{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T21:49:41Z","timestamp":1743112181911,"version":"3.40.3"},"publisher-location":"Cham","reference-count":14,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031463167"},{"type":"electronic","value":"9783031463174"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-3-031-46317-4_25","type":"book-chapter","created":{"date-parts":[[2023,10,28]],"date-time":"2023-10-28T06:03:06Z","timestamp":1698472986000},"page":"308-319","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Large Window Attention Based Transformer Network for Change Detection of Remote Sensing Images"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1345-7283","authenticated-orcid":false,"given":"Kunfeng","family":"Yu","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0005-7696-4783","authenticated-orcid":false,"given":"Yuqian","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Bo","family":"Hou","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4771-2315","authenticated-orcid":false,"given":"Tao","family":"Xu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0798-5725","authenticated-orcid":false,"given":"Wenshuo","family":"Li","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7887-868X","authenticated-orcid":false,"given":"Zhen","family":"Liu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0007-7526-8019","authenticated-orcid":false,"given":"Junyuan","family":"Zang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,10,29]]},"reference":[{"key":"25_CR1","doi-asserted-by":"publisher","unstructured":"Yan, H., Zhang, C., Wu, M.: LWA transformer: improving semantic segmentation transformer with multi-scale representations via large window attention. arXiv e-prints (2022). https:\/\/doi.org\/10.48550\/arXiv.2201.01615","DOI":"10.48550\/arXiv.2201.01615"},{"key":"25_CR2","doi-asserted-by":"crossref","unstructured":"Hou, B., Wang, Y., Liu, Q.: Change detection based on deep features and low rank. IEEE Geosci. Remote Sens. Lett. 14(12), 2418\u20132422 (2017)","DOI":"10.1109\/LGRS.2017.2766840"},{"key":"25_CR3","doi-asserted-by":"crossref","unstructured":"Chen, J., et al.: DASNet: dual attentive fully convolutional Siamese networks for change detection of high-resolution satellite images. IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens. 14, 1194\u20131206 (2020)","DOI":"10.1109\/JSTARS.2020.3037893"},{"key":"25_CR4","doi-asserted-by":"publisher","first-page":"1662","DOI":"10.3390\/rs12101662","volume":"12","author":"H Chen","year":"2020","unstructured":"Chen, H., Shi, Z.: A spatial-temporal attention-based method and a new dataset for remote sensing image change detection. Remote Sens. 12, 1662 (2020)","journal-title":"Remote Sens."},{"key":"25_CR5","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1016\/j.isprsjprs.2021.10.001","volume":"182","author":"H Cheng","year":"2021","unstructured":"Cheng, H., Wu, H., Zheng, J., Qi, K., Liu, W.: A hierarchical self-attention augmented Laplacian pyramid expanding network for change detection in high-resolution remote sensing images. ISPRS J. Photogramm. Remote Sens. 182, 52\u201366 (2021)","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"25_CR6","doi-asserted-by":"publisher","first-page":"3707","DOI":"10.3390\/rs13183707","volume":"13","author":"FI Diakogiannis","year":"2021","unstructured":"Diakogiannis, F.I., Waldner, F., Caccetta, P.: Looking for change? Roll the Dice and demand attention. Remote Sens. 13, 3707 (2021)","journal-title":"Remote Sens."},{"key":"25_CR7","first-page":"102597","volume":"105","author":"L Song","year":"2021","unstructured":"Song, L., Xia, M., Jin, J., Qian, M., Zhang, Y.: SUACDNet: attentional change detection network based on Siamese U-shaped structure. Int. J. Appl. Earth Obs. Geoinf. 105, 102597 (2021)","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"25_CR8","unstructured":"Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, vol. 30 (2017)"},{"issue":"9","key":"25_CR9","doi-asserted-by":"publisher","first-page":"2228","DOI":"10.3390\/rs14092228","volume":"14","author":"G Wang","year":"2022","unstructured":"Wang, G., Li, B., Zhang, T., Zhang, S.: A network combining a transformer and a convolutional neural network for remote sensing image change detection. Remote Sens. 14(9), 2228 (2022)","journal-title":"Remote Sens."},{"key":"25_CR10","first-page":"1","volume":"60","author":"Q Li","year":"2022","unstructured":"Li, Q., Zhong, R., Du, X., Du, Y.: TransUNetCD: a hybrid transformer network for change detection in optical remote-sensing images. IEEE Trans. Geosci. Remote Sens. 60, 1\u201319 (2022)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"25_CR11","doi-asserted-by":"crossref","unstructured":"Bandara, W.G.C., Patel, V.M.: A transformer-based Siamese network for change detection. arXiv:2201.01293 (2022)","DOI":"10.1109\/IGARSS46834.2022.9883686"},{"key":"25_CR12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TGRS.2020.3034752","volume":"60","author":"H Chen","year":"2021","unstructured":"Chen, H., Qi, Z., Shi, Z.: Remote sensing image change detection with transformers. IEEE Trans. Geosci. Remote Sens. 60, 1\u201314 (2021)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"issue":"4","key":"25_CR13","doi-asserted-by":"publisher","first-page":"263","DOI":"10.3390\/ijgi11040263","volume":"11","author":"Q Ke","year":"2022","unstructured":"Ke, Q., Zhang, P.: Hybrid-TransCD: a hybrid transformer remote sensing image change detection network via token aggregation. ISPRS Int. J. Geo Inf. 11(4), 263 (2022)","journal-title":"ISPRS Int. J. Geo Inf."},{"key":"25_CR14","first-page":"1","volume":"19","author":"F Song","year":"2022","unstructured":"Song, F., Zhang, S., Lei, T., Song, Y., Peng, Z.: MSTDSNet-CD: multiscale Swin transformer and deeply supervised network for change detection of the fast-growing urban regions. IEEE Geosci. Remote Sens. Lett. 19, 1\u20135 (2022)","journal-title":"IEEE Geosci. Remote Sens. Lett."}],"container-title":["Lecture Notes in Computer Science","Image and Graphics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-46317-4_25","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,10,28]],"date-time":"2023-10-28T06:12:49Z","timestamp":1698473569000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-46317-4_25"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031463167","9783031463174"],"references-count":14,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-46317-4_25","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"29 October 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICIG","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Image and Graphics","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Nanjing","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":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 September 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24 September 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icig2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/icig2023.csig.org.cn\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Conference Management Toolkit","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"409","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"166","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"41% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}