{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,3]],"date-time":"2025-12-03T18:13:55Z","timestamp":1764785635721,"version":"build-2065373602"},"reference-count":33,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2025,5,20]],"date-time":"2025-05-20T00:00:00Z","timestamp":1747699200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Guangxi Science and Technology Major Project","award":["AA19254016","Bei Kehe 2023158004"],"award-info":[{"award-number":["AA19254016","Bei Kehe 2023158004"]}]},{"name":"Beihai Science and Technology Bureau Project","award":["AA19254016","Bei Kehe 2023158004"],"award-info":[{"award-number":["AA19254016","Bei Kehe 2023158004"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Although some progress has been made in deep learning-based remote sensing image change detection, the complexity of scenes and the diversity of changes in remote sensing images lead to challenges related to background interference. For instance, remote sensing images typically contain numerous background regions, while the actual change regions constitute only a small proportion of the overall image. To address these challenges in remote sensing image change detection, this paper proposes a Dynamic Adaptive Context Attention Network (DACA-Net) based on an exchanging dual encoder\u2013decoder (EDED) architecture. The core innovation of DACA-Net is the development of a novel Dynamic Adaptive Context Attention Module (DACAM), which learns attention weights and automatically adjusts the appropriate scale according to the features present in remote sensing images. By fusing multi-scale contextual features, DACAM effectively captures information regarding changes within these images. In addition, DACA-Net adopts an EDED architectural design, where the conventional convolutional modules in the EDED framework are replaced by DACAM modules. Unlike the original EDED architecture, DACAM modules are embedded after each encoder unit, enabling dynamic recalibration of T1\/T2 features and cross-temporal information interaction. This design facilitates the capture of fine-grained change features at multiple scales. This architecture not only facilitates the extraction of discriminative features but also promotes a form of structural symmetry in the processing pipeline, contributing to more balanced and consistent feature representations. To validate the applicability of our proposed method in real-world scenarios, we constructed an Unmanned Aerial Vehicle (UAV) remote sensing dataset named the Guangxi Beihai Coast Nature Reserves (GBCNR). Extensive experiments conducted on three public datasets and our GBCNR dataset demonstrate that the proposed DACA-Net achieves strong performance across various evaluation metrics. For example, it attains an F1 score (F1) of 72.04% and a precision(P) of 66.59% on the GBCNR dataset, representing improvements of 3.94% and 4.72% over state-of-the-art methods such as semantic guidance and spatial localization network (SGSLN) and bi-temporal image Transformer (BIT), respectively. These results verify that the proposed network significantly enhances the ability to detect critical change regions and improves generalization performance.<\/jats:p>","DOI":"10.3390\/sym17050793","type":"journal-article","created":{"date-parts":[[2025,5,20]],"date-time":"2025-05-20T06:54:28Z","timestamp":1747724068000},"page":"793","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Remote Sensing Image Change Detection Based on Dynamic Adaptive Context Attention"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-1318-5955","authenticated-orcid":false,"given":"Yong","family":"Xie","sequence":"first","affiliation":[{"name":"School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin 541000, China"}]},{"given":"Yixuan","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Architecture and Civil Engineering, City University of Hong Kong, Hong Kong 999077, China"}]},{"given":"Xin","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin 541000, China"},{"name":"School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610000, China"},{"name":"School of Computer Engineering, Guilin University of Electronic Technology, Beihai 536000, China"}]},{"given":"Yin","family":"Tan","sequence":"additional","affiliation":[{"name":"School of Computer Engineering, Guilin University of Electronic Technology, Beihai 536000, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1743-3139","authenticated-orcid":false,"given":"Qin","family":"Qin","sequence":"additional","affiliation":[{"name":"School of Electronic Information, Guilin University of Electronic Technology, Beihai 536000, China"}]}],"member":"1968","published-online":{"date-parts":[[2025,5,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"609","DOI":"10.1109\/TGRS.2015.2463075","article-title":"A novel automatic change detection method for urban high-resolution remotely sensed imagery based on multiindex scene representation","volume":"54","author":"Wen","year":"2015","journal-title":"IEEE Trans. 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