{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:41:23Z","timestamp":1760143283701,"version":"build-2065373602"},"reference-count":50,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2024,1,31]],"date-time":"2024-01-31T00:00:00Z","timestamp":1706659200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the National Key R&amp;D Program of China","award":["2021YFB3901201"],"award-info":[{"award-number":["2021YFB3901201"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Change detection (CD) in remote sensing imagery has found broad applications in ecosystem service assessment, disaster evaluation, urban planning, land utilization, etc. In this paper, we propose a novel graph model-based method for synthetic aperture radar (SAR) image CD. To mitigate the influence of speckle noise on SAR image CD, we opt for comparing the structures of multi-temporal images instead of the conventional approach of directly comparing pixel values, which is more robust to the speckle noise. Specifically, we first segment the multi-temporal images into square patches at multiple scales and construct multi-scale K-nearest neighbor (KNN) graphs for each image, and then develop an effective graph fusion strategy, facilitating the exploitation of multi-scale information within SAR images, which offers an enhanced representation of the complex relationships among features in the images. Second, we accomplish the interaction of spatio-temporal-radiometric information between graph models through graph mapping, which can efficiently uncover the connections between multi-temporal images, leading to a more precise extraction of changes between the images. Finally, we use the Markov random field (MRF) based segmentation method to obtain the binary change map. Through extensive experimentation on real datasets, we demonstrate the remarkable superiority of our methodologies by comparing with some current state-of-the-art methods.<\/jats:p>","DOI":"10.3390\/rs16030560","type":"journal-article","created":{"date-parts":[[2024,2,1]],"date-time":"2024-02-01T09:43:22Z","timestamp":1706780602000},"page":"560","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A Multi-Scale Graph Based on Spatio-Temporal-Radiometric Interaction for SAR Image Change Detection"],"prefix":"10.3390","volume":"16","author":[{"given":"Peijing","family":"Zhang","sequence":"first","affiliation":[{"name":"College for Informatics and Cyber Security, People\u2019s Public Security University of China, Beijing 100038, China"},{"name":"College of Geosciences and Surveying Engineering, China University of Mining and Technology, Beijing 100083, China"}]},{"given":"Jinbao","family":"Jiang","sequence":"additional","affiliation":[{"name":"College of Geosciences and Surveying Engineering, China University of Mining and Technology, Beijing 100083, China"}]},{"given":"Peng","family":"Kou","sequence":"additional","affiliation":[{"name":"College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China"}]},{"given":"Shining","family":"Wang","sequence":"additional","affiliation":[{"name":"China Waterborne Transport Research Institute, Beijing 100088, China"}]},{"given":"Bin","family":"Wang","sequence":"additional","affiliation":[{"name":"Xi\u2019an Co-Build Regal Technology Co., Ltd., Xi\u2019an 710054, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,1,31]]},"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. 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