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Presently, post-classification techniques stand as the predominant strategy for SCD due to their simplicity and efficacy. However, these methods often overlook the intricate relationships between alterations in land cover. In this paper, we argue that comprehending the interplay of changes within land cover maps holds the key to enhancing SCD\u2019s performance. With this insight, a Temporal-Transform Module (TTM) is designed to capture change relationships across temporal dimensions. TTM selectively aggregates features across all temporal images, enhancing the unique features of each temporal image at distinct pixels. Moreover, we build a Temporal-Transform Network (TTNet) for SCD, comprising two semantic segmentation branches and a binary change detection branch. TTM is embedded into the decoder of each semantic segmentation branch, thus enabling TTNet to obtain better land cover classification results. Experimental results on the SECOND dataset show that TTNet achieves enhanced performance when compared to other benchmark methods in the SCD task. In particular, TTNet elevates mIoU accuracy by a minimum of 1.5% in the SCD task and 3.1% in the semantic segmentation task.<\/jats:p>","DOI":"10.3390\/rs15184555","type":"journal-article","created":{"date-parts":[[2023,9,17]],"date-time":"2023-09-17T23:32:27Z","timestamp":1694993547000},"page":"4555","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["TTNet: A Temporal-Transform Network for Semantic Change Detection Based on Bi-Temporal Remote Sensing Images"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4994-7644","authenticated-orcid":false,"given":"Liangcun","family":"Jiang","sequence":"first","affiliation":[{"name":"School of Resources and Environmental Engineering, Wuhan University of Technology, Wuhan 430070, China"}]},{"given":"Feng","family":"Li","sequence":"additional","affiliation":[{"name":"School of Resources and Environmental Engineering, Wuhan University of Technology, Wuhan 430070, China"}]},{"given":"Li","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China"},{"name":"Huawei Cloud & AI, Beijing 100085, China"}]},{"given":"Feifei","family":"Peng","sequence":"additional","affiliation":[{"name":"Key Laboratory for Geographical Process Analysis & Simulation of Hubei Province, Central China Normal University, Wuhan 430079, China"},{"name":"College of Urban and Environmental Sciences, Central China Normal University, Wuhan 430079, China"}]},{"given":"Lei","family":"Hu","sequence":"additional","affiliation":[{"name":"School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,9,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"989","DOI":"10.1080\/01431168908903939","article-title":"Review Article Digital Change Detection Techniques Using Remotely-Sensed Data","volume":"10","author":"Singh","year":"1989","journal-title":"Int. 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