{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T16:46:06Z","timestamp":1778258766721,"version":"3.51.4"},"publisher-location":"Cham","reference-count":22,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031442094","type":"print"},{"value":"9783031442100","type":"electronic"}],"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\/978-3-031-44210-0_21","type":"book-chapter","created":{"date-parts":[[2023,9,21]],"date-time":"2023-09-21T08:02:34Z","timestamp":1695283354000},"page":"259-270","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["End-to-End Remote Sensing Change Detection of\u00a0Unregistered Bi-temporal Images for\u00a0Natural Disasters"],"prefix":"10.1007","author":[{"given":"Guiqin","family":"Zhao","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lianlei","family":"Shan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weiqiang","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,9,22]]},"reference":[{"issue":"2","key":"21_CR1","doi-asserted-by":"publisher","first-page":"266","DOI":"10.1109\/LGRS.2018.2869608","volume":"16","author":"M Zhang","year":"2019","unstructured":"Zhang, M., Xu, G., Chen, K., Yan, M., Sun, X.: Triplet-based semantic relation learning for aerial remote sensing image change detection. IEEE Geosci. Remote Sens. Lett. 16(2), 266\u2013270 (2019)","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"21_CR2","unstructured":"Bandara, W.G.C., Patel, V.M.: A transformer-based Siamese network for change detection, pp. 2\u20135 (2022). http:\/\/arxiv.org\/abs\/2201.01293"},{"issue":"10","key":"21_CR3","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(10), 1662 (2020)","journal-title":"Remote Sens."},{"key":"21_CR4","first-page":"1","volume":"60","author":"H Chen","year":"2022","unstructured":"Chen, H., Qi, Z., Shi, Z.: Remote sensing image change detection with transformers. IEEE Trans. Geosci. Remote Sens. 60, 1\u201314 (2022)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"21_CR5","doi-asserted-by":"publisher","first-page":"1194","DOI":"10.1109\/JSTARS.2020.3037893","volume":"14","author":"J Chen","year":"2020","unstructured":"Chen, J., Yuan, Z., Peng, J., Chen, L., Li, H.: 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)","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"21_CR6","unstructured":"Daudt, R.C., Saux, B.L., Boulch, A.: Fully convolutional siamese networks for change detection. In: 2018 25th IEEE International Conference on Image Processing (ICIP) (2018)"},{"key":"21_CR7","doi-asserted-by":"publisher","unstructured":"Dosovitskiy, A., et al.: FlowNet: learning optical flow with convolutional networks. In: 2015 IEEE International Conference on Computer Vision (ICCV), pp. 2758\u20132766 (2015). https:\/\/doi.org\/10.1109\/ICCV.2015.316","DOI":"10.1109\/ICCV.2015.316"},{"key":"21_CR8","first-page":"1","volume":"19","author":"S Fang","year":"2022","unstructured":"Fang, S., Li, K., Shao, J., Li, Z.: SNUNet-CD: a densely connected Siamese network for change detection of VHR images. IEEE Geosci. Remote Sens. Lett. 19, 1\u20135 (2022)","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"21_CR9","unstructured":"Gupta, R., et al.: Creating XBD: a dataset for assessing building damage from satellite imagery. In: CVPR Workshops (2019)"},{"key":"21_CR10","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"21_CR11","doi-asserted-by":"publisher","unstructured":"Hui, T.W., Tang, X., Loy, C.C.: LiteFlowNet: a lightweight convolutional neural network for optical flow estimation. In: 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 8981\u20138989 (2018). https:\/\/doi.org\/10.1109\/CVPR.2018.00936","DOI":"10.1109\/CVPR.2018.00936"},{"key":"21_CR12","unstructured":"Hui, T.W., Tang, X., Loy, C.C.: A Lightweight Optical Flow CNN - Revisiting Data Fidelity and Regularization (2020). http:\/\/mmlab.ie.cuhk.edu.hk\/projects\/LiteFlowNet\/"},{"issue":"5","key":"21_CR13","doi-asserted-by":"publisher","first-page":"2664","DOI":"10.1109\/TGRS.2014.2363548","volume":"53","author":"C Marin","year":"2015","unstructured":"Marin, C., Bovolo, F., Bruzzone, L.: Building change detection in multitemporal very high resolution SAR images. IEEE Trans. Geosci. Remote Sens. 53(5), 2664\u20132682 (2015)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"issue":"9","key":"21_CR14","doi-asserted-by":"publisher","first-page":"7296","DOI":"10.1109\/TGRS.2020.3033009","volume":"59","author":"X Peng","year":"2021","unstructured":"Peng, X., Zhong, R., Li, Z., Li, Q.: Optical remote sensing image change detection based on attention mechanism and image difference. IEEE Trans. Geosci. Remote Sens. 59(9), 7296\u20137307 (2021)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"issue":"2","key":"21_CR15","doi-asserted-by":"publisher","first-page":"1020","DOI":"10.1109\/TPAMI.2020.3016711","volume":"44","author":"I Rocco","year":"2022","unstructured":"Rocco, I., Cimpoi, M., Arandjelovi\u0107, R., Torii, A., Pajdla, T., Sivic, J.: NCNet: neighbourhood consensus networks for estimating image correspondences. IEEE Trans. Pattern Anal. Mach. Intell. 44(2), 1020\u20131034 (2022)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"5","key":"21_CR16","doi-asserted-by":"publisher","first-page":"4173","DOI":"10.3390\/rs6054173","volume":"6","author":"K Rokni","year":"2014","unstructured":"Rokni, K., Ahmad, A., Selamat, A., Hazini, S.: Water feature extraction and change detection using multitemporal landsat imagery. Remote Sens. 6(5), 4173\u20134189 (2014)","journal-title":"Remote Sens."},{"key":"21_CR17","doi-asserted-by":"publisher","unstructured":"Sun, D., Yang, X., Liu, M.Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 8934\u20138943 (2018). https:\/\/doi.org\/10.1109\/CVPR.2018.00931","DOI":"10.1109\/CVPR.2018.00931"},{"issue":"6","key":"21_CR18","doi-asserted-by":"publisher","first-page":"1408","DOI":"10.1109\/TPAMI.2019.2894353","volume":"42","author":"D Sun","year":"2020","unstructured":"Sun, D., Yang, X., Liu, M.Y., Kautz, J.: Models matter, so does training: an empirical study of CNNs for optical flow estimation. IEEE Trans. Pattern Anal. Mach. Intell. 42(6), 1408\u20131423 (2020). https:\/\/doi.org\/10.1109\/TPAMI.2019.2894353","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"21_CR19","doi-asserted-by":"crossref","unstructured":"Truong, P., Danelljan, M., Timofte, R.: GLU-Net: global-local universal network for dense flow and correspondences. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2020)","DOI":"10.1109\/CVPR42600.2020.00629"},{"key":"21_CR20","doi-asserted-by":"publisher","unstructured":"Vincent, P., Larochelle, H., Bengio, Y., Manzagol, P.A.: Extracting and composing robust features with denoising autoencoders. In: Proceedings of the 25th International Conference on Machine Learning, ICML 2008, pp. 1096\u20131103. Association for Computing Machinery, New York (2008). https:\/\/doi.org\/10.1145\/1390156.1390294","DOI":"10.1145\/1390156.1390294"},{"key":"21_CR21","unstructured":"Xu, J.Z., Lu, W., Li, Z., Khaitan, P., Zaytseva, V.: Building damage detection in satellite imagery using convolutional neural networks. arXiv (2019)"},{"issue":"10","key":"21_CR22","doi-asserted-by":"publisher","first-page":"1845","DOI":"10.1109\/LGRS.2017.2738149","volume":"14","author":"Y Zhan","year":"2017","unstructured":"Zhan, Y., Fu, K., Yan, M., Sun, X., Wang, H., Qiu, X.: Change detection based on deep Siamese convolutional network for optical aerial images. IEEE Geosci. Remote Sens. Lett. 14(10), 1845\u20131849 (2017)","journal-title":"IEEE Geosci. Remote Sens. Lett."}],"container-title":["Lecture Notes in Computer Science","Artificial Neural Networks and Machine Learning \u2013 ICANN 2023"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-44210-0_21","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,21]],"date-time":"2023-09-21T08:06:08Z","timestamp":1695283568000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-44210-0_21"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031442094","9783031442100"],"references-count":22,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-44210-0_21","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"22 September 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICANN","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Artificial Neural Networks","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Heraklion","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Greece","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":"26 September 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 September 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"32","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icann2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/e-nns.org\/icann2023\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"easyacademia.org","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"947","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":"426","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":"22","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":"45% - 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":"2.4","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":"4","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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"type of other papers accepted  : 9 Abstract","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}