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The superior performance and robustness of various contemporary models suggest that rapid development of BCD in the deep learning age is being witnessed. However, challenges abound, particularly due to the diverse nature of targets in urban settings, intricate city backgrounds, and the presence of obstructions, such as trees and shadows, when using very high-resolution (VHR) remote sensing images. To overcome the shortcomings of information loss and lack of feature extraction ability, this paper introduces a Siamese Multiscale Attention Decoding Network (SMADNet). This network employs the Multiscale Context Feature Fusion Module (MCFFM) to amalgamate contextual information drawn from multiscale target, weakening the heterogeneity between raw image features and difference features. Additionally, our method integrates a Dual Contextual Attention Decoding Module (CADM) to identify spatial and channel relations amongst features. For enhanced accuracy, a Deep Supervision (DS) strategy is deployed to enhance the ability to extract more features for middle layers. Comprehensive experiments on three benchmark datasets, i.e., GDSCD, LEVIR-CD, and HRCUS-CD, establish the superiority of SMADNet over seven other state-of-the-art (SOTA) algorithms.<\/jats:p>","DOI":"10.3390\/rs15215127","type":"journal-article","created":{"date-parts":[[2023,10,27]],"date-time":"2023-10-27T09:56:36Z","timestamp":1698400596000},"page":"5127","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A Siamese Multiscale Attention Decoding Network for Building Change Detection on High-Resolution Remote Sensing Images"],"prefix":"10.3390","volume":"15","author":[{"given":"Yao","family":"Chen","sequence":"first","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China"}]},{"given":"Jindou","family":"Zhang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China"}]},{"given":"Zhenfeng","family":"Shao","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4323-382X","authenticated-orcid":false,"given":"Xiao","family":"Huang","sequence":"additional","affiliation":[{"name":"Department of Environmental Sciences, Emory University, Atlanta, GA 30322, USA"}]},{"given":"Qing","family":"Ding","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China"}]},{"given":"Xianyi","family":"Li","sequence":"additional","affiliation":[{"name":"Zhuhai Obit Satellite Big Data Co., Ltd., Zhuhai 519082, China"},{"name":"School of Marine Sciences, Sun Yat-sen University, Zhuhai 519082, China"}]},{"given":"Youju","family":"Huang","sequence":"additional","affiliation":[{"name":"Guangxi Zhuang Autonomous Region Institute of Natural Resources Remote Sensing, Nanning 530200, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,10,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"989","DOI":"10.1080\/01431168908903939","article-title":"Review article digital change detection techniques using remotely-sensed data","volume":"10","author":"Singh","year":"1989","journal-title":"Int. J. Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"300","DOI":"10.1109\/TGRS.2012.2195727","article-title":"Updating land-cover maps by classification of image time series: A novel change-detection-driven transfer learning approach","volume":"51","author":"Demir","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1016\/j.isprsjprs.2003.09.007","article-title":"Object-based classification of remote sensing data for change detection","volume":"58","author":"Walter","year":"2004","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Jimenez-Sierra, D.A., Ben\u00edtez-Restrepo, H.D., Vargas-Cardona, H.D., and Chanussot, J. (2020). Graph-based data fusion applied to: Change detection and biomass estimation in rice crops. Remote Sens., 12.","DOI":"10.3390\/rs12172683"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1109\/TNNLS.2015.2435783","article-title":"Change detection in synthetic aperture radar images based on deep neural networks","volume":"27","author":"Gong","year":"2015","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"574","DOI":"10.1109\/TGRS.2018.2858817","article-title":"Fully convolutional networks for multisource building extraction from an open aerial and satellite imagery data set","volume":"57","author":"Ji","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"108717","DOI":"10.1016\/j.patcog.2022.108717","article-title":"HFA-Net: High frequency attention siamese network for building change detection in VHR remote sensing images","volume":"129","author":"Zheng","year":"2022","journal-title":"Pattern Recognit."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1007\/s12145-019-00380-5","article-title":"Change detection techniques for remote sensing applications: A survey","volume":"12","author":"Asokan","year":"2019","journal-title":"Earth Sci. Inform."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Awrangjeb, M., Gilani, S.A.N., and Siddiqui, F.U. (2018). An effective data-driven method for 3-d building roof reconstruction and robust change detection. Remote Sens., 10.","DOI":"10.3390\/rs10101512"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Zhu, Q., Guo, X., Li, Z., and Li, D. (2022). A review of multi-class change detection for satellite remote sensing imagery. Geo-Spat. Inf. Sci., 1\u201315.","DOI":"10.1080\/10095020.2022.2128902"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.isprsjprs.2013.03.006","article-title":"Change detection from remotely sensed images: From pixel-based to object-based approaches","volume":"80","author":"Hussain","year":"2013","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1109\/LGRS.2017.2763182","article-title":"Object-based change detection for VHR images based on multiscale uncertainty analysis","volume":"15","author":"Zhang","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"2440","DOI":"10.1109\/JSTARS.2018.2817121","article-title":"High-resolution remote sensing image change detection by statistical-object-based method","volume":"11","author":"Zhang","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1016\/j.isprsjprs.2016.08.010","article-title":"Description and validation of a new set of object-based temporal geostatistical features for land-use\/land-cover change detection","volume":"121","author":"Ruiz","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"6723","DOI":"10.1080\/01431161.2013.805282","article-title":"Object-based land cover change detection for cross-sensor images","volume":"34","author":"Qin","year":"2013","journal-title":"Int. J. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Ma, L., Li, M., Blaschke, T., Ma, X., Tiede, D., Cheng, L., Chen, Z., and Chen, D. (2016). Object-based change detection in urban areas: The effects of segmentation strategy, scale, and feature space on unsupervised methods. Remote Sens., 8.","DOI":"10.3390\/rs8090761"},{"key":"ref_17","unstructured":"Bai, T., Wang, L., Yin, D., Sun, K., Chen, Y., Li, W., and Li, D. (2022). Deep learning for change detection in remote sensing: A review. Geo-Spat. Inf. Sci., 1\u201327."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"4823","DOI":"10.1080\/01431160801950162","article-title":"PCA-based land-use change detection and analysis using multitemporal and multisensor satellite data","volume":"29","author":"Deng","year":"2008","journal-title":"Int. J. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1016\/j.rse.2017.07.009","article-title":"A post-classification change detection method based on iterative slow feature analysis and Bayesian soft fusion","volume":"199","author":"Wu","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1016\/j.isprsjprs.2019.04.015","article-title":"Deep learning in remote sensing applications: A meta-analysis and review","volume":"152","author":"Ma","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"923","DOI":"10.1080\/2150704X.2018.1492172","article-title":"Change detection based on Faster R-CNN for high-resolution remote sensing images","volume":"9","author":"Wang","year":"2018","journal-title":"Remote Sens. Lett."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"4205","DOI":"10.1109\/JSTARS.2021.3070368","article-title":"On creating benchmark dataset for aerial image interpretation: Reviews, guidances, and million-aid","volume":"14","author":"Long","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_23","first-page":"102591","article-title":"DSA-Net: A novel deeply supervised attention-guided network for building change detection in high-resolution remote sensing images","volume":"105","author":"Ding","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Daudt, R.C., Le Saux, B., Boulch, A., and Gousseau, Y. (2018, January 22\u201327). Urban change detection for multispectral earth observation using convolutional neural networks. Proceedings of the IGARSS 2018\u20142018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain.","DOI":"10.1109\/IGARSS.2018.8518015"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., and Darrell, T. (2015, January 7\u201312). Fully convolutional networks for semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref_26","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015). Medical Image Computing and Computer-Assisted Intervention\u2014MICCAI 2015, Proceedings of the 18th International Conference, Munich, Germany, 5\u20139 October 2015, Springer International Publishing. Proceedings, Part III 18."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Ji, S., Shen, Y., Lu, M., and Zhang, Y. (2019). Building instance change detection from large-scale aerial images using convolutional neural networks and simulated samples. Remote Sens., 11.","DOI":"10.3390\/rs11111343"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1845","DOI":"10.1109\/LGRS.2017.2738149","article-title":"Change detection based on deep siamese convolutional network for optical aerial images","volume":"14","author":"Zhan","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"811","DOI":"10.1109\/LGRS.2020.2988032","article-title":"Building change detection for remote sensing images using a dual-task constrained deep siamese convolutional network model","volume":"18","author":"Liu","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_30","unstructured":"Daudt, R.C., Le Saux, B., and Boulch, A. (2018, January 7\u201310). Fully convolutional siamese networks for change detection. Proceedings of the 2018 25th IEEE International Conference on Image Processing (ICIP), Athens, Greece."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Zhao, H., Shi, J., Qi, X., Wang, X., and Jia, J. (2017, January 21\u201326). Pyramid scene parsing network. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.660"},{"key":"ref_32","unstructured":"Chen, L.C., Papandreou, G., Schroff, F., and Adam, H. (2017). Rethinking atrous convolution for semantic image segmentation. arXiv."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Varghese, A., Gubbi, J., Ramaswamy, A., and Balamuralidhar, P. (2018, January 8\u201314). ChangeNet: A deep learning architecture for visual change detection. Proceedings of the European Conference on Computer Vision (ECCV) Workshops, Munich, Germany.","DOI":"10.1007\/978-3-030-11012-3_10"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"3376","DOI":"10.1109\/TIP.2021.3060167","article-title":"CDNet: Complementary depth network for RGB-D salient object detection","volume":"30","author":"Jin","year":"2021","journal-title":"IEEE Trans. Image Process."},{"key":"ref_35","unstructured":"Zhou, Z., Rahman Siddiquee, M.M., Tajbakhsh, N., and Liang, J. (2018). Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, Proceedings of the 4th International Workshop, DLMIA 2018, and 8th International Workshop, ML-CDS 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, 20 September 2018, Springer International Publishing. Proceedings 4."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Peng, D., Zhang, Y., and Guan, H. (2019). End-to-end change detection for high resolution satellite images using improved UNet++. Remote Sens., 11.","DOI":"10.3390\/rs11111382"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1016\/j.isprsjprs.2021.03.005","article-title":"CLNet: Cross-layer convolutional neural network for change detection in optical remote sensing imagery","volume":"175","author":"Zheng","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_38","first-page":"1","article-title":"A deeply supervised attention metric-based network and an open aerial image dataset for remote sensing change detection","volume":"60","author":"Shi","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1194","DOI":"10.1109\/JSTARS.2020.3037893","article-title":"DASNet: Dual attentive fully convolutional Siamese networks for change detection in high-resolution satellite images","volume":"14","author":"Chen","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_40","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, \u0141., and Polosukhin, I. (2017). Attention is all you need. Adv. Neural Inf. Process. Syst., 30."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Bandara, W.G.C., and Patel, V.M. (2022, January 17\u201322). A transformer-based siamese network for change detection. Proceedings of the IGARSS 2022\u20142022 IEEE International Geoscience and Remote Sensing Symposium, Kuala Lumpur, Malaysia.","DOI":"10.1109\/IGARSS46834.2022.9883686"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Wang, D., Chen, X., Guo, N., Yi, H., and Li, Y. (2023). STCD: Efficient Siamese transformers-based change detection method for remote sensing images. Geo-Spat. Inf. Sci., 1\u201320.","DOI":"10.1080\/10095020.2022.2157762"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Shi, W., Zhang, M., Zhang, R., Chen, S., and Zhan, Z. (2020). Change detection based on artificial intelligence: State-of-the-art and challenges. Remote Sens., 12.","DOI":"10.3390\/rs12101688"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"15389","DOI":"10.1109\/ACCESS.2018.2889326","article-title":"Local descriptor learning for change detection in synthetic aperture radar images via convolutional neural networks","volume":"7","author":"Dong","year":"2018","journal-title":"IEEE Access"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"212","DOI":"10.1016\/j.isprsjprs.2017.05.001","article-title":"Feature learning and change feature classification based on deep learning for ternary change detection in SAR images","volume":"129","author":"Gong","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"5891","DOI":"10.1109\/TGRS.2020.3011913","article-title":"SemiCDNet: A semisupervised convolutional neural network for change detection in high resolution remote-sensing images","volume":"59","author":"Peng","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Chen, H., and Shi, Z. (2020). A spatial-temporal attention-based method and a new dataset for remote sensing image change detection. Remote Sens., 12.","DOI":"10.3390\/rs12101662"},{"key":"ref_48","first-page":"5617116","article-title":"AERNet: An attention-guided edge refinement network and a dataset for remote sensing building change detection","volume":"61","author":"Zhang","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sensing"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/21\/5127\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T21:12:28Z","timestamp":1760130748000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/21\/5127"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,26]]},"references-count":49,"journal-issue":{"issue":"21","published-online":{"date-parts":[[2023,11]]}},"alternative-id":["rs15215127"],"URL":"https:\/\/doi.org\/10.3390\/rs15215127","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2023,10,26]]}}}