{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T04:28:59Z","timestamp":1785385739639,"version":"3.55.0"},"reference-count":198,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2022,2,11]],"date-time":"2022-02-11T00:00:00Z","timestamp":1644537600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Guo Cao","award":["Grant BK20191284 and Grants 61801222"],"award-info":[{"award-number":["Grant BK20191284 and Grants 61801222"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Images gathered from different satellites are vastly available these days due to the fast development of remote sensing (RS) technology. These images significantly enhance the data sources of change detection (CD). CD is a technique of recognizing the dissimilarities in the images acquired at distinct intervals and are used for numerous applications, such as urban area development, disaster management, land cover object identification, etc. In recent years, deep learning (DL) techniques have been used tremendously in change detection processes, where it has achieved great success because of their practical applications. Some researchers have even claimed that DL approaches outperform traditional approaches and enhance change detection accuracy. Therefore, this review focuses on deep learning techniques, such as supervised, unsupervised, and semi-supervised for different change detection datasets, such as SAR, multispectral, hyperspectral, VHR, and heterogeneous images, and their advantages and disadvantages will be highlighted. In the end, some significant challenges are discussed to understand the context of improvements in change detection datasets and deep learning models. Overall, this review will be beneficial for the future development of CD methods.<\/jats:p>","DOI":"10.3390\/rs14040871","type":"journal-article","created":{"date-parts":[[2022,2,13]],"date-time":"2022-02-13T20:34:45Z","timestamp":1644784485000},"page":"871","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":397,"title":["Deep Learning-Based Change Detection in Remote Sensing Images: A Review"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6298-7179","authenticated-orcid":false,"given":"Ayesha","family":"Shafique","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2689-0932","authenticated-orcid":false,"given":"Guo","family":"Cao","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zia","family":"Khan","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Central South University, Changsha 410083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0036-1714","authenticated-orcid":false,"given":"Muhammad","family":"Asad","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Nagoya Institute of Technology, Nagoya 466-8555, Aichi, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Muhammad","family":"Aslam","sequence":"additional","affiliation":[{"name":"School of Computing, Engineering and Physical Sciences, University of West of Scotland, Blantyre, Glasgow G72 0LH, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,2,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Varghese, A., Gubbi, J., Ramaswamy, A., and Balamuralidhar, P. 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