{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,5]],"date-time":"2026-01-05T22:21:17Z","timestamp":1767651677583,"version":"3.37.3"},"reference-count":63,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2024,11,20]],"date-time":"2024-11-20T00:00:00Z","timestamp":1732060800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,11,20]],"date-time":"2024-11-20T00:00:00Z","timestamp":1732060800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Key Research and Development and Promotion Program of Henan Province","award":["232102210115"],"award-info":[{"award-number":["232102210115"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2025,1]]},"DOI":"10.1007\/s00521-024-10666-5","type":"journal-article","created":{"date-parts":[[2024,11,20]],"date-time":"2024-11-20T09:38:15Z","timestamp":1732095495000},"page":"1429-1456","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["A feature enhancement network combining UNet and vision transformer for building change detection in high-resolution remote sensing images"],"prefix":"10.1007","volume":"37","author":[{"given":"Yu","family":"Sun","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yujuan","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4419-7023","authenticated-orcid":false,"given":"Xianwei","family":"Han","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Gao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yunliang","family":"Hu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yimin","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,11,20]]},"reference":[{"issue":"6","key":"10666_CR1","doi-asserted-by":"publisher","first-page":"989","DOI":"10.1080\/01431168908903939","volume":"10","author":"A Singh","year":"1989","unstructured":"Singh A (1989) Review article digital change detection techniques using remotely-sensed data. Int J Remote Sens 10(6):989\u20131003","journal-title":"Int J Remote Sens"},{"issue":"1","key":"10666_CR2","doi-asserted-by":"publisher","first-page":"2257978","DOI":"10.1080\/15481603.2023.2257978","volume":"60","author":"Babak Mohammadi","year":"2023","unstructured":"Mohammadi Babak, Pilesj\u00f6 Petter, Duan Zheng (2023) The superiority of the adjusted normalized difference snow index (ANDSI) for mapping glaciers using Sentinel-2 multispectral satellite imagery. GIScience Remote Sens 60(1):2257978","journal-title":"GIScience Remote Sens"},{"key":"10666_CR3","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1016\/j.comnet.2015.12.023","volume":"101","author":"MM Rathore","year":"2016","unstructured":"Rathore MM, Ahmad A, Paul A, Rho S (2016) Urban planning and building smart cities based on the Internet of Things using Big Data analytics. Comput Netw 101:63\u201380","journal-title":"Comput Netw"},{"key":"10666_CR4","doi-asserted-by":"crossref","unstructured":"Jing-Fa Z, Li-li X, Xia-xin T (2003) Change detection of earthquake-damaged buildings on remote sensing image and its application in seismic disaster assessment. In: Proceedings IEEE International Geoscience Remote Sensing Symposium (IGARSS) Toulouse, France USA 4: 2436\u20132438","DOI":"10.1109\/IGARSS.2003.1294467"},{"issue":"10","key":"10666_CR5","doi-asserted-by":"publisher","first-page":"1688","DOI":"10.3390\/rs12101688","volume":"12","author":"W Shi","year":"2020","unstructured":"Shi W, Zhang M, Zhang R, Chen S, Zhan Z (2020) Change detection based on artificial intelligence: state-of-the-art and challenges. Remote Sens 12(10):1688","journal-title":"Remote Sens"},{"key":"10666_CR6","doi-asserted-by":"publisher","first-page":"126776","DOI":"10.1016\/j.jclepro.2021.126776","volume":"298","author":"L Zhang","year":"2021","unstructured":"Zhang L, Huang Z, Liu W, Guo Z, Zhang Z (2021) Weather radar echo prediction method based on convolution neural network and long short-term memory networks for sustainable e-agriculture. J Clean Prod 298:126776","journal-title":"J Clean Prod"},{"issue":"2","key":"10666_CR7","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1016\/S0034-4257(97)00112-0","volume":"63","author":"MK Ridd","year":"1998","unstructured":"Ridd MK, Liu J (1998) A comparison of four algorithms for change detection in an urban environment. Remote Sens Environ 63(2):95\u2013100","journal-title":"Remote Sens Environ"},{"issue":"3","key":"10666_CR8","doi-asserted-by":"publisher","first-page":"294","DOI":"10.1109\/TIP.2004.838698","volume":"14","author":"RJ Radke","year":"2005","unstructured":"Radke RJ, Andra S, Al-Kofahi O, Roysam B (2005) Image change detection algorithms: a systematic survey. IEEE Trans Image Process 14(3):294\u2013307","journal-title":"IEEE Trans Image Process"},{"issue":"1","key":"10666_CR9","doi-asserted-by":"publisher","first-page":"145","DOI":"10.1109\/TGRS.2008.2002695","volume":"47","author":"M Chini","year":"2009","unstructured":"Chini M, Pierdicca N, Emery WJ (2009) Exploiting SAR and VHR optical images to quantify damage caused by the 2003 Bam earthquake. IEEE Trans Geosci Remote Sens 47(1):145\u2013152","journal-title":"IEEE Trans Geosci Remote Sens"},{"issue":"4","key":"10666_CR10","doi-asserted-by":"publisher","first-page":"046019","DOI":"10.1117\/1.JRS.10.046019","volume":"10","author":"F Gao","year":"2016","unstructured":"Gao F, Dong J, Li B, Xu Q, Xie C (2016) Change detection from synthetic aperture radar images based on neighborhood-based ratio and extreme learning machine. J Appl Remote Sens 10(4):046019","journal-title":"J Appl Remote Sens"},{"issue":"2","key":"10666_CR11","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1109\/MGRS.2016.2540798","volume":"4","author":"L Zhang","year":"2016","unstructured":"Zhang L, Zhang L, Du B (2016) Deep learning for remote sensing data: a technical tutorial on the state of the art. IEEE Geosci Remote Sen M 4(2):22\u201340","journal-title":"IEEE Geosci Remote Sen M"},{"key":"10666_CR12","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2023.3282935","author":"Z Lv","year":"2023","unstructured":"Lv Z, Huang H, Sun W, Jia M, Benediktsson JA, Chen F (2023) Iterative training sample augmentation for enhancing land cover change detection performance with deep learning neural network. IEEE Trans Neural Netw Learn Syst. https:\/\/doi.org\/10.1109\/TNNLS.2023.3282935","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"6","key":"10666_CR13","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1145\/3065386","volume":"60","author":"A Krizhevsky","year":"2017","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2017) ImageNet classification with deep convolutional neural networks. Commun ACM 60(6):84\u201390","journal-title":"Commun ACM"},{"key":"10666_CR14","doi-asserted-by":"crossref","unstructured":"Deng J, Dong W, Socher R, Li L, Li K, FeiFei L (2009) ImageNet: a large-scale hierarchical image database. In: 2009 IEEE conference on computer vision and pattern recognition, pp. 248\u2013255","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"10666_CR15","first-page":"1","volume":"20","author":"Z Lv","year":"2023","unstructured":"Lv Z, Zhong P, Wang W, You Z, Falco N (2023) Multiscale attention network guided with change gradient image for land cover change detection using remote sensing images. IEEE Geosci Remote Sens Lett 20:1\u20135","journal-title":"IEEE Geosci Remote Sens Lett"},{"key":"10666_CR16","first-page":"1","volume":"61","author":"Z Lv","year":"2023","unstructured":"Lv Z, Zhang P, Sun W, Benediktsson JA, Li J, Wang W (2023) Novel adaptive region spectral-spatial features for land cover classification with high spatial resolution remotely sensed imagery. IEEE Trans Geosci Remote Sens 61:1\u201312","journal-title":"IEEE Trans Geosci Remote Sens"},{"issue":"5","key":"10666_CR17","doi-asserted-by":"publisher","first-page":"847","DOI":"10.3390\/rs13050847","volume":"13","author":"W Huang","year":"2021","unstructured":"Huang W, Li G, Chen Q, Ju M, Qu J (2021) CF2PN: a cross-scale feature fusion pyramid network based remote sensing target detection. Remote Sens 13(5):847","journal-title":"Remote Sens"},{"key":"10666_CR18","first-page":"1","volume":"61","author":"Z Lv","year":"2023","unstructured":"Lv Z, Zhong P, Wang W, You Z, Benediktsson JA, Shi C (2023) Novel piecewise distance based on adaptive region key-points extraction for LCCD with VHR remote-sensing images. IEEE Trans Geosci Remote Sens 61:1\u20139","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"10666_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TGRS.2022.3231215","volume":"60","author":"L Sun","year":"2022","unstructured":"Sun L, Zhao G, Zheng Y, Wu Z (2022) Spectral-spatial feature tokenization transformer for hyperspectral image classification. IEEE Trans Geosci Remote Sens 60:1\u201314","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"10666_CR20","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TGRS.2022.3231215","volume":"60","author":"L Sun","year":"2022","unstructured":"Sun L, Fang Y, Chen Y, Huang W, Wu Z, Jeon B (2022) Multi-structure KELM with attention fusion strategy for hyperspectral image classification. IEEE Trans Geosci Remote Sens 60:1\u201317","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"10666_CR21","doi-asserted-by":"publisher","first-page":"719","DOI":"10.1109\/JSTARS.2023.3333959","volume":"17","author":"Z Du","year":"2024","unstructured":"Du Z, Li X, Miao J, Huang Y, Shen H, Zhang L (2024) Concatenated deep-learning framework for multitask change detection of optical and SAR images. IEEE J Select Topics Appl Earth Obs Remote Sens 17:719\u2013731","journal-title":"IEEE J Select Topics Appl Earth Obs Remote Sens"},{"key":"10666_CR22","doi-asserted-by":"crossref","unstructured":"Zhang Y, Qiu Z, Yao T, Liu D, Mei T (2018) Fully convolutional adaptation networks for semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 6810\u20136818","DOI":"10.1109\/CVPR.2018.00712"},{"key":"10666_CR23","doi-asserted-by":"crossref","unstructured":"Long J, Shelhamer E, Darrell T (2015) Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 3431\u20133440","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"10666_CR24","doi-asserted-by":"publisher","first-page":"565","DOI":"10.5194\/isprs-archives-XLII-2-565-2018","volume":"42","author":"MA Lebedev","year":"2018","unstructured":"Lebedev MA, Vizilter YV, Vygolov OV, Knyaz VA, Rubis AY (2018) Change detection in remote sensing images using conditional adversarial networks. Int Arch Photogramm Remote Sens Spatial Inf Sci 42:565\u2013571","journal-title":"Int Arch Photogramm Remote Sens Spatial Inf Sci"},{"key":"10666_CR25","unstructured":"Zhang A, Liu X, Gros A, Tiecke T (2017) Building detection from satellite images on a global scale. https:\/\/arxiv.org\/abs\/1707.08952v1"},{"key":"10666_CR26","doi-asserted-by":"crossref","unstructured":"Ronneberger O, Fischer P, Brox T (2015) U-Net: convolutional networks for biomedical image segmentation. In: Medical image computing and computer-assisted intervention\u2013MICCAI 2015: 18th international conference, pp. 234\u2013241","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"10666_CR27","doi-asserted-by":"crossref","unstructured":"Daudt RC, Saux BL, Boulch A (2018) Fully convolutional Siamese networks for change detection. In: 2018 25th IEEE international conference on image processing (ICIP), pp. 4063\u20134067","DOI":"10.1109\/ICIP.2018.8451652"},{"issue":"11","key":"10666_CR28","doi-asserted-by":"publisher","first-page":"1382","DOI":"10.3390\/rs11111382","volume":"11","author":"D Peng","year":"2019","unstructured":"Peng D, Zhang Y, Guan H (2019) End-to-end change detection for high resolution satellite images using improved UNet++. Remote Sens 11(11):1382","journal-title":"Remote Sens"},{"key":"10666_CR29","doi-asserted-by":"publisher","unstructured":"Oktay O et al. (2018) Attention U-Net: learning where to look for the pancreas. https:\/\/doi.org\/10.48550\/arXiv.1804.03999","DOI":"10.48550\/arXiv.1804.03999"},{"key":"10666_CR30","doi-asserted-by":"crossref","unstructured":"Howard A, Pang R, Adam H, Le QV, Sandler M, Chen B, Wang W, Chen L, Tan M, Chu G, Vasudevan V, Zhu Y (2019) Searching for MobileNetV3. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp. 1314\u20131324","DOI":"10.1109\/ICCV.2019.00140"},{"issue":"4","key":"10666_CR31","doi-asserted-by":"publisher","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","volume":"40","author":"LC Chen","year":"2018","unstructured":"Chen LC, Papandreou G, Kokkinos I, Murphy K, Yuille AL (2018) DeepLab: semantic image segmentation with deep convolutional nets, Atrous convolution, and fully connected CRFs. IEEE Trans Pattern Anal Mach Intell 40(4):834\u2013848","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"10666_CR32","first-page":"102465","volume":"103","author":"D Peng","year":"2021","unstructured":"Peng D, Bruzzone L, Zhang Y, Guan H, He P (2021) SCDNET: a novel convolutional network for semantic change detection in high resolution optical remote sensing imagery. Int J Appl Earth Obs Geoinf 103:102465","journal-title":"Int J Appl Earth Obs Geoinf"},{"key":"10666_CR33","doi-asserted-by":"crossref","unstructured":"Ronneberger O, Fischer P, Brox T (2015) U-Net: convolutional networks for biomedical image segmentation. In: Medical image computing and computer-assisted intervention\u2013MICCAI 2015: 18th international conference, Munich, Germany, pp. 234-241","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"10666_CR34","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"10666_CR35","doi-asserted-by":"publisher","first-page":"8395","DOI":"10.1109\/JSTARS.2023.3310208","volume":"16","author":"C Han","year":"2023","unstructured":"Han C, Wu C, Guo H, Hu M, Li J, Chen H (2023) Change guiding network: incorporating change prior to guide change detection in remote sensing imagery. IEEE J Select Topics Appl Earth Obs Remote Sens 16:8395\u20138407","journal-title":"IEEE J Select Topics Appl Earth Obs Remote Sens"},{"key":"10666_CR36","unstructured":"Vaswani A et al. (2017) Attention is all you need. In: 31st conference on neural information processing systems (NIPS 2017), 30: 5998\u20136008"},{"key":"10666_CR37","first-page":"12077","volume":"34","author":"E Xie","year":"2021","unstructured":"Xie E, Wang W, Yu Z, Anandkumar A, Alvarez JM, Luo P (2021) SegFormer: simple and efficient design for semantic segmentation with transformers. Neural Inf Process Syst 34:12077\u201312090","journal-title":"Neural Inf Process Syst"},{"key":"10666_CR38","unstructured":"Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai X, Unterthiner T, Dehghani M, Minderer M, Heigold G, Gelly S, Uszkoreit J, Houlsby N (2020) An iImage is worth 16 $$\\times $$ 16 words: transformers for image recognition at scale. https:\/\/arxiv.org\/abs\/2010.11929"},{"key":"10666_CR39","doi-asserted-by":"crossref","unstructured":"Liu Z, Lin Y, Cao Y, Hu H, Wei Y, Zhang Z, Lin S, Guo B (2021) Swin transformer: hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp. 10012\u201310022","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"10666_CR40","unstructured":"Mehta S, Rastegari M (2021) MobileViT: light-weight, general-purpose, and mobile-friendly vision transformer. https:\/\/arxiv.org\/abs\/2110.02178"},{"issue":"11","key":"10666_CR41","doi-asserted-by":"publisher","first-page":"2611","DOI":"10.3390\/rs14112611","volume":"14","author":"X Xiao","year":"2022","unstructured":"Xiao X, Guo W, Chen R, Hui Y, Wang J, Zhao H (2022) A Swin transformer-based encoding booster integrated in U-shaped network for building extraction. Remote Sens 14(11):2611","journal-title":"Remote Sens"},{"issue":"9","key":"10666_CR42","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 (2022) A network combining a transformer and a convolutional neural network for remote sensing image change detection. Remote Sens 14(9):2228","journal-title":"Remote Sens"},{"key":"10666_CR43","unstructured":"Mnih V, Heess N, Graves A, Kavukcuoglu K (2014) Recurrent models of visual attention. In: Proceedings of the 27th International conference on neural information processing systems 27, 2204\u20132212"},{"key":"10666_CR44","doi-asserted-by":"crossref","unstructured":"Woo S, Park J, Lee J-Y, Kweon IS (2018) CBAM: convolutional block attention module. In: Proceedings of the European conference on computer vision (ECCV) pp. 3\u201319","DOI":"10.1007\/978-3-030-01234-2_1"},{"issue":"7","key":"10666_CR45","doi-asserted-by":"publisher","first-page":"884","DOI":"10.3390\/rs11070884","volume":"11","author":"L Wang","year":"2019","unstructured":"Wang L, Peng J, Sun W (2019) Spatial-spectral squeeze-and-excitation residual network for hyperspectral image classification. Remote Sens 11(7):884","journal-title":"Remote Sens"},{"key":"10666_CR46","doi-asserted-by":"crossref","unstructured":"Wang F et al. (2017) Residual attention network for image classification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 3156\u20133164","DOI":"10.1109\/CVPR.2017.683"},{"key":"10666_CR47","doi-asserted-by":"crossref","unstructured":"Fu J et al. (2019) Dual attention network for scene segmentation. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp. 3146\u20133154","DOI":"10.1109\/CVPR.2019.00326"},{"key":"10666_CR48","first-page":"102591","volume":"105","author":"Q Ding","year":"2021","unstructured":"Ding Q, Shao Z, Huang X, Altan O (2021) DSA-Net: a novel deeply supervised attention-guided network for building change detection in high-resolution remote sensing images. Int J Appl Earth Obs Geoinf 105:102591","journal-title":"Int J Appl Earth Obs Geoinf"},{"key":"10666_CR49","first-page":"102597","volume":"105","author":"L Song","year":"2021","unstructured":"Song L, Xia M, Jin J, Qian M, Zhang Y (2021) SUACDNet: attentional change detection network based on Siamese U-shaped structure. Int J Appl Earth Obs Geoinf 105:102597","journal-title":"Int J Appl Earth Obs Geoinf"},{"key":"10666_CR50","unstructured":"Baldi P, Sadowski P (2013) Understanding dropout. In: Proceedings of the 27th International conference on neural information processing systems 26: 2814\u20132822"},{"key":"10666_CR51","doi-asserted-by":"crossref","unstructured":"Sudre CH, Li W, Vercauteren T, Ourselin S, Jorge Cardoso M (2017) Generalised dice overlap as a deep learning loss function for highly unbalanced segmentations. In: Deep learning in medical image analysis and multimodal learning for clinical decision support: third international workshop, DLMIA 2017, and 7th international workshop, ML-CDS pp. 240\u2013248","DOI":"10.1007\/978-3-319-67558-9_28"},{"issue":"2","key":"10666_CR52","doi-asserted-by":"publisher","first-page":"318","DOI":"10.1109\/TPAMI.2018.2858826","volume":"42","author":"TY Lin","year":"2020","unstructured":"Lin TY, Goyal P, Girshick R, He K, Doll\u00e1r P (2020) Focal loss for dense object detection. IEEE Trans Pattern Anal Mach Intell 42(2):318\u2013327","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"10666_CR53","first-page":"1","volume":"19","author":"X Li","year":"2021","unstructured":"Li X, He M, Li H, Shen H (2021) A combined loss-based multiscale fully convolutional network for high-resolution remote sensing image change detection. IEEE Geosci Remote Sens Lett 19:1\u20135","journal-title":"IEEE Geosci Remote Sens Lett"},{"issue":"10","key":"10666_CR54","doi-asserted-by":"publisher","first-page":"1662","DOI":"10.3390\/rs12101662","volume":"12","author":"H Chen","year":"2020","unstructured":"Chen H, Shi Z (2020) A spatial-temporal attention-based method and a new dataset for remote sensing image change detection. Remote Sens 12(10):1662","journal-title":"Remote Sens"},{"issue":"1","key":"10666_CR55","doi-asserted-by":"publisher","first-page":"574","DOI":"10.1109\/TGRS.2018.2858817","volume":"57","author":"S Ji","year":"2019","unstructured":"Ji S, Wei S, Lu M (2019) Fully convolutional networks for multisource building extraction from an open aerial and satellite imagery data set. IEEE Trans Geosci Remote Sens 57(1):574\u2013586","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"10666_CR56","doi-asserted-by":"publisher","first-page":"565","DOI":"10.5194\/isprs-archives-XLII-2-565-2018","volume":"42","author":"MA Lebedev","year":"2018","unstructured":"Lebedev MA, Vizilter YV, Vygolov OV, Knyaz VA, Rubis AY (2018) Change detection in remote sensing images using conditional adversarial networks. Int Arch Photogramm Remote Sens Spatial Inf Sci 42:565\u2013571","journal-title":"Int Arch Photogramm Remote Sens Spatial Inf Sci"},{"issue":"5","key":"10666_CR57","doi-asserted-by":"publisher","first-page":"811","DOI":"10.1109\/LGRS.2020.2988032","volume":"18","author":"Y Liu","year":"2021","unstructured":"Liu Y, Pang C, Zhan Z, Zhang X, Yang X (2021) Building change detection for remote sensing images using a dual-task constrained deep Siamese convolutional network model. IEEE Geosci Remote Sens Lett 18(5):811\u2013815","journal-title":"IEEE Geosci Remote Sens Lett"},{"key":"10666_CR58","first-page":"1","volume":"19","author":"S Fang","year":"2022","unstructured":"Fang S, Li K, Shao J, Li Z (2022) SNUNET-CD: a densely connected Siamese network for change detection of VHR images. IEEE Geosci Remote Sens Lett 19:1\u20135","journal-title":"IEEE Geosci Remote Sens Lett"},{"key":"10666_CR59","first-page":"1","volume":"60","author":"H Chen","year":"2022","unstructured":"Chen H, Qi Z, Shi Z (2022) Remote sensing image change detection with transformers. IEEE Trans Geosci Remote Sens 60:1\u201314","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"10666_CR60","doi-asserted-by":"crossref","unstructured":"Bandara WGC, Patel VM (2022) A transformer-based Siamese network for change detection. In: IGARSS 2022-2022 IEEE International geoscience and remote sensing symposium pp. 207\u2013210","DOI":"10.1109\/IGARSS46834.2022.9883686"},{"key":"10666_CR61","doi-asserted-by":"publisher","unstructured":"Guo Q, Wang R, Huang R, Sun S, Zhang Y (2022) IDET: iterative difference-enhanced transformers for high-quality change detection. https:\/\/doi.org\/10.48550\/arXiv.2207.09240","DOI":"10.48550\/arXiv.2207.09240"},{"key":"10666_CR62","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TGRS.2023.3335359","volume":"61","author":"T Lei","year":"2023","unstructured":"Lei T, Geng X, Ning H, Lv Z, Gong M, Jin Y, Nandi AK (2023) Ultralightweight spatial-spectral feature cooperation network for change detection in remote sensing images. IEEE Trans Geosci Remote Sens 61:1\u201314","journal-title":"IEEE Trans Geosci Remote Sens"},{"issue":"3","key":"10666_CR63","first-page":"1688","volume":"27","author":"UH Atasever","year":"2018","unstructured":"Atasever UH, Gunen MA, Besdok E (2018) A new unsupervised change detection approach based on PCA based blocking and GMM clustering for detecting flood damage. Fresenius Environ Bull 27(3):1688\u20131694","journal-title":"Fresenius Environ Bull"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-024-10666-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-024-10666-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-024-10666-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,24]],"date-time":"2025-01-24T04:31:08Z","timestamp":1737693068000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-024-10666-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,20]]},"references-count":63,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2025,1]]}},"alternative-id":["10666"],"URL":"https:\/\/doi.org\/10.1007\/s00521-024-10666-5","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"type":"print","value":"0941-0643"},{"type":"electronic","value":"1433-3058"}],"subject":[],"published":{"date-parts":[[2024,11,20]]},"assertion":[{"value":"20 December 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 October 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 November 2024","order":3,"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 that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to participate"}},{"value":"This manuscript is approved by all authors for publication.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}}]}}