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Recently, transformer-based methodologies have advanced from their powerful global modeling capabilities in RSCD tasks. Nevertheless, they remain under excessive parameterization, which continues to be severely constrained by time and computation resources. Here, we present a transformer-based RSCD model called the Segmentation Multi-Branch Change Detection Network (SMBCNet). Our proposed approach combines a hierarchically structured transformer encoder with a cross-scale enhancement module (CEM) to extract global information with lower complexity. To account for the diverse nature of changes, we introduce a plug-and-play multi-branch change fusion module (MCFM) that integrates temporal features. Within this module, we transform the change detection task into a semantic segmentation problem. Moreover, we identify the Temporal Feature Aggregation Module (TFAM) to facilitate integrating features from diverse spatial scales. These results demonstrate that semantic segmentation is an effective solution to change detection (CD) problems in remote sensing images.<\/jats:p>","DOI":"10.3390\/rs15143566","type":"journal-article","created":{"date-parts":[[2023,7,17]],"date-time":"2023-07-17T00:56:47Z","timestamp":1689555407000},"page":"3566","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["SMBCNet: A Transformer-Based Approach for Change Detection in Remote Sensing Images through Semantic Segmentation"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9155-1865","authenticated-orcid":false,"given":"Jiangfan","family":"Feng","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinyu","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhujun","family":"Gu","sequence":"additional","affiliation":[{"name":"Pearl River Water Resources Research Institute, Pearl River Water Resources Commission, Guangzhou 510610, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maimai","family":"Zeng","sequence":"additional","affiliation":[{"name":"Pearl River Water Resources Research Institute, Pearl River Water Resources Commission, Guangzhou 510610, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-9833-5543","authenticated-orcid":false,"given":"Wei","family":"Zheng","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,7,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Shafique, A., Cao, G., Khan, Z., Asad, M., and Aslam, M. 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