmugam","year":"2024","journal-title":"Expert. Syst. Appl."},{"key":"ref_53","first-page":"5503115","article-title":"ACTN: Adaptive coupling transformer network for hyperspectral image classification","volume":"63","author":"Yang","year":"2025","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"7064","DOI":"10.1109\/JSTARS.2025.3542228","article-title":"Pixel-Level and Global Similarity-Based Adversarial Autoencoder Network for Hyperspectral Unmixing","volume":"18","author":"Tao","year":"2025","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"352","DOI":"10.1007\/s12145-025-01779-z","article-title":"DSSFT: Dual branch spectral-spatial feature fusion transformer network for hyperspectral image unmixing","volume":"18","author":"Hadi","year":"2025","journal-title":"Earth Sci. Inform."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"5506005","DOI":"10.1109\/LGRS.2024.3402256","article-title":"Generative Adversarial Autoencoder Network for Anti-Shadow Hyperspectral Unmixing","volume":"21","author":"Sun","year":"2024","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"1695","DOI":"10.1007\/s11760-021-01902-9","article-title":"Degradation learning for unsupervised hyperspectral image super-resolution based on generative adversarial network","volume":"15","author":"Zhang","year":"2021","journal-title":"Signal Image Video Process."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"109701","DOI":"10.1016\/j.patcog.2023.109701","article-title":"Features kept generative adversarial network data augmentation strategy for hyperspectral image classification","volume":"142","author":"Zhang","year":"2023","journal-title":"Pattern Recognit."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"17137","DOI":"10.1109\/TNNLS.2023.3300099","article-title":"HSGAN: Hyperspectral reconstruction from RGB images with generative adversarial network","volume":"34","author":"Zhao","year":"2024","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"3316","DOI":"10.3390\/rs13163316","article-title":"Self-attention-based conditional variational auto-encoder generative adversarial networks for hyperspectral classification","volume":"13","author":"Chen","year":"2021","journal-title":"Remote Sens."},{"key":"ref_61","first-page":"5502005","article-title":"Mixture of spectral generative adversarial networks for imbalanced hyperspectral image classification","volume":"19","author":"Dam","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"5526217","DOI":"10.1109\/TGRS.2023.3320100","article-title":"Cross-scene classification of hyperspectral images via generative adversarial network in latent space","volume":"61","author":"Yang","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_63","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_64","doi-asserted-by":"crossref","first-page":"5520018","DOI":"10.1109\/TGRS.2023.3304836","article-title":"Multi-complementary generative adversarial networks with contrastive learning for hyperspectral image classification","volume":"61","author":"Feng","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Hennessy, A., Clarke, K., and Lewis, M. (2021). Generative adversarial network synthesis of hyperspectral vegetation data. Remote Sens., 13.","DOI":"10.3390\/rs13122243"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"538","DOI":"10.1364\/JOSAA.478585","article-title":"Hybrid spatial-spectral generative adversarial network for hyperspectral image classification","volume":"40","author":"Ma","year":"2023","journal-title":"JOSA A"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"5536517","DOI":"10.1109\/TGRS.2022.3202908","article-title":"Self-supervised divide-and-conquer generative adversarial network for classification of hyperspectral images","volume":"60","author":"Feng","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"6017","DOI":"10.1109\/TGRS.2020.3013022","article-title":"Characterization of background-anomaly separability with generative adversarial network for hyperspectral anomaly detection","volume":"59","author":"Zhong","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_69","first-page":"5508413","article-title":"Fusion of hyperspectral and panchromatic images using generative adversarial network and image segmentation","volume":"60","author":"Dong","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"1791","DOI":"10.1109\/LGRS.2020.3009017","article-title":"An optimized training method for GAN-based hyperspectral image classification","volume":"18","author":"Zhang","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Su, L., Sui, Y., and Yuan, Y. (2023). An unmixing-based multi-attention GAN for unsupervised hyperspectral and multispectral image fusion. Remote Sens., 15.","DOI":"10.3390\/rs15040936"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"5040","DOI":"10.1109\/TGRS.2020.3015843","article-title":"Adaptive DropBlock-enhanced generative adversarial networks for hyperspectral image classification","volume":"59","author":"Wang","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"5510205","DOI":"10.1109\/LGRS.2023.3322139","article-title":"Generative adversarial network with transformer for hyperspectral image classification","volume":"20","author":"Hao","year":"2023","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"104346","DOI":"10.1016\/j.jfca.2021.104346","article-title":"Hyperspectral imaging combined with generative adversarial network (GAN)-based data augmentation to identify haploid maize kernels","volume":"106","author":"Zhang","year":"2022","journal-title":"J. Food Compos. Anal."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"375","DOI":"10.1016\/j.neunet.2021.05.029","article-title":"Self-spectral learning with GAN based spectral\u2013spatial target detection for hyperspectral image","volume":"142","author":"Xie","year":"2021","journal-title":"Neural Netw."},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Xu, T., Han, B., Li, J., and Du, Y. (2023). Domain-invariant feature and generative adversarial network boundary enhancement for multi-source unsupervised hyperspectral image classification. Remote Sens., 15.","DOI":"10.3390\/rs15225306"},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"131047","DOI":"10.1016\/j.foodchem.2021.131047","article-title":"Near-infrared hyperspectral imaging technology combined with deep convolutional generative adversarial network to predict oil content of single maize kernel","volume":"370","author":"Zhang","year":"2022","journal-title":"Food Chem."},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Wang, B., Zhang, Y., Feng, Y., Xie, B., and Mei, S. (2023). Attention-enhanced generative adversarial network for hyperspectral imagery spatial super-resolution. Remote Sens., 15.","DOI":"10.3390\/rs15143644"},{"key":"ref_79","doi-asserted-by":"crossref","unstructured":"Bai, J., Lu, J., Xiao, Z., Chen, Z., and Jiao, L. (2022). Generative adversarial networks based on transformer encoder and convolution block for hyperspectral image classification. Remote Sens., 14.","DOI":"10.3390\/rs14143426"},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"109795","DOI":"10.1016\/j.patcog.2023.109795","article-title":"Hyperspectral anomaly detection based on variational background inference and generative adversarial network","volume":"143","author":"Wang","year":"2023","journal-title":"Pattern Recognit."},{"key":"ref_81","first-page":"5512811","article-title":"Sparse coding-inspired GAN for hyperspectral anomaly detection in weakly supervised learning","volume":"60","author":"Li","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_82","doi-asserted-by":"crossref","unstructured":"Li, Z., Zhu, X., Xin, Z., Guo, F., Cui, X., and Wang, L. (2021). Variational generative adversarial network with crossed spatial and spectral interactions for hyperspectral image classification. Remote Sens., 13.","DOI":"10.3390\/rs13163131"},{"key":"ref_83","first-page":"5506010","article-title":"Convolutional two-stream generative adversarial network-based hyperspectral feature extraction","volume":"60","author":"Yu","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_84","doi-asserted-by":"crossref","unstructured":"Li, Z., Shi, S., Wang, L., Xu, M., and Li, L. (2022). Unsupervised generative adversarial network with background enhancement and irredundant pooling for hyperspectral anomaly detection. Remote Sens., 14.","DOI":"10.3390\/rs14051265"},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"5518516","DOI":"10.1109\/TGRS.2024.3402058","article-title":"MFT-GAN: A Multiscale Feature-guided Transformer Network for Unsupervised Hyperspectral Pansharpening","volume":"62","author":"Shang","year":"2024","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"5543614","DOI":"10.1109\/TGRS.2022.3210280","article-title":"Immune evolutionary generative adversarial networks for hyperspectral image classification","volume":"60","author":"Bai","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"948","DOI":"10.1109\/TCI.2021.3110103","article-title":"Hyperspectral imagery spatial super-resolution using generative adversarial network","volume":"7","author":"Wang","year":"2021","journal-title":"IEEE Trans. Comput. Imaging"},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"26351","DOI":"10.1007\/s11042-024-19969-0","article-title":"A GAN based method for cross-scene classification of hyperspectral scenes captured by different sensors","volume":"84","author":"Mahmoudi","year":"2024","journal-title":"Multimedia Tools Appl."},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"5511416","DOI":"10.1109\/TGRS.2023.3274778","article-title":"Distance constraint-based generative adversarial networks for hyperspectral image classification","volume":"61","author":"Qin","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"6504","DOI":"10.1109\/TNNLS.2021.3082158","article-title":"Weakly supervised discriminative learning with spectral constrained generative adversarial network for hyperspectral anomaly detection","volume":"33","author":"Jiang","year":"2021","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"523","DOI":"10.1109\/LGRS.2020.2976482","article-title":"Generative adversarial capsule network with ConvLSTM for hyperspectral image classification","volume":"18","author":"Wang","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"1258","DOI":"10.1049\/cit2.12154","article-title":"Frequency-to-spectrum mapping GAN for semisupervised hyperspectral anomaly detection","volume":"8","author":"Wang","year":"2023","journal-title":"CAAI Trans. Intell. Technol."},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"463","DOI":"10.1109\/TGRS.2020.2994238","article-title":"HPGAN: Hyperspectral pansharpening using 3-D generative adversarial networks","volume":"59","author":"Xie","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1007\/s11694-023-02145-7","article-title":"Discrimination of maturity of Camellia oleifera fruit on-site based on generative adversarial network and hyperspectral imaging technique","volume":"18","author":"Sun","year":"2024","journal-title":"J. Food Meas. Charact."},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"106017","DOI":"10.1016\/j.engappai.2023.106017","article-title":"MSRA-G: Combination of multi-scale residual attention network and generative adversarial networks for hyperspectral image classification","volume":"121","author":"Zhao","year":"2023","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"6009805","DOI":"10.1109\/LGRS.2022.3175836","article-title":"3-D auxiliary classifier gan for hyperspectral anomaly detection via weakly supervised learning","volume":"19","author":"Luo","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_97","doi-asserted-by":"crossref","unstructured":"Liang, H., Bao, W., and Shen, X. (2021). Adaptive weighting feature fusion approach based on generative adversarial network for hyperspectral image classification. Remote Sens., 13.","DOI":"10.3390\/rs13020198"},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"5539016","DOI":"10.1109\/TGRS.2024.3490537","article-title":"Few-shot hyperspectral image classification using relational generative adversarial network","volume":"62","author":"Wei","year":"2024","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"5452","DOI":"10.1080\/01431161.2022.2135412","article-title":"Dual hybrid convolutional generative adversarial network for hyperspectral image classification","volume":"43","author":"Shi","year":"2022","journal-title":"Int. J. Remote Sens."},{"key":"ref_100","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_101","doi-asserted-by":"crossref","first-page":"109202","DOI":"10.1016\/j.sigpro.2023.109202","article-title":"Hyperspectral remote sensing image classification based on residual generative adversarial neural networks","volume":"213","author":"Feng","year":"2023","journal-title":"Signal Process."},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"5224","DOI":"10.1109\/TGRS.2020.2975295","article-title":"Semisupervised spectral learning with generative adversarial network for hyperspectral anomaly detection","volume":"58","author":"Jiang","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"5533213","DOI":"10.1109\/TGRS.2024.3468311","article-title":"Domain-adversarial generative and dual feature representation discriminative network for hyperspectral image domain generalization","volume":"62","author":"Chu","year":"2024","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_104","doi-asserted-by":"crossref","first-page":"5534819","DOI":"10.1109\/TGRS.2022.3193441","article-title":"A 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_105","doi-asserted-by":"crossref","unstructured":"Tang, R., Liu, H., and Wei, J. (2020). Visualizing near infrared hyperspectral images with generative adversarial networks. Remote Sens., 12.","DOI":"10.3390\/rs12233848"},{"key":"ref_106","doi-asserted-by":"crossref","unstructured":"Xu, T., Wang, Y., Li, J., and Du, Y. (2024). Generative adversarial network and mutual-point learning algorithm for few-shot open-set classification of hyperspectral images. Remote Sens., 16.","DOI":"10.3390\/rs16071285"},{"key":"ref_107","first-page":"5521812","article-title":"Rank-aware generative adversarial network for hyperspectral band selection","volume":"60","author":"Zhang","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_108","doi-asserted-by":"crossref","first-page":"4666","DOI":"10.1109\/TGRS.2020.2965961","article-title":"Discriminative reconstruction constrained generative adversarial network for hyperspectral anomaly detection","volume":"58","author":"Jiang","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_109","doi-asserted-by":"crossref","first-page":"1742","DOI":"10.1038\/s41598-018-20142-5","article-title":"Multi-wavelength emission from a single InGaN\/GaN nanorod analyzed by cathodoluminescence hyperspectral imaging","volume":"8","author":"Kusch","year":"2018","journal-title":"Sci. Rep."},{"key":"ref_110","doi-asserted-by":"crossref","first-page":"2669","DOI":"10.1109\/TGRS.2018.2876123","article-title":"Unsupervised feature extraction in hyperspectral images based on Wasserstein generative adversarial network","volume":"57","author":"Zhang","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_111","doi-asserted-by":"crossref","first-page":"812","DOI":"10.1109\/LED.2020.2989919","article-title":"Time resolved hyperspectral quantum rod thermography of microelectronic devices: Temperature transients in a GaN HEMT","volume":"41","author":"Pomeroy","year":"2020","journal-title":"IEEE Electron. Device Lett."},{"key":"ref_112","doi-asserted-by":"crossref","first-page":"212","DOI":"10.1109\/LGRS.2017.2780890","article-title":"Semisupervised hyperspectral image classification based on generative adversarial networks","volume":"15","author":"Zhan","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_113","doi-asserted-by":"crossref","first-page":"4377","DOI":"10.1038\/s41598-019-40066-y","article-title":"Early detection of tomato spotted wilt virus by hyperspectral imaging and outlier removal auxiliary classifier generative adversarial nets (OR-AC-GAN)","volume":"9","author":"Wang","year":"2019","journal-title":"Sci. Rep."},{"key":"ref_114","doi-asserted-by":"crossref","first-page":"5424","DOI":"10.1109\/JSTARS.2020.3022781","article-title":"Structure aware generative adversarial networks for hyperspectral image classification","volume":"13","author":"Arefi","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_115","doi-asserted-by":"crossref","first-page":"987","DOI":"10.1007\/s11548-019-01940-2","article-title":"Estimation of tissue oxygen saturation from RGB images and sparse hyperspectral signals based on conditional generative adversarial network","volume":"14","author":"Li","year":"2019","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"key":"ref_116","doi-asserted-by":"crossref","unstructured":"He, Z., Liu, H., Wang, Y., and Hu, J. (2017). Generative adversarial networks-based semi-supervised learning for hyperspectral image classification. Remote Sens., 9.","DOI":"10.3390\/rs9101042"},{"key":"ref_117","doi-asserted-by":"crossref","unstructured":"Gao, H., Yao, D., Wang, M., Li, C., Liu, H., Hua, Z., and Wang, J. (2019). A hyperspectral image classification method based on multi-discriminator generative adversarial networks. Sensors, 19.","DOI":"10.3390\/s19153269"},{"key":"ref_118","doi-asserted-by":"crossref","first-page":"914","DOI":"10.1109\/JSTARS.2020.2974577","article-title":"Semisupervised variational generative adversarial networks for hyperspectral image classification","volume":"13","author":"Tao","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_119","doi-asserted-by":"crossref","first-page":"7232","DOI":"10.1109\/TGRS.2019.2912468","article-title":"Caps-TripleGAN: GAN-assisted CapsNet for hyperspectral image classification","volume":"57","author":"Wang","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_120","doi-asserted-by":"crossref","unstructured":"Feng, J., Feng, X., Chen, J., Cao, X., Zhang, X., Jiao, L., and Yu, T. (2020). Generative adversarial networks based on collaborative learning and attention mechanism for hyperspectral image classification. Remote Sens., 12.","DOI":"10.3390\/rs12071149"},{"key":"ref_121","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_122","doi-asserted-by":"crossref","unstructured":"Zhao, W., Chen, X., Chen, J., and Qu, Y. (2020). Sample generation with self-attention generative adversarial adaptation network (SaGAAN) for hyperspectral image classification. Remote Sens., 12.","DOI":"10.3390\/rs12050843"},{"key":"ref_123","doi-asserted-by":"crossref","first-page":"983","DOI":"10.1080\/2150704X.2020.1804641","article-title":"A mixture generative adversarial network with category multi-classifier for hyperspectral image classification","volume":"11","author":"Li","year":"2020","journal-title":"Remote Sens. Lett."},{"key":"ref_124","doi-asserted-by":"crossref","first-page":"108842","DOI":"10.1016\/j.compag.2024.108842","article-title":"Development of a new hyperspectral imaging technology with autoencoder-assisted generative adversarial network for predicting the content of polyunsaturated fatty acids in red meat","volume":"220","author":"Cui","year":"2024","journal-title":"Comput. Electron. Agric."},{"key":"ref_125","doi-asserted-by":"crossref","first-page":"4640","DOI":"10.1109\/TIP.2024.3444323","article-title":"Fast projected fuzzy clustering with anchor guidance for multimodal remote sensing imagery","volume":"33","author":"Zhang","year":"2024","journal-title":"IEEE Trans. Image Process."},{"key":"ref_126","first-page":"5521713","article-title":"Dual graph learning affinity propagation for multimodal remote sensing image clustering","volume":"62","author":"Zhang","year":"2024","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_127","first-page":"5513412","article-title":"LRR-Net: An interpretable deep unfolding network for hyperspectral anomaly detection","volume":"61","author":"Li","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_128","first-page":"5516417","article-title":"A semisupervised Siamese network for hyperspectral image classification","volume":"60","author":"Jia","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_129","doi-asserted-by":"crossref","first-page":"4555","DOI":"10.1109\/TNNLS.2021.3114203","article-title":"Adversarial autoencoder network for hyperspectral unmixing","volume":"34","author":"Jin","year":"2021","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_130","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/LGRS.2025.3529884","article-title":"Cross-Domain Few-Shot Learning for Hyperspectral Image Classification Based on Global-to-Local Enhanced Channel Attention","volume":"22","author":"Dang","year":"2025","journal-title":"IEEE Geosci. Remote Sensing Lett."},{"key":"ref_131","first-page":"5453","article-title":"Regulating, L. HyRANK: A benchmark for hyperspectral domain adaptation","volume":"58","author":"Fletcher","year":"2020","journal-title":"IEEE TGRS"},{"key":"ref_132","doi-asserted-by":"crossref","first-page":"2241","DOI":"10.1109\/TIP.2010.2046811","article-title":"Generalized Assorted Pixel Camera: Postcapture Control of Resolution, Dynamic Range, and Spectrum","volume":"19","author":"Yasuma","year":"2010","journal-title":"IEEE Trans. Image Process."},{"key":"ref_133","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_134","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_135","doi-asserted-by":"crossref","unstructured":"Aasen, H., Honkavaara, E., Lucieer, A., and Zarco-Tejada, P.J. (2018). Quantitative remote sensing at ultra-high resolution with UAV spectroscopy: A review of sensor technology, measurement procedures, and data correction workflows. Remote Sens., 10.","DOI":"10.3390\/rs10071091"},{"key":"ref_136","doi-asserted-by":"crossref","first-page":"1605","DOI":"10.1109\/LSP.2024.3411516","article-title":"Advance One-Shot Multispectral Instance Detection With Text\u2019s Supervision","volume":"31","author":"Feng","year":"2024","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_137","doi-asserted-by":"crossref","first-page":"010901","DOI":"10.1117\/1.JBO.19.1.010901","article-title":"Medical hyperspectral imaging: A review","volume":"19","author":"Lu","year":"2014","journal-title":"J. Biomed. Opt."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/18\/2\/196\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,7]],"date-time":"2026-01-07T08:32:30Z","timestamp":1767774750000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/18\/2\/196"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,6]]},"references-count":137,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2026,1]]}},"alternative-id":["rs18020196"],"URL":"https:\/\/doi.org\/10.3390\/rs18020196","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,6]]}}}