{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T19:40:44Z","timestamp":1771702844123,"version":"3.50.1"},"reference-count":43,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2023,4,19]],"date-time":"2023-04-19T00:00:00Z","timestamp":1681862400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Research Funding of Satellite Information Intelligent Processing and Application Research Laboratory"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Remote sensing change detection involves detecting pixels that have changed from a bi-temporal image of the same location. Current mainstream change detection models use encoder-decoder structures as well as Siamese networks. However, there are still some challenges with this: (1) Existing change feature fusion approaches do not take into account the symmetry of change features, which leads to information loss; (2) The encoder is independent of the change detection task, and feature extraction is performed separately for dual-time images, which leads to underutilization of the encoder parameters; (3) There are problems of unbalanced positive and negative samples and bad edge region detection. To solve the above problems, a mutual feature-aware network (MFNet) is proposed in this paper. Three modules are proposed for the purpose: (1) A symmetric change feature fusion module (SCFM), which uses double-branch feature selection without losing feature information and focuses explicitly on focal spatial regions based on cosine similarity to introduce strong a priori information; (2) A mutual feature-aware module (MFAM), which introduces change features in advance at the encoder stage and uses a cross-type attention mechanism for long-range dependence modeling; (3) A loss function for edge regions. After detailed experiments, the F1 scores of MFNet on SYSU-CD and LEVIR-CD were 83.11% and 91.52%, respectively, outperforming several advanced algorithms, demonstrating the effectiveness of the proposed method.<\/jats:p>","DOI":"10.3390\/rs15082145","type":"journal-article","created":{"date-parts":[[2023,4,20]],"date-time":"2023-04-20T01:42:39Z","timestamp":1681954959000},"page":"2145","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["MFNet: Mutual Feature-Aware Networks for Remote Sensing Change Detection"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-6120-0801","authenticated-orcid":false,"given":"Qi","family":"Zhang","sequence":"first","affiliation":[{"name":"National Key Laboratory of Human-Machine Hybrid Augmented Intelligence, Xi\u2019an Jiaotong University, Xi\u2019an 710049, China"},{"name":"National Engineering Research Center for Visual Information and Applications, Xi\u2019an Jiaotong University, Xi\u2019an 710049, China"}]},{"given":"Yao","family":"Lu","sequence":"additional","affiliation":[{"name":"Beijing Institute of Remote Sensing, Beijing 100011, China"}]},{"given":"Sicheng","family":"Shao","sequence":"additional","affiliation":[{"name":"National Key Laboratory of Human-Machine Hybrid Augmented Intelligence, Xi\u2019an Jiaotong University, Xi\u2019an 710049, China"},{"name":"National Engineering Research Center for Visual Information and Applications, Xi\u2019an Jiaotong University, Xi\u2019an 710049, China"},{"name":"Institute of Artificial Intelligence and Robotics, Xi\u2019an Jiaotong University, Xi\u2019an 710049, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7957-4442","authenticated-orcid":false,"given":"Li","family":"Shen","sequence":"additional","affiliation":[{"name":"Beijing Institute of Remote Sensing, Beijing 100011, China"}]},{"given":"Fei","family":"Wang","sequence":"additional","affiliation":[{"name":"National Key Laboratory of Human-Machine Hybrid Augmented Intelligence, Xi\u2019an Jiaotong University, Xi\u2019an 710049, China"},{"name":"National Engineering Research Center for Visual Information and Applications, Xi\u2019an Jiaotong University, Xi\u2019an 710049, China"},{"name":"Institute of Artificial Intelligence and Robotics, Xi\u2019an Jiaotong University, Xi\u2019an 710049, China"}]},{"given":"Xuetao","family":"Zhang","sequence":"additional","affiliation":[{"name":"National Key Laboratory of Human-Machine Hybrid Augmented Intelligence, Xi\u2019an Jiaotong University, Xi\u2019an 710049, China"},{"name":"National Engineering Research Center for Visual Information and Applications, Xi\u2019an Jiaotong University, Xi\u2019an 710049, China"},{"name":"Institute of Artificial Intelligence and Robotics, Xi\u2019an Jiaotong University, Xi\u2019an 710049, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,4,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1007\/s12145-019-00380-5","article-title":"Change detection techniques for remote sensing applications: A survey","volume":"12","author":"Asokan","year":"2019","journal-title":"Earth Sci. 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