{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T16:35:49Z","timestamp":1775666149175,"version":"3.50.1"},"reference-count":60,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2024,2,2]],"date-time":"2024-02-02T00:00:00Z","timestamp":1706832000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["42271446"],"award-info":[{"award-number":["42271446"]}]},{"name":"National Natural Science Foundation of China","award":["41971281"],"award-info":[{"award-number":["41971281"]}]},{"name":"National Natural Science Foundation of China","award":["TKL2023A12"],"award-info":[{"award-number":["TKL2023A12"]}]},{"name":"Tianjin Key Laboratory of Rail Transit Navigation Positioning and Spatiotemporal Big Data Technology","award":["42271446"],"award-info":[{"award-number":["42271446"]}]},{"name":"Tianjin Key Laboratory of Rail Transit Navigation Positioning and Spatiotemporal Big Data Technology","award":["41971281"],"award-info":[{"award-number":["41971281"]}]},{"name":"Tianjin Key Laboratory of Rail Transit Navigation Positioning and Spatiotemporal Big Data Technology","award":["TKL2023A12"],"award-info":[{"award-number":["TKL2023A12"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Change detection (CD) stands out as a pivotal yet challenging task in the interpretation of remote sensing images. Significant developments have been witnessed, particularly with the rapid advancements in deep learning techniques. Nevertheless, challenges such as incomplete detection targets and unsmooth boundaries remain as most CD methods suffer from ineffective feature fusion. Therefore, this paper presents a multi-scale gated fusion network (MSGFNet) to improve the accuracy of CD results. To effectively extract bi-temporal features, the EfficientNetB4 model based on a Siamese network is employed. Subsequently, we propose a multi-scale gated fusion module (MSGFM) that comprises a multi-scale progressive fusion (MSPF) unit and a gated weight adaptive fusion (GWAF) unit, aimed at fusing bi-temporal multi-scale features to maintain boundary details and detect completely changed targets. Finally, we use the simple yet efficient UNet structure to recover the feature maps and predict results. To demonstrate the effectiveness of the MSGFNet, the LEVIR-CD, WHU-CD, and SYSU-CD datasets were utilized, and the MSGFNet achieved F1 scores of 90.86%, 92.46%, and 80.39% on the three datasets, respectively. Furthermore, the low computational costs and small model size have validated the superior performance of the MSGFNet.<\/jats:p>","DOI":"10.3390\/rs16030572","type":"journal-article","created":{"date-parts":[[2024,2,2]],"date-time":"2024-02-02T09:42:32Z","timestamp":1706866952000},"page":"572","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["MSGFNet: Multi-Scale Gated Fusion Network for Remote Sensing Image Change Detection"],"prefix":"10.3390","volume":"16","author":[{"given":"Yukun","family":"Wang","sequence":"first","affiliation":[{"name":"School of Mechatronics Engineering, Beijing Institute of Technology, Beijing 100081, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3708-153X","authenticated-orcid":false,"given":"Mengmeng","family":"Wang","sequence":"additional","affiliation":[{"name":"Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China"}]},{"given":"Zhonghu","family":"Hao","sequence":"additional","affiliation":[{"name":"School of Mechatronics Engineering, Beijing Institute of Technology, Beijing 100081, China"}]},{"given":"Qiang","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Mechatronics Engineering, Beijing Institute of Technology, Beijing 100081, China"}]},{"given":"Qianwen","family":"Wang","sequence":"additional","affiliation":[{"name":"The Fifth Military Delegate Office in Beijing, Beijing 100038, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6843-6722","authenticated-orcid":false,"given":"Yuanxin","family":"Ye","sequence":"additional","affiliation":[{"name":"Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,2,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2365","DOI":"10.1080\/0143116031000139863","article-title":"Change Detection Techniques","volume":"25","author":"Lu","year":"2004","journal-title":"Int. J. Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1109\/JSTARS.2013.2252423","article-title":"Building Change Detection from Multitemporal High-Resolution Remotely Sensed Images Based on a Morphological Building Index","volume":"7","author":"Huang","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"555","DOI":"10.5194\/essd-15-555-2023","article-title":"UGS-1m: Fine-Grained Urban Green Space Mapping of 31 Major Cities in China Based on the Deep Learning Framework","volume":"15","author":"Shi","year":"2023","journal-title":"Earth Syst. Sci. Data"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"046019","DOI":"10.1117\/1.JRS.10.046019","article-title":"Change Detection from Synthetic Aperture Radar Images Based on Neighborhood-Based Ratio and Extreme Learning Machine","volume":"10","author":"Gao","year":"2016","journal-title":"J. Appl. Remote Sens."},{"key":"ref_5","first-page":"6002005","article-title":"Unsupervised Change Detection Based on Weighted Change Vector Analysis and Improved Markov Random Field for High Spatial Resolution Imagery","volume":"19","author":"Fang","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"6209","DOI":"10.1080\/01431161.2021.1937372","article-title":"An Object-Based Graph Model for Unsupervised Change Detection in High Resolution Remote Sensing Images","volume":"42","author":"Wu","year":"2021","journal-title":"Int. J. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"681","DOI":"10.1109\/TGRS.1990.572980","article-title":"An Assessment of TM Imagery for Land-Cover Change Detection","volume":"28","author":"Fung","year":"1990","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_8","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_9","doi-asserted-by":"crossref","first-page":"2858","DOI":"10.1109\/TGRS.2013.2266673","article-title":"Slow Feature Analysis for Change Detection in Multispectral Imagery","volume":"52","author":"Wu","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"346","DOI":"10.1080\/2150704X.2023.2201382","article-title":"Exploiting Neighbourhood Structural Features for Change Detection","volume":"14","author":"Wang","year":"2023","journal-title":"Remote Sens. Lett."},{"key":"ref_11","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_12","doi-asserted-by":"crossref","first-page":"2848","DOI":"10.1109\/TGRS.2019.2956756","article-title":"Change Detection in Multisource VHR Images via Deep Siamese Convolutional Multiple-Layers Recurrent Neural Network","volume":"58","author":"Chen","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_13","first-page":"5606115","article-title":"R\u2082FD\u2082: Fast and Robust Matching of Multimodal Remote Sensing Images via Repeatable Feature Detector and Rotation-Invariant Feature Descriptor","volume":"61","author":"Zhu","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1016\/j.isprsjprs.2022.04.011","article-title":"A Robust Multimodal Remote Sensing Image Registration Method and System Using Steerable Filters with First- and Second-Order Gradients","volume":"188","author":"Ye","year":"2022","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"5622215","DOI":"10.1109\/TGRS.2022.3167644","article-title":"A Multiscale Framework with Unsupervised Learning for Remote Sensing Image Registration","volume":"60","author":"Ye","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"5148","DOI":"10.1109\/TCSVT.2023.3250464","article-title":"An Adaptive Interference Removal Framework for Video Person Re-Identification","volume":"33","author":"Tao","year":"2023","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"5618214","DOI":"10.1109\/TGRS.2023.3305499","article-title":"Adjacent-Level Feature Cross-Fusion With 3-D CNN for Remote Sensing Image Change Detection","volume":"61","author":"Ye","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"5627114","DOI":"10.1109\/TGRS.2022.3196040","article-title":"Joint Frequency-Spatial Domain Network for Remote Sensing Optical Image Change Detection","volume":"60","author":"Zhou","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"109766","DOI":"10.1016\/j.asoc.2022.109766","article-title":"SUMLP: A Siamese U-Shaped MLP-Based Network for Change Detection","volume":"131","author":"Zhang","year":"2022","journal-title":"Appl. Soft Comput."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"8007805","DOI":"10.1109\/LGRS.2021.3056416","article-title":"SNUNet-CD: A Densely Connected Siamese Network for Change Detection of VHR Images","volume":"19","author":"Fang","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Hu, Q., Wang, D., and Yang, C. (2022). PPG-Based Blood Pressure Estimation Can Benefit from Scalable Multi-Scale Fusion Neural Networks and Multi-Task Learning. Biomed. Signal Process. Control, 78.","DOI":"10.1016\/j.bspc.2022.103891"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"6511605","DOI":"10.1109\/LGRS.2022.3184179","article-title":"FCDNet: A Change Detection Network Based on Full-Scale Skip Connections and Coordinate Attention","volume":"19","author":"Xiang","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"9735","DOI":"10.1109\/JSTARS.2023.3323372","article-title":"Multiscale Change Detection Network Based on Channel Attention and Fully Convolutional BiLSTM for Medium-Resolution Remote Sensing Imagery","volume":"16","author":"Li","year":"2023","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"4709918","DOI":"10.1109\/TGRS.2022.3226778","article-title":"Joint Variation Learning of Fusion and Difference Features for Change Detection in Remote Sensing Images","volume":"60","author":"Jiang","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_25","unstructured":"Tan, M., and Le, Q. (2019, January 24). EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. Proceedings of the 36th International Conference on Machine Learning, PMLR, Long Beach, CA, USA."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1109\/MGRS.2021.3063465","article-title":"Change Detection From Very-High-Spatial-Resolution Optical Remote Sensing Images: Methods, Applications, and Future Directions","volume":"9","author":"Wen","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"8004505","DOI":"10.1109\/LGRS.2022.3179400","article-title":"L-UNet: An LSTM Network for Remote Sensing Image Change Detection","volume":"19","author":"Sun","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_28","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_29","doi-asserted-by":"crossref","first-page":"4963","DOI":"10.1109\/JSTARS.2023.3279863","article-title":"A Siamese Network Combining Multiscale Joint Supervision and Improved Consistency Regularization for Weakly Supervised Building Change Detection","volume":"16","author":"Dai","year":"2023","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Ye, Y., Zhou, L., Zhu, B., Yang, C., Sun, M., Fan, J., and Fu, Z. (2022). Feature Decomposition-Optimization-Reorganization Network for Building Change Detection in Remote Sensing Images. Remote Sens., 14.","DOI":"10.3390\/rs14030722"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"2557","DOI":"10.1109\/JSTARS.2023.3344635","article-title":"A Lightweight Change Detection Network Based on Feature Interleaved Fusion and Bistage Decoding","volume":"17","author":"Wang","year":"2024","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_32","first-page":"5602812","article-title":"Lightweight Remote Sensing Change Detection with Progressive Feature Aggregation and Supervised Attention","volume":"61","author":"Li","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_33","first-page":"4403712","article-title":"Mining Joint Intraimage and Interimage Context for Remote Sensing Change Detection","volume":"61","author":"Zhou","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_34","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 IEEE International Geoscience and Remote Sensing Symposium, Kuala Lumpur, Malaysia.","DOI":"10.1109\/IGARSS46834.2022.9883686"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"8442","DOI":"10.1109\/JSTARS.2022.3204191","article-title":"PSTNet: Progressive Sampling Transformer Network for Remote Sensing Image Change Detection","volume":"15","author":"Song","year":"2022","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"5610111","DOI":"10.1109\/TGRS.2023.3277496","article-title":"Changer: Feature Interaction Is What You Need for Change Detection","volume":"61","author":"Fang","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"5224713","DOI":"10.1109\/TGRS.2022.3221492","article-title":"SwinSUNet: Pure Transformer Network for Remote Sensing Image Change Detection","volume":"60","author":"Zhang","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_38","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":"2023","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"7506005","DOI":"10.1109\/LGRS.2023.3323367","article-title":"LHDACT: Lightweight Hybrid Dual Attention CNN and Transformer Network for Remote Sensing Image Change Detection","volume":"20","author":"Song","year":"2023","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"4410213","DOI":"10.1109\/TGRS.2022.3168331","article-title":"ICIF-Net: Intra-Scale Cross-Interaction and Inter-Scale Feature Fusion Network for Bitemporal Remote Sensing Images Change Detection","volume":"60","author":"Feng","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"5920416","DOI":"10.1109\/TGRS.2022.3209972","article-title":"Remote Sensing Image Change Detection Transformer Network Based on Dual-Feature Mixed Attention","volume":"60","author":"Song","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_42","first-page":"103141","article-title":"DBFGAN: Dual Branch Feature Guided Aggregation Network for Remote Sensing Image","volume":"116","author":"Chu","year":"2023","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"5615814","DOI":"10.1109\/TGRS.2023.3296383","article-title":"WNet: W-Shaped Hierarchical Network for Remote-Sensing Image Change Detection","volume":"61","author":"Tang","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_44","first-page":"234","article-title":"U-Net: Convolutional Networks for Biomedical Image Segmentation","volume":"Volume 9351","author":"Navab","year":"2015","journal-title":"Medical Image Computing and Computer-Assisted Intervention\u2014MICCAI 2015"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"4900","DOI":"10.1109\/JSTARS.2023.3278726","article-title":"Enhanced Self-Attention Network for Remote Sensing Building Change Detection","volume":"16","author":"Liang","year":"2023","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"4014","DOI":"10.1109\/JSTARS.2022.3174780","article-title":"DRMNet: Difference Image Reconstruction Enhanced Multiresolution Network for Optical Change Detection","volume":"15","author":"Chouhan","year":"2022","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_47","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_48","first-page":"8006605","article-title":"DifUnet++: A Satellite Images Change Detection Network Based on Unet++ and Differential Pyramid","volume":"19","author":"Zhang","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_49","first-page":"6001005","article-title":"ECFNet: A Siamese Network with Fewer FPs and Fewer FNs for Change Detection of Remote-Sensing Images","volume":"20","author":"Zhu","year":"2023","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Cheng, Y., Cai, R., Li, Z., Zhao, X., and Huang, K. (2017, January 21\u201326). Locality-Sensitive Deconvolution Networks with Gated Fusion for RGB-D Indoor Semantic Segmentation. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.161"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"8170","DOI":"10.1109\/JSTARS.2022.3206898","article-title":"Supervised Change Detection Using Prechange Optical-SAR and Postchange SAR Data","volume":"15","author":"Saha","year":"2022","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"5061","DOI":"10.1109\/JSTARS.2023.3280589","article-title":"DAFT: Differential Feature Extraction Network Based on Adaptive Frequency Transformer for Remote Sensing Change Detection","volume":"16","author":"Fu","year":"2023","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"537","DOI":"10.1109\/TETCI.2022.3230941","article-title":"RSCDNet: A Robust Deep Learning Architecture for Change Detection from Bi-Temporal High Resolution Remote Sensing Images","volume":"7","author":"Barkur","year":"2023","journal-title":"IEEE Trans. Emerg. Top. Comput. Intell."},{"key":"ref_54","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":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_55","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_56","doi-asserted-by":"crossref","first-page":"5604816","DOI":"10.1109\/TGRS.2022.3158741","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":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_57","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_58","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1016\/j.isprsjprs.2020.06.003","article-title":"A Deeply Supervised Image Fusion Network for Change Detection in High Resolution Bi-Temporal Remote Sensing Images","volume":"166","author":"Zhang","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_59","first-page":"5607514","article-title":"Remote Sensing Image Change Detection with Transformers","volume":"60","author":"Chen","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Yang, H., Chen, Y., Wu, W., Pu, S., Wu, X., Wan, Q., and Dong, W. (2023). A Lightweight Siamese Neural Network for Building Change Detection Using Remote Sensing Images. Remote Sens., 15.","DOI":"10.3390\/rs15040928"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/3\/572\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T13:53:33Z","timestamp":1760104413000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/3\/572"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2,2]]},"references-count":60,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2024,2]]}},"alternative-id":["rs16030572"],"URL":"https:\/\/doi.org\/10.3390\/rs16030572","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,2,2]]}}}