{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,27]],"date-time":"2026-01-27T01:58:18Z","timestamp":1769479098500,"version":"3.49.0"},"reference-count":26,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62271302,62101316"],"award-info":[{"award-number":["62271302,62101316"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100007219","name":"Shanghai Municipal Natural Science Foundation","doi-asserted-by":"crossref","award":["20ZR1423500"],"award-info":[{"award-number":["20ZR1423500"]}],"id":[{"id":"10.13039\/100007219","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["SIViP"],"published-print":{"date-parts":[[2026,1]]},"DOI":"10.1007\/s11760-025-04972-1","type":"journal-article","created":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T02:32:39Z","timestamp":1768530759000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Cascaded hypergraph compression and reconstruction with semantic-aware segmentation and spectral unmixing for hyperspectral image classification"],"prefix":"10.1007","volume":"20","author":[{"given":"Ming","family":"Zhao","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ying","family":"Lai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lufang","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lixiang","family":"Ma","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Changqing","family":"Lin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andr\u00e9","family":"Kaup","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,1,16]]},"reference":[{"issue":"1","key":"4972_CR1","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1109\/79.974718","volume":"19","author":"D Landgrebe","year":"2002","unstructured":"Landgrebe, D.: Hyperspectral image data analysis. IEEE Signal Process. Mag. 19(1), 17\u201328 (2002)","journal-title":"IEEE Signal Process. Mag."},{"key":"4972_CR2","doi-asserted-by":"publisher","first-page":"209","DOI":"10.1016\/j.isprsjprs.2023.05.025","volume":"201","author":"C Zhao","year":"2023","unstructured":"Zhao, C., Jia, M., Wang, Z., Mao, D., Wang, Y.: Identifying mangroves through knowledge extracted from trained random forest models: An interpretable mangrove mapping approach (imma). ISPRS J. Photogramm. Remote. Sens. 201, 209\u2013225 (2023)","journal-title":"ISPRS J. Photogramm. Remote. Sens."},{"key":"4972_CR3","doi-asserted-by":"crossref","unstructured":"Kossira, K., Sch\u00f6n n, D., Seiler, J., Kaup, A.: Conditional optimal filter selection for multi-spectral image classification. In: Proc. IEEE Int. Conf. Image Process. (ICIP), Abu Dhabi, UAE (2024)","DOI":"10.1109\/ICIP51287.2024.10647688"},{"key":"4972_CR4","doi-asserted-by":"crossref","unstructured":"Hu, W., Huang, Y., Wei, L., Zhang, F., Li, H.: Deep convolutional neural networks for hyperspectral image classification. J. Sens. 2015 (2015)","DOI":"10.1155\/2015\/258619"},{"key":"4972_CR5","doi-asserted-by":"crossref","unstructured":"Li, X., Ding, M., Pi\u017eurica, A.: Deep feature fusion via two-stream convolutional neural network for hyperspectral image classification. IEEE Trans. Geosci. Remote Sens. 58(4), 2615\u20132629 (2020)","DOI":"10.1109\/TGRS.2019.2952758"},{"key":"4972_CR6","doi-asserted-by":"crossref","unstructured":"Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-D-2-D cnn feature hierarchy for hyperspectral image classification. IEEE Geosci. Remote Sens. Lett. 17(2), 277\u2013281 (2020)","DOI":"10.1109\/LGRS.2019.2918719"},{"key":"4972_CR7","unstructured":"Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. In: Proc. Int. Conf. Learn. Represent. (ICLR), pp. 1\u201314 (2017)"},{"key":"4972_CR8","doi-asserted-by":"crossref","unstructured":"Liu, Q., Xiao, L., Yang, J., Wei, Z.: CNN-enhanced graph convolutional network with pixel- and superpixel-level feature fusion for hyperspectral image classification. IEEE Trans. Geosci. Remote Sens. 59(10), 8657\u20138671 (2021)","DOI":"10.1109\/TGRS.2020.3037361"},{"key":"4972_CR9","first-page":"1","volume":"61","author":"H Zhou","year":"2023","unstructured":"Zhou, H., Luo, F., Zhuang, H., Weng, Z., Gong, X., Lin, Z.: Attention multihop graph and multiscale convolutional fusion network for hyperspectral image classification. IEEE Trans. Geosci. Remote Sens. 61, 1\u201314 (2023)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"4972_CR10","doi-asserted-by":"crossref","unstructured":"Hu, Z., Tu, B., Liu, B., He, Y., Li, J., Plaza, A.: Self-supervised graph masked autoencoders for hyperspectral image classification. IEEE Trans. Geosci. Remote Sens. 63 (2025)","DOI":"10.1109\/TGRS.2025.3555967"},{"key":"4972_CR11","doi-asserted-by":"crossref","unstructured":"Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., S\u00fcsstrunk, S.: Slic superpixels compared to state-of-the-art superpixel methods. IEEE Trans. Pattern Anal. Mach. Intell. 34(11), 2274\u20132282 (2012)","DOI":"10.1109\/TPAMI.2012.120"},{"key":"4972_CR12","doi-asserted-by":"crossref","unstructured":"Yang, F., Sun, Q., Jin, H., Zhou, Z.: Superpixel segmentation with fully convolutional networks. In: Proc. IEEE\/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR), pp. 13961\u201313970 (2020)","DOI":"10.1109\/CVPR42600.2020.01398"},{"key":"4972_CR13","doi-asserted-by":"crossref","unstructured":"Wu, G., Al-qaness, M.A.A., Al-Alimi, D., Dahou, A., Abd Elaziz, M., Ewees, A.A.: Hyperspectral image classification using graph convolutional network: A comprehensive review. Expert Syst. Appl. 257, 125106 (2024)","DOI":"10.1016\/j.eswa.2024.125106"},{"key":"4972_CR14","doi-asserted-by":"crossref","unstructured":"Feng, Y., You, H., Zhang, Z., Ji, R., Gao, Y.: Hypergraph neural networks. In: Proc. AAAI Conf. Artif. Intell., vol. 33, pp. 3558\u20133565 (2019)","DOI":"10.1609\/aaai.v33i01.33013558"},{"key":"4972_CR15","first-page":"1","volume":"20","author":"Q Xu","year":"2023","unstructured":"Xu, Q., Xu, S., Liu, J., Huang, L.: Dynamic hypergraph convolution and recursive gated convolution fusion network for hyperspectral image classification. IEEE Geosci. Remote Sens. Lett. 20, 1\u20135 (2023)","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"4972_CR16","first-page":"1","volume":"62","author":"Y Wang","year":"2024","unstructured":"Wang, Y., Xue, Z., Jia, M., Liu, Z., Su, H.: Hypergraph convolutional network with multiple hyperedges fusion for hyperspectral image classification under limited samples. IEEE Trans. Geosci. Remote Sens. 62, 1\u201318 (2024)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"4972_CR17","first-page":"1","volume":"19","author":"Z Han","year":"2022","unstructured":"Han, Z., Hong, D., Gao, L., Roy, S.K., Zhang, B., Chanussot, J.: Reinforcement learning for neural architecture search in hyperspectral unmixing. IEEE Geosci. Remote Sens. Lett. 19, 1\u20135 (2022)","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"4972_CR18","doi-asserted-by":"crossref","unstructured":"Ren, L., Hong, D., Gao, L., Sun, X., Huang, M., Chanussot, J.: Hyperspectral sparse unmixing via nonconvex shrinkage penalties. IEEE Trans. Geosci. Remote Sens. 61, 1\u201315 (2023)","DOI":"10.1109\/TGRS.2022.3232570"},{"key":"4972_CR19","first-page":"1","volume":"62","author":"Z Han","year":"2024","unstructured":"Han, Z., Yang, J., Gao, L., Zeng, Z., Zhang, B., Chanussot, J.: Dual-branch subpixel-guided network for hyperspectral image classification. IEEE Trans. Geosci. Remote Sens. 62, 1\u201313 (2024)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"4972_CR20","unstructured":"Lostar, M., Rekik, I.: Deep hypergraph u-net for brain graph embedding and classification. arXiv preprint arXiv:2008.13118 (2020)"},{"key":"4972_CR21","doi-asserted-by":"crossref","unstructured":"Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral-spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Trans. Geosci. Remote Sens. 56(2), 847\u2013858 (2018)","DOI":"10.1109\/TGRS.2017.2755542"},{"key":"4972_CR22","doi-asserted-by":"crossref","unstructured":"Li, R., Zheng, S., Duan, C., Yang, Y., Wang, X.: Classification of hyperspectral image based on double-branch dual-attention mechanism network. Remote Sens. 12(3) (2020)","DOI":"10.3390\/rs12030582"},{"key":"4972_CR23","first-page":"1","volume":"62","author":"Z Li","year":"2023","unstructured":"Li, Z., Xue, Z., Xu, Q., Zhang, L.: Spformer: Self-pooling transformer for few-shot hyperspectral image classification. IEEE Trans. Geosci. Remote Sens. 62, 1\u201319 (2023)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"4972_CR24","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2025.107311","volume":"187","author":"Y Xu","year":"2025","unstructured":"Xu, Y., Wang, D., Zhang, L., Zhang, L.: Dual selective fusion transformer network for hyperspectral image classification. Neural Netw. 187, 107311 (2025)","journal-title":"Neural Netw."},{"key":"4972_CR25","doi-asserted-by":"publisher","first-page":"8671","DOI":"10.1109\/TIP.2021.3118977","volume":"30","author":"Y Xu","year":"2021","unstructured":"Xu, Y., Du, B., Zhang, L.: Self-attention context network: Addressing the threat of adversarial attacks for hyperspectral image classification. IEEE Trans. Image Process. 30, 8671\u20138685 (2021)","journal-title":"IEEE Trans. Image Process."},{"key":"4972_CR26","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TGRS.2023.3250450","volume":"61","author":"B Tu","year":"2023","unstructured":"Tu, B., He, W., Li, Q., Peng, Y., Plaza, A.: A new context-aware framework for defending against adversarial attacks in hyperspectral image classification. IEEE Trans. Geosci. Remote Sens. 61, 1\u201314 (2023)","journal-title":"IEEE Trans. Geosci. Remote Sens."}],"container-title":["Signal, Image and Video Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-025-04972-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11760-025-04972-1","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-025-04972-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,26]],"date-time":"2026-01-26T15:44:10Z","timestamp":1769442250000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11760-025-04972-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1]]},"references-count":26,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,1]]}},"alternative-id":["4972"],"URL":"https:\/\/doi.org\/10.1007\/s11760-025-04972-1","relation":{},"ISSN":["1863-1703","1863-1711"],"issn-type":[{"value":"1863-1703","type":"print"},{"value":"1863-1711","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1]]},"assertion":[{"value":"7 September 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 October 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 November 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 January 2026","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"24"}}