{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T17:01:38Z","timestamp":1782493298569,"version":"3.54.5"},"reference-count":25,"publisher":"Springer Science and Business Media LLC","issue":"16","license":[{"start":{"date-parts":[[2025,11,17]],"date-time":"2025-11-17T00:00:00Z","timestamp":1763337600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,11,17]],"date-time":"2025-11-17T00:00:00Z","timestamp":1763337600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100009988","name":"Huzhou Municipal Science and Technology Bureau","doi-asserted-by":"publisher","award":["2023YZ55"],"award-info":[{"award-number":["2023YZ55"]}],"id":[{"id":"10.13039\/501100009988","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62277016"],"award-info":[{"award-number":["62277016"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["SIViP"],"published-print":{"date-parts":[[2025,12]]},"DOI":"10.1007\/s11760-025-04959-y","type":"journal-article","created":{"date-parts":[[2025,11,17]],"date-time":"2025-11-17T14:55:30Z","timestamp":1763391330000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Semi-supervised deep residual generative adversarial network for hyperspectral image classification"],"prefix":"10.1007","volume":"19","author":[{"given":"Hui","family":"Zhang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yonghui","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huanhuan","family":"Lv","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ruiqin","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,11,17]]},"reference":[{"issue":"16","key":"4959_CR1","doi-asserted-by":"publisher","first-page":"3969","DOI":"10.3390\/rs15163969","volume":"15","author":"W Ding","year":"2023","unstructured":"Ding, W., Ding, L., Li, Q., et al.: Lithium-rich pegmatite detection integrating high-resolution and hyperspectral satellite data in Zhawulong Area, Western Sichuan, China. Remote Sens. 15(16), 3969 (2023)","journal-title":"Remote Sens."},{"key":"4959_CR2","doi-asserted-by":"publisher","first-page":"3972","DOI":"10.1109\/JSTARS.2022.3174412","volume":"15","author":"J Yuan","year":"2022","unstructured":"Yuan, J., Wang, S., Wu, C., et al.: Fine-grained classification of urban functional zones and landscape pattern analysis using hyperspectral satellite imagery: A case study of Wuhan. IEEE J. Sel. Top. Appl. Earth Obs Remote Sens. 15, 3972\u20133991 (2022)","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs Remote Sens."},{"key":"4959_CR3","doi-asserted-by":"crossref","unstructured":"Sethy, P.K., Pandey, C., Sahu, Y.K., et al.: Hyperspectral imagery applications for precision agriculture-a systemic survey. Multimedia Tools Appl. 81, 1\u201334 (2022)","DOI":"10.1007\/s11042-021-11729-8"},{"issue":"7","key":"4959_CR4","doi-asserted-by":"publisher","first-page":"518","DOI":"10.1007\/s10661-022-10125-5","volume":"194","author":"HW Chen","year":"2022","unstructured":"Chen, H.W., Chen, C.-Y., Nguyen, K.L.P., et al.: Hyperspectral sensing of heavy metals in soil by integrating AI and UAV technology. Environ. Monit. Assess. 194(7), 518 (2022)","journal-title":"Environ. Monit. Assess."},{"issue":"17","key":"4959_CR5","doi-asserted-by":"publisher","first-page":"6221","DOI":"10.1080\/01431161.2022.2133579","volume":"43","author":"P Ranjan","year":"2022","unstructured":"Ranjan, P., Girdhar, A.: A comprehensive systematic review of deep learning methods for hyperspectral images classification. Int. J. Remote Sens. 43(17), 6221\u20136306 (2022)","journal-title":"Int. J. Remote Sens."},{"key":"4959_CR6","doi-asserted-by":"publisher","first-page":"2501","DOI":"10.1007\/s11042-023-15444-4","volume":"83","author":"P Ranjan","year":"2024","unstructured":"Ranjan, P., Girdhar, A.: Deep Siamese network with handcrafted feature extraction for hyperspectral image classification. Multimed Tools Appl. 83, 2501\u20132526 (2024)","journal-title":"Multimed Tools Appl."},{"issue":"9","key":"4959_CR7","doi-asserted-by":"publisher","first-page":"839","DOI":"10.1080\/2150704X.2017.1331053","volume":"8","author":"B Liu","year":"2017","unstructured":"Liu, B., Yu, X., Zhang, P., et al.: A semi-supervised convolutional neural network for hyperspectral image classification. Remote Sens. Lett. 8(9), 839\u2013848 (2017)","journal-title":"Remote Sens. Lett."},{"key":"4959_CR8","doi-asserted-by":"publisher","first-page":"594","DOI":"10.1016\/j.patrec.2020.08.020","volume":"138","author":"A Sellami","year":"2020","unstructured":"Sellami, A., Abbes, A.B., Barra, V., et al.: Fused 3-D spectral-spatial deep neural networks and spectral clustering for hyperspectral image classification. Pattern Recognit. Lett. 138, 594\u2013600 (2020)","journal-title":"Pattern Recognit. Lett."},{"key":"4959_CR9","doi-asserted-by":"publisher","first-page":"4311","DOI":"10.1109\/JSTARS.2020.3011992","volume":"13","author":"Z Lu","year":"2020","unstructured":"Lu, Z., Xu, B., Sun, L., et al.: 3-D channel and spatial attention based multiscale spatial\u2013spectral residual network for hyperspectral image classification. IEEE J. Sel. Top. Appl. Earth Obs Remote Sens. 13, 4311\u20134324 (2020)","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs Remote Sens."},{"issue":"14","key":"4959_CR10","doi-asserted-by":"publisher","first-page":"5204","DOI":"10.1080\/01431161.2022.2130727","volume":"43","author":"P Ranjan","year":"2022","unstructured":"Ranjan, P., Girdhar, A.: Xcep-Dense: A novel lightweight extreme inception model for hyperspectral image classification. Int. J. Remote Sens. 43(14), 5204\u20135230 (2022)","journal-title":"Int. J. Remote Sens."},{"issue":"18","key":"4959_CR11","doi-asserted-by":"publisher","first-page":"4471","DOI":"10.3390\/rs15184471","volume":"15","author":"L Fu","year":"2023","unstructured":"Fu, L., Chen, X., Pirasteh, S., et al.: The classification of hyperspectral images: A double-branch multi-scale residual network. Remote Sens. 15(18), 4471 (2023)","journal-title":"Remote Sens."},{"key":"4959_CR12","doi-asserted-by":"publisher","first-page":"8974","DOI":"10.1109\/JSTARS.2022.3213865","volume":"15","author":"Z Yang","year":"2022","unstructured":"Yang, Z., Xi, Z., Zhang, T., et al.: CMR-CNN: Cross-mixing residual network for hyperspectral image classification. IEEE J. Sel. Top. Appl. Earth Obs Remote Sens. 15, 8974\u20138989 (2022)","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs Remote Sens."},{"key":"4959_CR13","unstructured":"Lamprier, S., Scialom, T., Chaffin, A., et al.: Generative cooperative networks for natural language generation. In International Conference on Machine Learning, pp. 11891\u201311905. (2022)"},{"key":"4959_CR14","doi-asserted-by":"publisher","first-page":"105729","DOI":"10.1016\/j.compbiomed.2022.105729","volume":"147","author":"J Mao","year":"2022","unstructured":"Mao, J., Yin, X., Zhang, G., et al.: Pseudo-labeling generative adversarial networks for medical image classification. Comput. Biol. Med. 147, 105729 (2022)","journal-title":"Comput. Biol. Med."},{"key":"4959_CR15","doi-asserted-by":"publisher","first-page":"107982","DOI":"10.1016\/j.buildenv.2021.107982","volume":"201","author":"K Yan","year":"2021","unstructured":"Yan, K.: Chiller fault detection and diagnosis with anomaly detective generative adversarial network. Build. Environ. 201, 107982 (2021)","journal-title":"Build. Environ."},{"issue":"2","key":"4959_CR16","doi-asserted-by":"publisher","first-page":"212","DOI":"10.1109\/LGRS.2017.2780890","volume":"15","author":"Y Zhan","year":"2017","unstructured":"Zhan, Y., Hu, D., Wang, Y., et al.: Semisupervised hyperspectral image classification based on generative adversarial networks. IEEE Geosci. Remote Sens. Lett. 15(2), 212\u2013216 (2017)","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"issue":"9","key":"4959_CR17","doi-asserted-by":"publisher","first-page":"5046","DOI":"10.1109\/TGRS.2018.2805286","volume":"56","author":"L Zhu","year":"2018","unstructured":"Zhu, L., Chen, Y., Ghamisi, P., et al.: Generative adversarial networks for hyperspectral image classification. IEEE Trans. Geosci. Remote Sens. 56(9), 5046\u20135063 (2018)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"issue":"6","key":"4959_CR18","doi-asserted-by":"publisher","first-page":"5251","DOI":"10.1007\/s12145-024-01451-y","volume":"17","author":"P Ranjan","year":"2024","unstructured":"Ranjan, P., Girdhar, A., Ankur, et al.: A novel spectral-spatial 3D auxiliary conditional GAN integrated convolutional LSTM for hyperspectral image classification. Earth Sci. Inf. 17(6), 5251\u20135271 (2024)","journal-title":"Earth Sci. Inf."},{"issue":"6","key":"4959_CR19","doi-asserted-by":"publisher","first-page":"5040","DOI":"10.1109\/TGRS.2020.3015843","volume":"59","author":"J Wang","year":"2020","unstructured":"Wang, J., Gao, F., Dong, J., et al.: Adaptive dropblock-enhanced generative adversarial networks for hyperspectral image classification. IEEE Trans. Geosci. Remote Sens. 59(6), 5040\u20135053 (2020)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"4959_CR20","doi-asserted-by":"publisher","first-page":"7336","DOI":"10.1109\/ACCESS.2022.3232152","volume":"11","author":"D Song","year":"2022","unstructured":"Song, D., Tang, Y., Wang, B., et al.: Two-branch generative adversarial network with multiscale connections for hyperspectral image classification. IEEE Access. 11, 7336\u20137347 (2022)","journal-title":"IEEE Access."},{"issue":"14","key":"4959_CR21","doi-asserted-by":"publisher","first-page":"5452","DOI":"10.1080\/01431161.2022.2135412","volume":"43","author":"C Shi","year":"2022","unstructured":"Shi, C., Zhang, T., Liao, D., et al.: Dual hybrid convolutional generative adversarial network for hyperspectral image classification. Int. J. Remote Sens. 43(14), 5452\u20135479 (2022)","journal-title":"Int. J. Remote Sens."},{"issue":"3","key":"4959_CR22","doi-asserted-by":"publisher","first-page":"538","DOI":"10.1364\/JOSAA.478585","volume":"40","author":"C Ma","year":"2023","unstructured":"Ma, C., Wan, M., Kong, X., et al.: Hybrid spatial-spectral generative adversarial network for hyperspectral image classification. JOSA A. 40(3), 538\u2013548 (2023)","journal-title":"JOSA A"},{"key":"4959_CR23","unstructured":"Salimans, T., Goodfellow, I., Zaremba, W., et al.: Improved techniques for training gans. Adv. Neural. Inf. Process. Syst. 29 (2016)"},{"issue":"4","key":"4959_CR24","doi-asserted-by":"publisher","first-page":"709","DOI":"10.1049\/iet-ipr.2019.0869","volume":"14","author":"Z Xue","year":"2020","unstructured":"Xue, Z.: Semi-supervised convolutional generative adversarial network for hyperspectral image classification. IET Image Proc. 14(4), 709\u2013719 (2020)","journal-title":"IET Image Proc."},{"issue":"2","key":"4959_CR25","doi-asserted-by":"publisher","first-page":"198","DOI":"10.3390\/rs13020198","volume":"13","author":"H Liang","year":"2021","unstructured":"Liang, H., Bao, W., Shen, X.: Adaptive weighting feature fusion approach based on generative adversarial network for hyperspectral image classification. Remote Sens. 13(2), 198 (2021)","journal-title":"Remote Sens."}],"container-title":["Signal, Image and Video Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-025-04959-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11760-025-04959-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-025-04959-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,3]],"date-time":"2025-12-03T00:55:30Z","timestamp":1764723330000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11760-025-04959-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,17]]},"references-count":25,"journal-issue":{"issue":"16","published-print":{"date-parts":[[2025,12]]}},"alternative-id":["4959"],"URL":"https:\/\/doi.org\/10.1007\/s11760-025-04959-y","relation":{},"ISSN":["1863-1703","1863-1711"],"issn-type":[{"value":"1863-1703","type":"print"},{"value":"1863-1711","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,17]]},"assertion":[{"value":"19 January 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 October 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 November 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 November 2025","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"The authors declare no competing interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"1369"}}