{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,14]],"date-time":"2026-03-14T18:17:05Z","timestamp":1773512225492,"version":"3.50.1"},"publisher-location":"Singapore","reference-count":24,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819784929","type":"print"},{"value":"9789819784936","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,11,1]],"date-time":"2024-11-01T00:00:00Z","timestamp":1730419200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,11,1]],"date-time":"2024-11-01T00:00:00Z","timestamp":1730419200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-981-97-8493-6_32","type":"book-chapter","created":{"date-parts":[[2024,10,31]],"date-time":"2024-10-31T20:04:29Z","timestamp":1730405069000},"page":"459-471","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Hyperspectral Image Change Detection via Cross-Sample Slot Attention and Dual Gated Feed-Forward Network"],"prefix":"10.1007","author":[{"given":"Luyao","family":"Cheng","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junyan","family":"Lin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Feng","family":"Gao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,11,1]]},"reference":[{"key":"32_CR1","doi-asserted-by":"crossref","unstructured":"Ding, J., Li, X.: A spatial-spectral-temporal attention method for hyperspectral image change detection. In: The 2022 IEEE International Geoscience and Remote Sensing Symposium, pp. 3704\u20133707 (2022)","DOI":"10.1109\/IGARSS46834.2022.9883386"},{"key":"32_CR2","doi-asserted-by":"publisher","first-page":"10748","DOI":"10.1109\/JSTARS.2021.3120381","volume":"14","author":"Y Gao","year":"2021","unstructured":"Gao, Y., Gao, F., Dong, J., Du, Q., Li, H.-C.: Synthetic aperture radar image change detection via siamese adaptive fusion network. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 14, 10748\u201310760 (2021)","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"32_CR3","doi-asserted-by":"publisher","first-page":"7724","DOI":"10.1109\/JSTARS.2022.3204541","volume":"15","author":"X Ou","year":"2022","unstructured":"Ou, X., Liu, L., Tan, S., Zhang, G., Li, W., Tu, B.: A hyperspectral image change detection framework with self-supervised contrastive learning pretrained model. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 15, 7724\u20137740 (2022)","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"32_CR4","unstructured":"Malila, W.A.: Change vector analysis: an approach for detecting forest changes with Landsat. In: LARS Symposia, pp. 1\u201312 (1980)"},{"key":"32_CR5","first-page":"1","volume":"19","author":"R Song","year":"2022","unstructured":"Song, R., Ni, W., Cheng, W., Wang, X.: CSANet: cross-temporal interaction symmetric attention network for hyperspectral image change detection. IEEE Geosci. Remote Sens. Lett. 19, 1\u20135 (2022)","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"32_CR6","first-page":"1","volume":"61","author":"W Dong","year":"2023","unstructured":"Dong, W., Yang, Y., Qu, J., Xiao, S., Li, Y.: Local information enhanced graph-transformer for hyperspectral image change detection with limited training samples. IEEE Trans. Geosci. Remote Sens. 61, 1\u201314 (2023)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"32_CR7","unstructured":"Yang, T., Wang, Y., Lu, Y., Zheng, N.: Visual concepts tokenization. In: The 2022 Conference and Workshop on Neural Information Processing Systems, vol. 35, pp. 31571\u201331582 (2022)"},{"issue":"4","key":"32_CR8","doi-asserted-by":"publisher","first-page":"433","DOI":"10.1002\/wics.101","volume":"2","author":"H Abdi","year":"2010","unstructured":"Abdi, H., Williams, L.J.: Principal component analysis. Wiley Interdisc. Rev. Comput. Stat. 2(4), 433\u2013459 (2010)","journal-title":"Wiley Interdisc. Rev. Comput. Stat."},{"issue":"4","key":"32_CR9","doi-asserted-by":"publisher","first-page":"772","DOI":"10.1109\/LGRS.2009.2025059","volume":"6","author":"T Celik","year":"2009","unstructured":"Celik, T.: Unsupervised change detection in satellite images using principal component analysis and $$k$$-means clustering. IEEE Geosci. Remote Sens. Lett. 6(4), 772\u2013776 (2009)","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"issue":"4","key":"32_CR10","doi-asserted-by":"publisher","first-page":"2141","DOI":"10.1109\/TIP.2011.2170702","volume":"21","author":"M Gong","year":"2011","unstructured":"Gong, M., Zhou, Z., Ma, J.: Change detection in synthetic aperture radar images based on image fusion and fuzzy clustering. IEEE Trans. Image Process. 21(4), 2141\u20132151 (2011)","journal-title":"IEEE Trans. Image Process."},{"key":"32_CR11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TGRS.2022.3156041","volume":"60","author":"X Ou","year":"2022","unstructured":"Ou, X., Liu, L., Tu, B., Zhang, G., Xu, Z.: A CNN framework with slow-fast band selection and feature fusion grouping for hyperspectral image change detection. IEEE Trans. Geosci. Remote Sens. 60, 1\u201316 (2022)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"32_CR12","doi-asserted-by":"crossref","unstructured":"Wang, L., Wan, L., Bruzzone, L.: A sub-pixel convolution-based residual network for hyperspectral image change detection. In: The 2022 IEEE International Geoscience and Remote Sensing Symposium, pp. 1059\u20131062 (2022)","DOI":"10.1109\/IGARSS46834.2022.9884805"},{"key":"32_CR13","first-page":"1","volume":"19","author":"X Qu","year":"2021","unstructured":"Qu, X., Gao, F., Dong, J., Du, Q., Li, H.-C.: Change detection in synthetic aperture radar images using a dual-domain network. IEEE Geosci. Remote Sens. Lett. 19, 1\u20135 (2021)","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"32_CR14","first-page":"1","volume":"60","author":"Q Guo","year":"2021","unstructured":"Guo, Q., Zhang, J., Zhu, S., Zhong, C., Zhang, Y.: Deep multiscale siamese network with parallel convolutional structure and self-attention for change detection. IEEE Trans. Geosci. Remote Sens. 60, 1\u201312 (2021)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"issue":"10","key":"32_CR15","doi-asserted-by":"publisher","first-page":"6615","DOI":"10.1109\/TCSVT.2022.3176055","volume":"32","author":"Y Zhou","year":"2022","unstructured":"Zhou, Y., Wang, F., Zhao, J., Yao, R., Chen, S., Ma, H.: Spatial-temporal based multihead self-attention for remote sensing image change detection. IEEE Trans. Circ. Syst. Video Technol. 32(10), 6615\u20136626 (2022)","journal-title":"IEEE Trans. Circ. Syst. Video Technol."},{"key":"32_CR16","first-page":"1","volume":"60","author":"H Dong","year":"2021","unstructured":"Dong, H., Ma, W., Jiao, L., Liu, F., Li, L.: A multiscale self-attention deep clustering for change detection in SAR images. IEEE Trans. Geosci. Remote Sens. 60, 1\u201316 (2021)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"32_CR17","unstructured":"Locatello, F., Weissenborn, D., Unterthiner, T., Mahendran, A., Heigold, G., Uszkoreit, J., Dosovitskiy, A., Kipf, T.: Object-centric learning with slot attention. In: Advances in Neural Information Processing Systems, vol. 33, pp. 11525\u201311538 (2020)"},{"key":"32_CR18","doi-asserted-by":"crossref","unstructured":"Cho, K., Van, M., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning phrase representations using RNN encoder-decoder for statistical machine translation. In: The 2014 Conference on Empirical Methods in Natural Language Processing, pp. 1\u201315 (2014)","DOI":"10.3115\/v1\/D14-1179"},{"key":"32_CR19","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: The 2016 IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"32_CR20","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A., Kaiser, L., Polosukhin, I.: Attention is all you need. In: The 2017 Conference and Workshop on Neural Information Processing Systems, vol. 30 (2017)"},{"key":"32_CR21","first-page":"1","volume":"60","author":"L Wang","year":"2021","unstructured":"Wang, L., Wang, L., Wang, Q., Atkinson, P.M.: SSA-SiamNet: spectral-spatial-wise attention-based Siamese network for hyperspectral image change detection. IEEE Trans. Geosci. Remote Sens. 60, 1\u201318 (2021)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"issue":"5","key":"32_CR22","doi-asserted-by":"publisher","first-page":"895","DOI":"10.3390\/rs13050895","volume":"13","author":"T Zhan","year":"2021","unstructured":"Zhan, T., Song, B., Xu, Y., Wan, M., Wang, X., Yang, G., Wu, Z.: SSCNN-S: a spectral-spatial convolution neural network with siamese architecture for change detection. Remote Sens. 13(5), 895 (2021)","journal-title":"Remote Sens."},{"key":"32_CR23","first-page":"1","volume":"19","author":"J Ding","year":"2022","unstructured":"Ding, J., Li, X., Zhao, L.: CDFormer: a hyperspectral image change detection method based on transformer encoders. IEEE Geosci. Remote Sens. Lett. 19, 1\u20135 (2022)","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"32_CR24","first-page":"1","volume":"60","author":"Y Wang","year":"2022","unstructured":"Wang, Y., Hong, D., Sha, J., Gao, L., Liu, L., Zhang, Y., Rong, X.: Spectral-spatial-temporal transformers for hyperspectral image change detection. IEEE Trans. Geosci. Remote Sens. 60, 1\u201314 (2022)","journal-title":"IEEE Trans. Geosci. Remote Sens."}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition and Computer Vision"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-97-8493-6_32","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,10]],"date-time":"2025-04-10T19:09:17Z","timestamp":1744312157000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-97-8493-6_32"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,1]]},"ISBN":["9789819784929","9789819784936"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-981-97-8493-6_32","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,1]]},"assertion":[{"value":"1 November 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PRCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Chinese Conference on Pattern Recognition and Computer Vision  (PRCV)","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Urumqi","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 October 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ccprcv2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/2024.prcv.cn\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}