{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T18:09:58Z","timestamp":1775326198719,"version":"3.50.1"},"reference-count":59,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"name":"National Scholarship for Building High Level Universities"},{"DOI":"10.13039\/501100004543","name":"China Scholarship Council","doi-asserted-by":"publisher","award":["201906700002"],"award-info":[{"award-number":["201906700002"]}],"id":[{"id":"10.13039\/501100004543","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U1813222"],"award-info":[{"award-number":["U1813222"]}],"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":["42075129"],"award-info":[{"award-number":["42075129"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Hebei Province Natural Science Foundation","award":["E2021202179"],"award-info":[{"award-number":["E2021202179"]}]},{"name":"Key Research and Development Project from Hebei Province","award":["19210404D"],"award-info":[{"award-number":["19210404D"]}]},{"name":"Key Research and Development Project from Hebei Province","award":["20351802D"],"award-info":[{"award-number":["20351802D"]}]},{"name":"Key Research and Development Project from Hebei Province","award":["21351803D"],"award-info":[{"award-number":["21351803D"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing"],"published-print":{"date-parts":[[2022]]},"DOI":"10.1109\/jstars.2022.3192127","type":"journal-article","created":{"date-parts":[[2022,7,18]],"date-time":"2022-07-18T20:32:41Z","timestamp":1658176361000},"page":"6053-6068","source":"Crossref","is-referenced-by-count":47,"title":["HyperViTGAN: Semisupervised Generative Adversarial Network With Transformer for Hyperspectral Image Classification"],"prefix":"10.1109","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4902-5690","authenticated-orcid":false,"given":"Ziping","family":"He","sequence":"first","affiliation":[{"name":"School of Electronics and Information Engineering, Hebei University of Technology, Tianjin, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3968-481X","authenticated-orcid":false,"given":"Kewen","family":"Xia","sequence":"additional","affiliation":[{"name":"School of Electronics and Information Engineering, Hebei University of Technology, Tianjin, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1203-741X","authenticated-orcid":false,"given":"Pedram","family":"Ghamisi","sequence":"additional","affiliation":[{"name":"Helmholtz-Zentrum Dresden-Rossendorf, Helmholtz Institute Freiberg for Resource Technology, Freiberg, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3427-0677","authenticated-orcid":false,"given":"Yuhen","family":"Hu","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, University of Wisconsin-Madison, Madison, WI, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0091-4182","authenticated-orcid":false,"given":"Shurui","family":"Fan","sequence":"additional","affiliation":[{"name":"School of Electronics and Information Engineering, Hebei University of Technology, Tianjin, China"}]},{"given":"Baokai","family":"Zu","sequence":"additional","affiliation":[{"name":"Faculty of Information Technology, Beijing University of Technology, Beijing, China"}]}],"member":"263","reference":[{"key":"ref39","first-page":"14745","article-title":"TransGAN: Two pure transformers can make one strong GAN, and that can scale up","volume":"34","author":"jiang","year":"2021","journal-title":"Adv Neural Inf Process Syst"},{"key":"ref38","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2022.3172371","article-title":"Spectralformer: Rethinking hyperspectral image classification with transformers","volume":"60","author":"hong","year":"2021","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2021.3052048"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2020.3015843"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/LGRS.2020.2976482"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2019.2912468"},{"key":"ref37","article-title":"An image is worth 16x16 words: Transformers for image recognition at scale","author":"kolesnikov","year":"0","journal-title":"Proc Int Conf Lear Representations"},{"key":"ref36","first-page":"5999","article-title":"Attention is all you need","volume":"30","author":"vaswani","year":"2017","journal-title":"Adv Neural Inf Process Syst"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/W14-4012"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"ref28","first-page":"2672","article-title":"Generative adversarial nets","volume":"3","author":"goodfellow","year":"2014","journal-title":"Adv Neural Inf Process Syst"},{"key":"ref27","first-page":"1929","article-title":"Dropout: A simple way to prevent neural networks from overfitting","volume":"15","author":"srivastava","year":"2014","journal-title":"J Mach Learn Res"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2018.2805286"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/MGRS.2018.2854840"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2012.2197589"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2020.3043267"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2020.3015157"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2019.2899129"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2017.2755542"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2017.2725580"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/MGRS.2020.2979764"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.1968.1054102"},{"key":"ref50","first-page":"4171","article-title":"Bert: Pre-training of deep bidirectional transformers for language understanding","author":"kenton","year":"0","journal-title":"Proc NAACL-HLT"},{"key":"ref51","article-title":"Mixup: Beyond empirical risk minimization","author":"zhang","year":"0","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref59","article-title":"Adam: A method for stochastic optimization","author":"kingma","year":"0","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref58","article-title":"Unsupervised representation learning with deep convolutional generative adversarial networks","author":"radford","year":"0","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2016.09.010"},{"key":"ref56","first-page":"3371","article-title":"Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion","volume":"11","author":"vincent","year":"2010","journal-title":"J Mach Learn Res"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1002\/wics.101"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1109\/JSTARS.2014.2305441"},{"key":"ref53","article-title":"Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks","volume":"3","author":"lee","year":"0","journal-title":"Proc Workshop Challenges Representation Learn"},{"key":"ref52","first-page":"15797","article-title":"EC-GAN: Low-sample classification using semi-supervised algorithms and GANs","volume":"35","author":"haque","year":"0","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/JSTARS.2019.2926123"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/IGARSS47720.2021.9554113"},{"key":"ref40","article-title":"ViTGAN: Training GANs with vision transformers","author":"lee","year":"0","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.3390\/rs10081271"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.3390\/rs13020193"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1080\/01431161.2022.2048916"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2020.3037249"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2016.2636241"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2021.3116138"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/JSTARS.2019.2892975"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2020.3045273"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1117\/12.339824"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/MGRS.2016.2616418"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2004.842481"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2004.831865"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2011.2162649"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2010.2055876"},{"key":"ref49","first-page":"2642","article-title":"Conditional image synthesis with auxiliary classifier GANs","author":"odena","year":"0","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1080\/014311699213622"},{"key":"ref46","article-title":"Conditional generative adversarial nets","author":"mirza","year":"2014"},{"key":"ref45","first-page":"214","article-title":"Wasserstein generative adversarial networks","author":"arjovsky","year":"0","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref48","first-page":"2234","article-title":"Improved techniques for training GANs","volume":"29","author":"salimans","year":"2016","journal-title":"Adv Neural Inf Process Syst"},{"key":"ref47","first-page":"2234","article-title":"Semi-supervised learning with generative adversarial networks","author":"odena","year":"0","journal-title":"Proc Data Efficient Mach Learn workshop ICML"},{"key":"ref42","first-page":"271","article-title":"f-GAN: Training generative neural samplers using variational divergence minimization","volume":"29","author":"nowozin","year":"2016","journal-title":"Adv Neural Inf Process Syst"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00813"},{"key":"ref44","first-page":"9078","article-title":"Bridging the gap between f-GANs and wasserstein GANs","author":"song","year":"0","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.2307\/1428011"}],"container-title":["IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/4609443\/9656571\/09832721.pdf?arnumber=9832721","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,2,11]],"date-time":"2023-02-11T22:27:56Z","timestamp":1676154476000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9832721\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"references-count":59,"URL":"https:\/\/doi.org\/10.1109\/jstars.2022.3192127","relation":{},"ISSN":["1939-1404","2151-1535"],"issn-type":[{"value":"1939-1404","type":"print"},{"value":"2151-1535","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]}}}