{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,31]],"date-time":"2026-01-31T11:04:36Z","timestamp":1769857476265,"version":"3.49.0"},"reference-count":47,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2024,7,13]],"date-time":"2024-07-13T00:00:00Z","timestamp":1720828800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"China Scholarship Council","award":["KLIGIP-2019B08"],"award-info":[{"award-number":["KLIGIP-2019B08"]}]},{"name":"China Scholarship Council","award":["2024AFB561"],"award-info":[{"award-number":["2024AFB561"]}]},{"name":"China Scholarship Council","award":["202301020102336"],"award-info":[{"award-number":["202301020102336"]}]},{"name":"China Scholarship Council","award":["41925007"],"award-info":[{"award-number":["41925007"]}]},{"name":"Sub-pixel Mapping of Hyperspectral Remote Sensing Images Based on Deep Unfolding Networks","award":["KLIGIP-2019B08"],"award-info":[{"award-number":["KLIGIP-2019B08"]}]},{"name":"Sub-pixel Mapping of Hyperspectral Remote Sensing Images Based on Deep Unfolding Networks","award":["2024AFB561"],"award-info":[{"award-number":["2024AFB561"]}]},{"name":"Sub-pixel Mapping of Hyperspectral Remote Sensing Images Based on Deep Unfolding Networks","award":["202301020102336"],"award-info":[{"award-number":["202301020102336"]}]},{"name":"Sub-pixel Mapping of Hyperspectral Remote Sensing Images Based on Deep Unfolding Networks","award":["41925007"],"award-info":[{"award-number":["41925007"]}]},{"name":"Knowledge Innovation Program of Wuhan\u2013Shuguang","award":["KLIGIP-2019B08"],"award-info":[{"award-number":["KLIGIP-2019B08"]}]},{"name":"Knowledge Innovation Program of Wuhan\u2013Shuguang","award":["2024AFB561"],"award-info":[{"award-number":["2024AFB561"]}]},{"name":"Knowledge Innovation Program of Wuhan\u2013Shuguang","award":["202301020102336"],"award-info":[{"award-number":["202301020102336"]}]},{"name":"Knowledge Innovation Program of Wuhan\u2013Shuguang","award":["41925007"],"award-info":[{"award-number":["41925007"]}]},{"name":"National Natural Science Foundation of China","award":["KLIGIP-2019B08"],"award-info":[{"award-number":["KLIGIP-2019B08"]}]},{"name":"National Natural Science Foundation of China","award":["2024AFB561"],"award-info":[{"award-number":["2024AFB561"]}]},{"name":"National Natural Science Foundation of China","award":["202301020102336"],"award-info":[{"award-number":["202301020102336"]}]},{"name":"National Natural Science Foundation of China","award":["41925007"],"award-info":[{"award-number":["41925007"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Change detection aims to identify the difference between dual-temporal images and has garnered considerable attention over the past decade. Recently, deep learning methods have shown robust feature extraction capabilities and have achieved improved detection results; however, they exhibit limitations in preserving clear boundaries for the identified regions, which is attributed to the inadequate contextual information aggregation capabilities of feature extraction, and fail to adequately constrain the delineation of boundaries. To address this issue, a novel dual-branch feature interaction backbone network integrating the CNN and Transformer architectures to extract pixel-level change information was developed. With our method, contextual feature aggregation can be achieved by using a cross-layer feature fusion module, and a dual-branch upsampling module is employed to incorporate both spatial and channel information, enhancing the precision of the identified change areas. In addition, a boundary constraint is incorporated, leveraging an MLP module to consolidate fragmented edge information, which increases the boundary constraints within the change areas and minimizes boundary blurring effectively. Quantitative and qualitative experiments were conducted on three benchmarks, including LEVIR-CD, WHU Building, and the xBD natural disaster dataset. The comprehensive results show the superiority of the proposed method compared with previous approaches.<\/jats:p>","DOI":"10.3390\/rs16142573","type":"journal-article","created":{"date-parts":[[2024,7,15]],"date-time":"2024-07-15T12:44:36Z","timestamp":1721047476000},"page":"2573","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["A CNN- and Transformer-Based Dual-Branch Network for Change Detection with Cross-Layer Feature Fusion and Edge Constraints"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-6447-7712","authenticated-orcid":false,"given":"Xiaofeng","family":"Wang","sequence":"first","affiliation":[{"name":"School of Computer, China University of Geosciences, Wuhan 430074, China"}]},{"given":"Zhongyu","family":"Guo","sequence":"additional","affiliation":[{"name":"School of Computer, China University of Geosciences, Wuhan 430074, China"}]},{"given":"Ruyi","family":"Feng","sequence":"additional","affiliation":[{"name":"School of Computer, China University of Geosciences, Wuhan 430074, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,7,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"484","DOI":"10.1109\/LGRS.2020.2977838","article-title":"SAR image change detection based on multiscale capsule network","volume":"18","author":"Gao","year":"2020","journal-title":"IEEE Geosci. 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