{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,27]],"date-time":"2025-12-27T07:28:42Z","timestamp":1766820522669,"version":"build-2065373602"},"reference-count":53,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2023,11,7]],"date-time":"2023-11-07T00:00:00Z","timestamp":1699315200000},"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 China","doi-asserted-by":"publisher","award":["62001396","JKW202206","2022ZDLGY06-08","2023-ZDLGY-46"],"award-info":[{"award-number":["62001396","JKW202206","2022ZDLGY06-08","2023-ZDLGY-46"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"RSP National Key Laboratory","award":["62001396","JKW202206","2022ZDLGY06-08","2023-ZDLGY-46"],"award-info":[{"award-number":["62001396","JKW202206","2022ZDLGY06-08","2023-ZDLGY-46"]}]},{"name":"Key Research and Development Program of Shaanxi","award":["62001396","JKW202206","2022ZDLGY06-08","2023-ZDLGY-46"],"award-info":[{"award-number":["62001396","JKW202206","2022ZDLGY06-08","2023-ZDLGY-46"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Over the past decade, deep learning techniques have significantly advanced the field of building change detection in remote sensing imagery. However, existing deep learning-based approaches often encounter limitations in complex remote sensing scenarios, resulting in false detections and detail loss. This paper introduces EFP-Net, a novel building change detection approach that resolves the mentioned issues by utilizing effective feature fusion and foreground perception. EFP-Net comprises three main modules, the feature extraction module (FEM), the spatial\u2013temporal correlation module (STCM), and the residual guidance module (RGM), which jointly enhance the fusion of bi-temporal features and hierarchical features. Specifically, the STCM utilizes the temporal change duality prior and multi-scale perception to augment the 3D convolution modeling capability for bi-temporal feature variations. Additionally, the RGM employs the higher-layer prediction map to guide shallow layer features, reducing the introduction of noise during the hierarchical feature fusion process. Furthermore, a dynamic Focal loss with foreground awareness is developed to mitigate the class imbalance problem. Extensive experiments on the widely adopted WHU-BCD, LEVIR-CD, and CDD datasets demonstrate that the proposed EFP-Net is capable of significantly improving accuracy in building change detection.<\/jats:p>","DOI":"10.3390\/rs15225268","type":"journal-article","created":{"date-parts":[[2023,11,7]],"date-time":"2023-11-07T11:25:31Z","timestamp":1699356331000},"page":"5268","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["EFP-Net: A Novel Building Change Detection Method Based on Efficient Feature Fusion and Foreground Perception"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2191-1121","authenticated-orcid":false,"given":"Renjie","family":"He","sequence":"first","affiliation":[{"name":"Shaanxi Provincial Key Laboratory of Information Acquisition and Processing, Northwestern Polytechnical University, Xi\u2019an 710072, China"},{"name":"Key Laboratory of Archaeological Exploration and Cultural Heritage Conservation Technology (Northwestern Polytechnical University), Ministry of Education, Xi\u2019an 710072, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenyao","family":"Li","sequence":"additional","affiliation":[{"name":"Shaanxi Provincial Key Laboratory of Information Acquisition and Processing, Northwestern Polytechnical University, Xi\u2019an 710072, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8018-596X","authenticated-orcid":false,"given":"Shaohui","family":"Mei","sequence":"additional","affiliation":[{"name":"Shaanxi Provincial Key Laboratory of Information Acquisition and Processing, Northwestern Polytechnical University, Xi\u2019an 710072, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4432-7406","authenticated-orcid":false,"given":"Yuchao","family":"Dai","sequence":"additional","affiliation":[{"name":"Shaanxi Provincial Key Laboratory of Information Acquisition and Processing, Northwestern Polytechnical University, Xi\u2019an 710072, China"},{"name":"Key Laboratory of Archaeological Exploration and Cultural Heritage Conservation Technology (Northwestern Polytechnical University), Ministry of Education, Xi\u2019an 710072, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2051-6955","authenticated-orcid":false,"given":"Mingyi","family":"He","sequence":"additional","affiliation":[{"name":"Shaanxi Provincial Key Laboratory of Information Acquisition and Processing, Northwestern Polytechnical University, Xi\u2019an 710072, China"},{"name":"Key Laboratory of Archaeological Exploration and Cultural Heritage Conservation Technology (Northwestern Polytechnical University), Ministry of Education, Xi\u2019an 710072, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,11,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Fu, L., Li, Y., and Zhang, Y. 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