{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T06:54:49Z","timestamp":1775199289495,"version":"3.50.1"},"reference-count":34,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2024,9,28]],"date-time":"2024-09-28T00:00:00Z","timestamp":1727481600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Research Project of China Water Resources Pearl River Planning Surveying &amp; Designing Co., Ltd.","award":["2023KY01"],"award-info":[{"award-number":["2023KY01"]}]},{"name":"Research Project of China Water Resources Pearl River Planning Surveying &amp; Designing Co., Ltd.","award":["2022KY06"],"award-info":[{"award-number":["2022KY06"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Remote sensing image change detection is crucial for urban planning, environmental monitoring, and disaster assessment, as it identifies temporal variations of specific targets, such as surface buildings, by analyzing differences between images from different time periods. Current research faces challenges, including the accurate extraction of change features and the handling of complex and varied image contexts. To address these issues, this study proposes an innovative model named the Segment Anything Model-UNet Change Detection Model (SCDM), which incorporates the proposed center expansion and reduction method (CERM), Segment Anything Model (SAM), UNet, and fine-grained loss function. The global feature map of the environment is extracted, the difference measurement features are extracted, and then the global feature map and the difference measurement features are fused. Finally, a global decoder is constructed to predict the changes of the same region in different periods. Detailed ablation experiments and comparative experiments are conducted on the WHU-CD and LEVIR-CD public datasets to evaluate the performance of the proposed method. At the same time, validation on more complex DTX datasets for scenarios is supplemented. The experimental results demonstrate that compared to traditional fixed-size partitioning methods, the CERM proposed in this study significantly improves the accuracy of SOTA models, including ChangeFormer, ChangerEx, Tiny-CD, BIT, DTCDSCN, and STANet. Additionally, compared with other methods, the SCDM demonstrates superior performance and generalization, showcasing its effectiveness in overcoming the limitations of existing methods.<\/jats:p>","DOI":"10.3390\/rs16193620","type":"journal-article","created":{"date-parts":[[2024,9,30]],"date-time":"2024-09-30T05:45:27Z","timestamp":1727675127000},"page":"3620","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Fine-Grained High-Resolution Remote Sensing Image Change Detection by SAM-UNet Change Detection Model"],"prefix":"10.3390","volume":"16","author":[{"given":"Xueqiang","family":"Zhao","sequence":"first","affiliation":[{"name":"School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China"},{"name":"China Water Resources Pearl River Planning Surveying & Designing Co., Ltd., Guangzhou 510610, China"}]},{"given":"Zheng","family":"Wu","sequence":"additional","affiliation":[{"name":"China Water Resources Pearl River Planning Surveying & Designing Co., Ltd., Guangzhou 510610, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4445-2933","authenticated-orcid":false,"given":"Yangbo","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China"}]},{"given":"Wei","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Computer Science, Xiangtan University, Xiangtan 411105, China"}]},{"given":"Mingan","family":"Wei","sequence":"additional","affiliation":[{"name":"School of Computer Science, Xiangtan University, Xiangtan 411105, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,9,28]]},"reference":[{"key":"ref_1","first-page":"1988","article-title":"Review of remote sensing change detection in deep learning: Bibliometric and analysis","volume":"27","author":"Yang","year":"2023","journal-title":"J. Remote Sens."},{"key":"ref_2","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_3","first-page":"1975","article-title":"Remote sensing change detection technology in the Era of artificial intelligence: Inheritance, development and challenges","volume":"27","author":"Liu","year":"2023","journal-title":"J. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1016\/j.isprsjprs.2020.03.002","article-title":"A method to improve the accuracy of SAR image change detection by using an image enhancement method","volume":"163","author":"Li","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"3130","DOI":"10.1109\/TIP.2019.2894284","article-title":"Semantic prior analysis for salient object detection","volume":"28","author":"Nguyen","year":"2019","journal-title":"IEEE Trans. Image Process."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1016\/j.dsp.2013.09.008","article-title":"Lossy image compression using singular value decomposition and wavelet difference reduction","volume":"24","author":"Rufai","year":"2014","journal-title":"Digit. Signal Process."},{"key":"ref_7","unstructured":"He, Z., Zhang, Z.W., Feng, H., and Wang, L. (2013, January 19\u201321). The Application of Wavelet Transform and the Adaptive Threshold Segmentation in Image Change Detection. Proceedings of the International Conference on Applied Science, Engineering and Technology (ICASET 2013), Qingdao, China."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"062804","DOI":"10.3788\/LOP54.062804","article-title":"Otsu Change Detection of Low and Moderate Resolution Synthetic Aperture Radar Image byUsingMulti-Texture Features","volume":"54","author":"Ma","year":"2017","journal-title":"Laser Optoelectron. Prog."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"5507312","DOI":"10.1109\/TGRS.2021.3090802","article-title":"Hyperspectral Change Detection Based on Multiple Morphological Profiles","volume":"60","author":"Hou","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Tao, M., Yang, L., Gu, Y., and Cheng, S. (2017, January 10\u201312). Object-Oriented Change Detection Based on Change Magnitude Fusion in Multitemporal Very High Resolution Images. Proceedings of the 9th International Conference on Modelling, Identification and Control (ICMIC), Kunming, China.","DOI":"10.1109\/ICMIC.2017.8321680"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1016\/j.isprsjprs.2021.05.001","article-title":"High-resolution triplet network with dynamic multiscale feature for change detection on satellite images","volume":"177","author":"Hou","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1109\/TIP.2020.3031173","article-title":"Hierarchical paired channel fusion network for street scene change detection","volume":"30","author":"Lei","year":"2020","journal-title":"IEEE Trans. Image Process."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1016\/j.isprsjprs.2023.11.023","article-title":"Multi-stage progressive change detection on high resolution remote sensing imagery","volume":"207","author":"Ning","year":"2024","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"5625716","DOI":"10.1109\/TGRS.2023.3332219","article-title":"RingMo-SAM: A Foundation Model for Segment Anything in Multimodal Remote-Sensing Images","volume":"61","author":"Yan","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Yang, Y., Chen, T., and Li, J. (2023, January 16\u201321). SRNet: Siamese Residual Network for Remote Sensing Change Detection. Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Pasadena, CA, USA.","DOI":"10.1109\/IGARSS52108.2023.10283340"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Jiang, H., Hu, X., Li, K., Zhang, J.M., Gong, J.Q., and Zhang, M. (2020). PGA-SiamNet: Pyramid Feature-Based Attention-Guided Siamese Network for Remote Sensing Orthoimagery Building Change Detection. Remote Sens., 12.","DOI":"10.3390\/rs12030484"},{"key":"ref_17","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_18","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_19","doi-asserted-by":"crossref","first-page":"924","DOI":"10.1109\/TGRS.2018.2863224","article-title":"Learning spectral-spatial-temporal features via a recurrent convolutional neural network for change detection in multispectral imagery","volume":"57","author":"Mou","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_20","first-page":"32","article-title":"Axial cross attention meets CNN: Bibranch fusion network for change detection","volume":"16","author":"Song","year":"2022","journal-title":"EEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1016\/j.isprsjprs.2023.04.001","article-title":"Global-aware siamese network for change detection on remote sensing images","volume":"199","author":"Zhang","year":"2023","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_22","unstructured":"Yi, L., Chao, P., Zongqian, Z., Xiaomeng, Z., and Xue, Y. (2019). Building Change Detection for Remote Sensing Images Using a Dual Task Constrained Deep Siamese Convolutional Network Model. arXiv."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Kolesnikov, A., Beyer, L., Xiaohua, Z., Puigcerver, J., Yung, J., Gelly, S., and Houlsby, N. (2020, January 23\u201328). Big Transfer (BiT): General Visual Representation Learning. Proceedings of the Computer Vision\u2014ECCV 2020 16th European Conference, Glasgow, UK. Lecture Notes in Computer Science (LNCS 12350).","DOI":"10.1007\/978-3-030-58558-7_29"},{"key":"ref_24","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 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Kuala Lumpur, Malaysia.","DOI":"10.1109\/IGARSS46834.2022.9883686"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"8471","DOI":"10.1007\/s00521-022-08122-3","article-title":"TINYCD: A (not so) deep learning model for change detection","volume":"35","author":"Codegoni","year":"2023","journal-title":"Neural Comput. Appl."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Ji, Z., Wang, X., Wang, Z., and Li, G. (2023, January 16\u201321). An Unsupervised Siamese Superpixel-Based Network for Change Detection in Heterogeneous Remote Sensing Images. Proceedings of the IGARSS 2023\u20142023 IEEE International Geoscience and Remote Sensing Symposium, Pasadena, CA, USA.","DOI":"10.1109\/IGARSS52108.2023.10283145"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"5616016","DOI":"10.1109\/TGRS.2023.3297092","article-title":"Boosting Semantic Segmentation of Aerial Images via Decoupled and Multi-level Compaction and Dispersion","volume":"61","author":"Shan","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Kirillov, A., Mintun, E., Ravi, N., Mao, H., Rolland, C., Gustafson, L., Xiao, T., Whitehead, S., Berg, A.C., and Lo, W.Y. (2023, January 2\u20133). Segment anything. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Paris, France.","DOI":"10.1109\/ICCV51070.2023.00371"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"7112","DOI":"10.1080\/01431161.2020.1754494","article-title":"Synthetic Aperture Radar (SAR) image processing for operational space-based agriculture mapping","volume":"41","author":"Davidson","year":"2020","journal-title":"Int. J. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"2223","DOI":"10.1007\/s00371-021-02328-7","article-title":"X-net: A dual encoding\u2013decoding method in medical image segmentation","volume":"39","author":"Li","year":"2023","journal-title":"Vis. Comput."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","article-title":"Segnet: A deep convolutional encoder-decoder architecture for image segmentation","volume":"39","author":"Badrinarayanan","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_32","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_33","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_34","doi-asserted-by":"crossref","unstructured":"Liu, J., and Ji, S. (2020, January 14\u201319). A Novel Recurrent Encoder-Decoder Structure for Large-Scale Multi-view Stereo Reconstruction from An Open Aerial Dataset. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Electr Network.","DOI":"10.1109\/CVPR42600.2020.00609"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/19\/3620\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:05:55Z","timestamp":1760112355000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/19\/3620"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,28]]},"references-count":34,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2024,10]]}},"alternative-id":["rs16193620"],"URL":"https:\/\/doi.org\/10.3390\/rs16193620","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,9,28]]}}}