{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T14:32:00Z","timestamp":1774967520543,"version":"3.50.1"},"reference-count":60,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2024,4,14]],"date-time":"2024-04-14T00:00:00Z","timestamp":1713052800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of PR China","doi-asserted-by":"publisher","award":["42075310"],"award-info":[{"award-number":["42075310"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>In the domains of geographic information systems and remote sensing image analysis, change detection is vital for examining surface variations in high-resolution remote sensing pictures. However, the intricate texture characteristics and rich details found in high-resolution remote sensing photos are difficult for conventional change detection systems to deal with. Target misdetection, missed detections, and edge blurring are further problems with current deep learning-based methods. This research proposes a high-resolution city change detection network based on difference and attention mechanisms under multi-scale feature fusion (MDANet) to address these issues and improve the accuracy of change detection. First, to extract features from dual-temporal remote sensing pictures, we use the Siamese architecture as the encoder network. The Difference Feature Module (DFM) is employed to learn the difference information between the dual-temporal remote sensing images. Second, the extracted difference features are optimized with the Attention Refinement Module (ARM). The Cross-Scale Fusion Module (CSFM) combines and enhances the optimized attention features, effectively capturing subtle differences in remote sensing images and learning the finer details of change targets. Finally, thorough tests on the BTCDD dataset, LEVIR-CD dataset, and CDD dataset show that the MDANet algorithm performs at a cutting-edge level.<\/jats:p>","DOI":"10.3390\/rs16081387","type":"journal-article","created":{"date-parts":[[2024,4,15]],"date-time":"2024-04-15T03:56:13Z","timestamp":1713153373000},"page":"1387","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":42,"title":["MDANet: A High-Resolution City Change Detection Network Based on Difference and Attention Mechanisms under Multi-Scale Feature Fusion"],"prefix":"10.3390","volume":"16","author":[{"given":"Shanshan","family":"Jiang","sequence":"first","affiliation":[{"name":"School of Management Science and Engineering, 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 210037, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4235-3598","authenticated-orcid":false,"given":"Hongjin","family":"Ren","sequence":"additional","affiliation":[{"name":"Jiangsu Key Laboratory of Big Data Analysis Technology, Nanjing University of Information Science and Technology, Nanjing 210044, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9994-771X","authenticated-orcid":false,"given":"Ziwei","family":"Hu","sequence":"additional","affiliation":[{"name":"Jiangsu Key Laboratory of Big Data Analysis Technology, Nanjing University of Information Science and Technology, Nanjing 210044, China"}]},{"given":"Liguo","family":"Weng","sequence":"additional","affiliation":[{"name":"Jiangsu Key Laboratory of Big Data Analysis Technology, 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":"Jiangsu Key Laboratory of Big Data Analysis Technology, Nanjing University of Information Science and Technology, Nanjing 210044, China"},{"name":"Collaborative Innovation Center on Atmospheric Environment and Equipment Technology, Nanjing University of Information Science and Technology, Nanjing 210044, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,4,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"6550","DOI":"10.1073\/pnas.0608998104","article-title":"Combined climate and carbon-cycle effects of large-scale deforestation","volume":"104","author":"Bala","year":"2007","journal-title":"Proc. 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