{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T12:13:29Z","timestamp":1742991209950,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":30,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819984619"},{"type":"electronic","value":"9789819984626"}],"license":[{"start":{"date-parts":[[2023,12,26]],"date-time":"2023-12-26T00:00:00Z","timestamp":1703548800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,12,26]],"date-time":"2023-12-26T00:00:00Z","timestamp":1703548800000},"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":[[2024]]},"DOI":"10.1007\/978-981-99-8462-6_26","type":"book-chapter","created":{"date-parts":[[2023,12,25]],"date-time":"2023-12-25T19:02:17Z","timestamp":1703530937000},"page":"318-329","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Multi-scale Contrastive Learning for\u00a0Building Change Detection in\u00a0Remote Sensing Images"],"prefix":"10.1007","author":[{"given":"Mingliang","family":"Xue","sequence":"first","affiliation":[]},{"given":"Xinyuan","family":"Huo","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0000-4757-6734","authenticated-orcid":false,"given":"Yao","family":"Lu","sequence":"additional","affiliation":[]},{"given":"Pengyuan","family":"Niu","sequence":"additional","affiliation":[]},{"given":"Xuan","family":"Liang","sequence":"additional","affiliation":[]},{"given":"Hailong","family":"Shang","sequence":"additional","affiliation":[]},{"given":"Shucai","family":"Jia","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,12,26]]},"reference":[{"issue":"3","key":"26_CR1","doi-asserted-by":"publisher","first-page":"842","DOI":"10.3390\/rs15030842","volume":"15","author":"M Zhang","year":"2023","unstructured":"Zhang, M., Liu, Z., Feng, J., Liu, L., Jiao, L.: Remote sensing image change detection based on deep multi-scale multi-attention siamese transformer network. Remote Sens. 15(3), 842 (2023)","journal-title":"Remote Sens."},{"key":"26_CR2","doi-asserted-by":"publisher","first-page":"262","DOI":"10.1080\/10095020.2022.2085633","volume":"26","author":"T Bai","year":"2022","unstructured":"Bai, T., et al.: Deep learning for change detection in remote sensing: a review. Geo-Spatial Inf. Sci. 26, 262\u2013288 (2022)","journal-title":"Geo-Spatial Inf. Sci."},{"key":"26_CR3","doi-asserted-by":"publisher","first-page":"3735","DOI":"10.1109\/JSTARS.2020.3005403","volume":"13","author":"G Cheng","year":"2020","unstructured":"Cheng, G., Xie, X., Han, J., Guo, L., Xia, G.S.: Remote sensing image scene classification meets deep learning: challenges, methods, benchmarks, and opportunities. IEEE J. Sel. Topics Appl. Earth Obs. Remote Sens. 13, 3735\u20133756 (2020)","journal-title":"IEEE J. Sel. Topics Appl. Earth Obs. Remote Sens."},{"key":"26_CR4","first-page":"1","volume":"61","author":"F Jiang","year":"2023","unstructured":"Jiang, F., Gong, M., Zheng, H., Liu, T., Zhang, M., Liu, J.: Self-supervised global-local contrastive learning for fine-grained change detection in VHR images. IEEE Trans. Geosci. Remote Sens. 61, 1\u201313 (2023)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"issue":"4","key":"26_CR5","doi-asserted-by":"publisher","first-page":"8","DOI":"10.1109\/MGRS.2017.2762307","volume":"5","author":"XX Zhu","year":"2017","unstructured":"Zhu, X.X., et al.: Deep learning in remote sensing: a comprehensive review and list of resources. IEEE Geosci. Remote Sens. Maga. 5(4), 8\u201336 (2017)","journal-title":"IEEE Geosci. Remote Sens. Maga."},{"key":"26_CR6","unstructured":"Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597\u20131607. PMLR (2020)"},{"key":"26_CR7","unstructured":"Bardes, A., Ponce, J., LeCun, Y.: VICReg: variance-invariance-covariance regularization for self-supervised learning. In: International Conference on Learning Representations (2022)"},{"key":"26_CR8","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"578","DOI":"10.1007\/978-3-030-68787-8_42","volume-title":"Pattern Recognition. ICPR International Workshops and Challenges","author":"M Leenstra","year":"2021","unstructured":"Leenstra, M., Marcos, D., Bovolo, F., Tuia, D.: Self-supervised pre-training enhances change detection in sentinel-2 imagery. In: Del Bimbo, A., et al. (eds.) ICPR 2021. LNCS, vol. 12667, pp. 578\u2013590. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-68787-8_42"},{"key":"26_CR9","doi-asserted-by":"crossref","unstructured":"Manas, O., Lacoste, A., Gir\u00f3-i Nieto, X., Vazquez, D., Rodriguez, P.: Seasonal contrast: unsupervised pre-training from uncurated remote sensing data. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 9414\u20139423 (2021)","DOI":"10.1109\/ICCV48922.2021.00928"},{"key":"26_CR10","doi-asserted-by":"publisher","first-page":"6438","DOI":"10.1109\/JSTARS.2021.3090418","volume":"14","author":"W Li","year":"2021","unstructured":"Li, W., Chen, H., Shi, Z.: Semantic segmentation of remote sensing images with self-supervised multitask representation learning. IEEE J. Sel. Topics Appl. Earth Obs. Remote Sens. 14, 6438\u20136450 (2021)","journal-title":"IEEE J. Sel. Topics Appl. Earth Obs. Remote Sens."},{"key":"26_CR11","first-page":"1","volume":"60","author":"H Li","year":"2022","unstructured":"Li, H., et al.: Global and local contrastive self-supervised learning for semantic segmentation of HR remote sensing images. IEEE Trans. Geosci. Remote Sens. 60, 1\u201314 (2022)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"26_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.compeleceng.2022.108223","volume":"102","author":"X Gu","year":"2022","unstructured":"Gu, X., Li, S., Ren, S., Zheng, H., Fan, C., Xu, H.: Adaptive enhanced swin transformer with u-net for remote sensing image segmentation. Comput. Electr. Eng. 102, 108223 (2022)","journal-title":"Comput. Electr. Eng."},{"key":"26_CR13","first-page":"1","volume":"19","author":"S Fang","year":"2021","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 (2021)","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"26_CR14","doi-asserted-by":"crossref","unstructured":"Miyai, A., Yu, Q., Ikami, D., Irie, G., Aizawa, K.: Rethinking rotation in self-supervised contrastive learning: adaptive positive or negative data augmentation. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 2809\u20132818 (2023)","DOI":"10.1109\/WACV56688.2023.00283"},{"key":"26_CR15","doi-asserted-by":"crossref","unstructured":"Wang, H., Yao, M., Jiang, G., Mi, Z., Fu, X.: Graph-collaborated auto-encoder hashing for multiview binary clustering. IEEE Trans. Neural Netw. Learn. Syst. (2023)","DOI":"10.1109\/TNNLS.2023.3239033"},{"key":"26_CR16","doi-asserted-by":"publisher","first-page":"191","DOI":"10.1016\/j.neucom.2019.03.080","volume":"347","author":"H Wang","year":"2019","unstructured":"Wang, H., Peng, J., Fu, X.: Co-regularized multi-view sparse reconstruction embedding for dimension reduction. Neurocomputing 347, 191\u2013199 (2019)","journal-title":"Neurocomputing"},{"key":"26_CR17","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2019.105172","volume":"191","author":"L Feng","year":"2020","unstructured":"Feng, L., Meng, X., Wang, H.: Multi-view locality low-rank embedding for dimension reduction. Knowl.-Based Syst. 191, 105172 (2020)","journal-title":"Knowl.-Based Syst."},{"key":"26_CR18","doi-asserted-by":"crossref","unstructured":"He, K., Fan, H., Wu, Y., Xie, S., Girshick, R.: Momentum contrast for unsupervised visual representation learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 9729\u20139738 (2020)","DOI":"10.1109\/CVPR42600.2020.00975"},{"key":"26_CR19","doi-asserted-by":"crossref","unstructured":"Chen, X., He, K.: Exploring simple siamese representation learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 15750\u201315758 (2021)","DOI":"10.1109\/CVPR46437.2021.01549"},{"key":"26_CR20","doi-asserted-by":"publisher","first-page":"265","DOI":"10.1007\/978-3-031-20056-4_16","volume-title":"Computer Vision - ECCV 2022","author":"B Pang","year":"2022","unstructured":"Pang, B., Zhang, Y., Li, Y., Cai, J., Lu, C.: Unsupervised visual representation learning by synchronous momentum grouping. In: Avidan, S., Brostow, G., Ciss\u00e9, M., Farinella, G.M., Hassner, T. (eds.) ECCV 2022. LNCS, vol. 13690, pp. 265\u2013282. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-20056-4_16"},{"key":"26_CR21","doi-asserted-by":"crossref","unstructured":"Wang, X., Zhang, R., Shen, C., Kong, T., Li, L.: Dense contrastive learning for self-supervised visual pre-training. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3024\u20133033 (2021)","DOI":"10.1109\/CVPR46437.2021.00304"},{"key":"26_CR22","doi-asserted-by":"crossref","unstructured":"Chen, H., Zao, Y., Liu, L., Chen, S., Shi, Z.: Semantic decoupled representation learning for remote sensing image change detection. In: IGARSS 2022\u20132022 IEEE International Geoscience and Remote Sensing Symposium, pp. 1051\u20131054. IEEE (2022)","DOI":"10.1109\/IGARSS46834.2022.9883441"},{"key":"26_CR23","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1016\/j.rse.2011.11.026","volume":"120","author":"M Drusch","year":"2012","unstructured":"Drusch, M., et al.: Sentinel-2: esa\u2019s optical high-resolution mission for GMES operational services. Remote Sens. Environ. 120, 25\u201336 (2012)","journal-title":"Remote Sens. Environ."},{"key":"26_CR24","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1016\/j.rse.2017.06.031","volume":"202","author":"N Gorelick","year":"2017","unstructured":"Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., Moore, R.: Google earth engine: planetary-scale geospatial analysis for everyone. Remote Sens. Environ. 202, 18\u201327 (2017)","journal-title":"Remote Sens. Environ."},{"key":"26_CR25","doi-asserted-by":"publisher","DOI":"10.1016\/j.rse.2022.113192","volume":"280","author":"S Hafner","year":"2022","unstructured":"Hafner, S., Ban, Y., Nascetti, A.: Unsupervised domain adaptation for global urban extraction using sentinel-1 sar and sentinel-2 msi data. Remote Sens. Environ. 280, 113192 (2022)","journal-title":"Remote Sens. Environ."},{"key":"26_CR26","doi-asserted-by":"crossref","unstructured":"Daudt, R.C., Le Saux, B., Boulch, A., Gousseau, Y.: Urban change detection for multispectral earth observation using convolutional neural networks. In: IGARSS 2018\u20132018 IEEE International Geoscience and Remote Sensing Symposium, pp. 2115\u20132118. IEEE (2018)","DOI":"10.1109\/IGARSS.2018.8518015"},{"issue":"10","key":"26_CR27","doi-asserted-by":"publisher","first-page":"3416","DOI":"10.1109\/TGRS.2009.2022633","volume":"47","author":"C Benedek","year":"2009","unstructured":"Benedek, C., Szir\u00e1nyi, T.: Change detection in optical aerial images by a multilayer conditional mixed markov model. IEEE Trans. Geosci. Remote Sens. 47(10), 3416\u20133430 (2009)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"issue":"2","key":"26_CR28","doi-asserted-by":"publisher","first-page":"282","DOI":"10.1016\/j.compag.2008.03.009","volume":"63","author":"GE Meyer","year":"2008","unstructured":"Meyer, G.E., Neto, J.C.: Verification of color vegetation indices for automated crop imaging applications. Comput. Electron. Agric. 63(2), 282\u2013293 (2008)","journal-title":"Comput. Electron. Agric."},{"key":"26_CR29","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"26_CR30","doi-asserted-by":"crossref","unstructured":"Ailimujiang, G., Jiaermuhamaiti, Y., Jumahong, H., Wang, H., Zhu, S., Nurmamaiti, P.: A transformer-based network for change detection in remote sensing using multiscale difference-enhancement. Comput. Intell. Neurosci. 2022 (2022)","DOI":"10.1155\/2022\/2189176"}],"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\/978-981-99-8462-6_26","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,12,25]],"date-time":"2023-12-25T19:05:32Z","timestamp":1703531132000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-99-8462-6_26"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,26]]},"ISBN":["9789819984619","9789819984626"],"references-count":30,"URL":"https:\/\/doi.org\/10.1007\/978-981-99-8462-6_26","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023,12,26]]},"assertion":[{"value":"26 December 2023","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":"Xiamen","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":"13 October 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 October 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ccprcv2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/prcv2023.xmu.edu.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":"Microsoft CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1420","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":"532","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":"37% - 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":"3,78","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":"3,69","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)"}}]}}