{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T01:06:35Z","timestamp":1773795995127,"version":"3.50.1"},"reference-count":63,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2023,8,25]],"date-time":"2023-08-25T00:00:00Z","timestamp":1692921600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of PR China","award":["42075130"],"award-info":[{"award-number":["42075130"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Recently, deep learning-based change detection methods for bitemporal remote sensing images have achieved promising results based on fully convolutional neural networks. However, due to the inherent characteristics of convolutional neural networks, if the previous block fails to correctly segment the entire target, erroneous predictions might accumulate in the subsequent blocks, leading to incomplete change detection results in terms of structure. To address this issue, we propose a bitemporal remote sensing image change detection network based on a Siamese-attention feedback architecture, referred to as SAFNet. First, we propose a global semantic module (GSM) on the encoder network, aiming to generate a low-resolution semantic change map to capture the changed objects. Second, we introduce a temporal interaction module (TIM), which is built through each encoding and decoding block, using the feature feedback between two temporal blocks to enhance the network\u2019s perception ability of the entire changed target. Finally, we propose two auxiliary modules\u2014the change feature extraction module (CFEM) and the feature refinement module (FRM)\u2014which are further used to learn the fine boundaries of the changed target. The deep model we propose produced satisfying results in dual-temporal remote sensing image change detection. Extensive experiments on two remote sensing image change detection datasets demonstrate that the SAFNet algorithm exhibits state-of-the-art performance.<\/jats:p>","DOI":"10.3390\/rs15174186","type":"journal-article","created":{"date-parts":[[2023,8,25]],"date-time":"2023-08-25T08:33:09Z","timestamp":1692952389000},"page":"4186","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Bitemporal Remote Sensing Image Change Detection Network Based on Siamese-Attention Feedback Architecture"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9254-9521","authenticated-orcid":false,"given":"Hongyang","family":"Yin","sequence":"first","affiliation":[{"name":"Collaborative Innovation Center on Atmospheric Environment and Equipment Technology, B-DAT, Nanjing University of Information Science and Technology, Nanjing 210044, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-8192-9676","authenticated-orcid":false,"given":"Chong","family":"Ma","sequence":"additional","affiliation":[{"name":"Collaborative Innovation Center on Atmospheric Environment and Equipment Technology, B-DAT, Nanjing University of Information Science and Technology, Nanjing 210044, China"}]},{"given":"Liguo","family":"Weng","sequence":"additional","affiliation":[{"name":"Collaborative Innovation Center on Atmospheric Environment and Equipment Technology, B-DAT, Nanjing University of Information Science and Technology, Nanjing 210044, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4681-9129","authenticated-orcid":false,"given":"Min","family":"Xia","sequence":"additional","affiliation":[{"name":"Collaborative Innovation Center on Atmospheric Environment and Equipment Technology, B-DAT, Nanjing University of Information Science and Technology, Nanjing 210044, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3835-6075","authenticated-orcid":false,"given":"Haifeng","family":"Lin","sequence":"additional","affiliation":[{"name":"College of Information Science and Technology, Nanjing Forestry University, Nanjing 210000, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,8,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"727","DOI":"10.1007\/s11769-018-0988-9","article-title":"Urban expansion in China based on remote sensing technology: A review","volume":"28","author":"Zhang","year":"2018","journal-title":"Chin. Geogr. Sci."},{"key":"ref_2","first-page":"791","article-title":"Using remote sensing technology to detect, model and map desertification: A review","volume":"11","author":"Albalawi","year":"2013","journal-title":"J. Food Agric. Environ."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1007\/s12145-017-0286-6","article-title":"An overview of satellite remote sensing technology used in China\u2019s environmental protection","volume":"10","author":"Zhao","year":"2017","journal-title":"Earth Sci. Inform."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1109\/JSTARS.2013.2267204","article-title":"Progress in hyperspectral remote sensing science and technology in China over the past three decades","volume":"7","author":"Tong","year":"2013","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"6812","DOI":"10.1109\/JSTARS.2023.3295729","article-title":"Sgformer: A Local and Global Features Coupling Network for Semantic Segmentation of Land Cover","volume":"16","author":"Weng","year":"2023","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"3283435","DOI":"10.1109\/TGRS.2023.3283435","article-title":"Multi-scale Attention Feature Aggregation Network for Cloud and Cloud Shadow Segmentation","volume":"61","author":"Chen","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"3276703","DOI":"10.1109\/TGRS.2023.3276703","article-title":"Multi-Scale Location Attention Network for Building and Water Segmentation of Remote Sensing Image","volume":"61","author":"Dai","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Ji, H., Xia, M., Zhang, D., and Lin, H. (2023). Multi-Supervised Feature Fusion Attention Network for Clouds and Shadows Detection. ISPRS Int. J. Geo-Inf., 12.","DOI":"10.3390\/ijgi12060247"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Chen, J., Xia, M., Wang, D., and Lin, H. (2023). Double Branch Parallel Network for Segmentation of Buildings and Waters in Remote Sensing Images. Remote Sens., 15.","DOI":"10.3390\/rs15061536"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1109\/JSTARS.2022.3224081","article-title":"Axial cross attention meets CNN: Bibranch fusion network for change detection","volume":"16","author":"Song","year":"2022","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Wang, D., Weng, L., Xia, M., and Lin, H. (2023). MBCNet: Multi-Branch Collaborative Change-Detection Network Based on Siamese Structure. Remote Sens., 15.","DOI":"10.3390\/rs15092237"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"106324","DOI":"10.1016\/j.engappai.2023.106324","article-title":"Dual-branch network for change detection of remote sensing image","volume":"123","author":"Ma","year":"2023","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_13","first-page":"18","article-title":"Remote sensing and unmanned aerial system technology for monitoring and quantifying forest fire impacts","volume":"4","author":"Wing","year":"2014","journal-title":"Int. J. Remote Sens. Appl."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"104940","DOI":"10.1016\/j.cageo.2021.104940","article-title":"Strip pooling channel spatial attention network for the segmentation of cloud and cloud shadow","volume":"157","author":"Qu","year":"2021","journal-title":"Comput. Geosci."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"5940","DOI":"10.1080\/01431161.2021.2014077","article-title":"Cloud\/shadow segmentation based on multi-level feature enhanced network for remote sensing imagery","volume":"43","author":"Miao","year":"2022","journal-title":"Int. J. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.rse.2007.02.010","article-title":"Early fire detection using non-linear multitemporal prediction of thermal imagery","volume":"110","author":"Koltunov","year":"2007","journal-title":"Remote Sens. Environ."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"5874","DOI":"10.1080\/01431161.2022.2073795","article-title":"MANet: A multi-level aggregation network for semantic segmentation of high-resolution remote sensing images","volume":"43","author":"Chen","year":"2022","journal-title":"Int. J. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Ma, Z., Xia, M., Weng, L., and Lin, H. (2023). Local Feature Search Network for Building and Water Segmentation of Remote Sensing Image. Sustainability, 15.","DOI":"10.3390\/su15043034"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1676","DOI":"10.1016\/j.rse.2010.02.018","article-title":"Updating the 2001 National Land Cover Database impervious surface products to 2006 using Landsat imagery change detection methods","volume":"114","author":"Xian","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Hu, K., Li, M., Xia, M., and Lin, H. (2022). Multi-Scale Feature Aggregation Network for Water Area Segmentation. Remote Sens., 14.","DOI":"10.3390\/rs14010206"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1080\/01431160802261163","article-title":"Three decades of land use variations in Mexico City","volume":"30","year":"2009","journal-title":"Int. J. Remote Sensinginternational J. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1702","DOI":"10.1080\/01431161.2023.2190471","article-title":"FENet: Feature enhancement network for land cover classification","volume":"44","author":"Ma","year":"2023","journal-title":"Int. J. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Hu, K., Zhang, E., Xia, M., Weng, L., and Lin, H. (2023). Mcanet: A multi-branch network for cloud\/snow segmentation in high-resolution remote sensing images. Remote Sens., 15.","DOI":"10.3390\/rs15041055"},{"key":"ref_24","first-page":"1","article-title":"Dual-branch network for cloud and cloud shadow segmentation","volume":"60","author":"Lu","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"16513","DOI":"10.1117\/1.JRS.16.016513","article-title":"MLNet: Multichannel feature fusion lozenge network for land segmentation","volume":"16","author":"Gao","year":"2022","journal-title":"J. Appl. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"4809","DOI":"10.1109\/JSTARS.2022.3181303","article-title":"LCDNet: Light-weighted cloud detection network for high-resolution remote sensing images","volume":"15","author":"Hu","year":"2022","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"27442","DOI":"10.1109\/ACCESS.2018.2807380","article-title":"Adaptive change detection with significance test","volume":"6","author":"Ke","year":"2018","journal-title":"IEEE Access"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"896","DOI":"10.1109\/36.239913","article-title":"Change detection techniques for ERS-1 SAR data","volume":"31","author":"Rignot","year":"1993","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1109\/TNNLS.2013.2248094","article-title":"PCA feature extraction for change detection in multidimensional unlabeled data","volume":"25","author":"Kuncheva","year":"2013","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_30","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_31","doi-asserted-by":"crossref","first-page":"772","DOI":"10.1109\/LGRS.2009.2025059","article-title":"Unsupervised change detection in satellite images using principal component analysis and k-means clustering","volume":"6","author":"Celik","year":"2009","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Zhang, X., Cui, J., Wang, W., and Lin, C. (2017). A study for texture feature extraction of high-resolution satellite images based on a direction measure and gray level co-occurrence matrix fusion algorithm. Sensors, 17.","DOI":"10.3390\/s17071474"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Guiming, S., and Jidong, S. (2016, January 4\u20136). Remote sensing image edge-detection based on improved Canny operator. Proceedings of the 2016 8th IEEE International Conference on Communication Software and Networks (ICCSN), Beijing, China.","DOI":"10.1109\/ICCSN.2016.7586604"},{"key":"ref_34","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_35","doi-asserted-by":"crossref","first-page":"396","DOI":"10.1080\/2150704X.2014.912766","article-title":"A novel dynamic threshold method for unsupervised change detection from remotely sensed images","volume":"5","author":"He","year":"2014","journal-title":"Remote Sens. Lett."},{"key":"ref_36","first-page":"131","article-title":"Robust Change Vector Analysis (RCVA) for multi-sensor very high resolution optical satellite data","volume":"50","author":"Thonfeld","year":"2016","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"691","DOI":"10.1109\/LGRS.2013.2275738","article-title":"Using combined difference image and k-means clustering for SAR image change detection","volume":"11","author":"Zheng","year":"2013","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Luppino, L.T., Bianchi, F.M., Moser, G., and Anfinsen, S.N. (2019). Unsupervised image regression for heterogeneous change detection. arXiv.","DOI":"10.1109\/MLSP.2018.8517033"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Zhang, C., Weng, L., Ding, L., Xia, M., and Lin, H. (2023). CRSNet: Cloud and Cloud Shadow Refinement Segmentation Networks for Remote Sensing Imagery. Remote Sens., 15.","DOI":"10.3390\/rs15061664"},{"key":"ref_40","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_41","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_42","doi-asserted-by":"crossref","first-page":"266","DOI":"10.1109\/LGRS.2018.2869608","article-title":"Triplet-based semantic relation learning for aerial remote sensing image change detection","volume":"16","author":"Zhang","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_43","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_44","first-page":"102597","article-title":"SUACDNet: Attentional change detection network based on siamese U-shaped structure","volume":"105","author":"Song","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_45","first-page":"1","article-title":"Spectral\u2013spatial\u2013temporal transformers for hyperspectral image change detection","volume":"60","author":"Wang","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_46","first-page":"103366","article-title":"Cascaded attention-induced difference representation learning for multispectral change detection","volume":"121","author":"Zhang","year":"2023","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_47","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_48","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_49","first-page":"102940","article-title":"DPCC-Net: Dual-perspective change contextual network for change detection in high-resolution remote sensing images","volume":"112","author":"Shu","year":"2022","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_50","first-page":"103206","article-title":"Attention-guided siamese networks for change detection in high resolution remote sensing images","volume":"117","author":"Yin","year":"2023","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_51","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_52","doi-asserted-by":"crossref","unstructured":"Hu, K., Li, J., Lu, M., Weng, L., and Xia, M. (2022). FedGCN: Federated Learning-Based Graph Convolutional Networks for Non-Euclidean Spatial Data. Mathematics, 10.","DOI":"10.3390\/math10061000"},{"key":"ref_53","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_54","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015, January 5\u20139). U-net: Convolutional networks for biomedical image segmentation. Proceedings of the Medical Image Computing and Computer-Assisted Intervention (MICCAI 2015): 18th International Conference, Munich, Germany. Proceedings\u2014Part III 18."},{"key":"ref_55","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_56","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_57","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_58","doi-asserted-by":"crossref","unstructured":"Qian, J., Xia, M., Zhang, Y., Liu, J., and Xu, Y. (2020). TCDNet: Trilateral Change Detection Network for Google Earth Image. Remote Sens., 12.","DOI":"10.3390\/rs12172669"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"10157","DOI":"10.1007\/s00521-022-06999-8","article-title":"MFGAN: Multi feature guided aggregation network for remote sensing image","volume":"34","author":"Chu","year":"2022","journal-title":"Neural Comput. Appl."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2020.3034752","article-title":"Remote sensing image change detection with transformers","volume":"60","author":"Chen","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_61","first-page":"1","article-title":"Remote sensing change detection via temporal feature interaction and guided refinement","volume":"60","author":"Li","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"3286180","DOI":"10.1109\/LGRS.2023.3286180","article-title":"Multireceiver SAS imagery with generalized PCA","volume":"20","author":"Zhang","year":"2023","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Jiang, N., Du, H., Ge, S., Zhu, J., Feng, D., Wang, J., and Huang, X. (2023). High-Resolution Azimuth Missing Data SAR Imaging Based on Sparse Representation Autofocusing. Remote Sens., 15.","DOI":"10.3390\/rs15133425"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/17\/4186\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:39:09Z","timestamp":1760128749000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/17\/4186"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,25]]},"references-count":63,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2023,9]]}},"alternative-id":["rs15174186"],"URL":"https:\/\/doi.org\/10.3390\/rs15174186","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,8,25]]}}}