{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T06:46:54Z","timestamp":1780469214517,"version":"3.54.1"},"reference-count":239,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2024,6,27]],"date-time":"2024-06-27T00:00:00Z","timestamp":1719446400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Change detection is an essential and widely utilized task in remote sensing that aims to detect and analyze changes occurring in the same geographical area over time, which has broad applications in urban development, agricultural surveys, and land cover monitoring. Detecting changes in remote sensing images is a complex challenge due to various factors, including variations in image quality, noise, registration errors, illumination changes, complex landscapes, and spatial heterogeneity. In recent years, deep learning has emerged as a powerful tool for feature extraction and addressing these challenges. Its versatility has resulted in its widespread adoption for numerous image-processing tasks. This paper presents a comprehensive survey of significant advancements in change detection for remote sensing images over the past decade. We first introduce some preliminary knowledge for the change detection task, such as problem definition, datasets, evaluation metrics, and transformer basics, as well as provide a detailed taxonomy of existing algorithms from three different perspectives: algorithm granularity, supervision modes, and frameworks in the Methodology section. This survey enables readers to gain systematic knowledge of change detection tasks from various angles. We then summarize the state-of-the-art performance on several dominant change detection datasets, providing insights into the strengths and limitations of existing algorithms. Based on our survey, some future research directions for change detection in remote sensing are well identified. This survey paper sheds some light the topic for the community and will inspire further research efforts in the change detection task.<\/jats:p>","DOI":"10.3390\/rs16132355","type":"journal-article","created":{"date-parts":[[2024,6,27]],"date-time":"2024-06-27T11:19:02Z","timestamp":1719487142000},"page":"2355","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":208,"title":["Change Detection Methods for Remote Sensing in the Last Decade: A Comprehensive Review"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8686-9513","authenticated-orcid":false,"given":"Guangliang","family":"Cheng","sequence":"first","affiliation":[{"name":"Department of Computer Science, University of Liverpool, Liverpool L69 3BX, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yunmeng","family":"Huang","sequence":"additional","affiliation":[{"name":"Department of Electronic Information Engineering, Beihang University, Beijing 100191, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiangtai","family":"Li","sequence":"additional","affiliation":[{"name":"School of Intelligence Science and Technology, Peking University, Beijing 100871, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9769-7083","authenticated-orcid":false,"given":"Shuchang","family":"Lyu","sequence":"additional","affiliation":[{"name":"Department of Electronic Information Engineering, Beihang University, Beijing 100191, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhaoyang","family":"Xu","sequence":"additional","affiliation":[{"name":"Department of Paediatrics, Cambridge University, Cambridge CB2 1TN, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1196-4089","authenticated-orcid":false,"given":"Hongbo","family":"Zhao","sequence":"additional","affiliation":[{"name":"Department of Electronic Information Engineering, Beihang University, Beijing 100191, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3508-027X","authenticated-orcid":false,"given":"Qi","family":"Zhao","sequence":"additional","affiliation":[{"name":"Department of Electronic Information Engineering, Beihang University, Beijing 100191, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2089-9733","authenticated-orcid":false,"given":"Shiming","family":"Xiang","sequence":"additional","affiliation":[{"name":"Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,6,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"920","DOI":"10.1109\/TITS.2011.2119372","article-title":"A Review of Computer Vision Techniques for the Analysis of Urban Traffic","volume":"12","author":"Buch","year":"2011","journal-title":"IEEE Trans. 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