{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T16:45:52Z","timestamp":1776357952420,"version":"3.51.2"},"reference-count":195,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2022,3,23]],"date-time":"2022-03-23T00:00:00Z","timestamp":1647993600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Chinese National Natural Science Foundation Projects","award":["92038301"],"award-info":[{"award-number":["92038301"]}]},{"name":"Chinese National Natural Science Foundation Projects","award":["41771363"],"award-info":[{"award-number":["41771363"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Change detection based on remote sensing images plays an important role in the field of remote sensing analysis, and it has been widely used in many areas, such as resources monitoring, urban planning, disaster assessment, etc. In recent years, it has aroused widespread interest due to the explosive development of artificial intelligence (AI) technology, and change detection algorithms based on deep learning frameworks have made it possible to detect more delicate changes (such as the alteration of small buildings) with the help of huge amounts of remote sensing data, especially high-resolution (HR) data. Although there are many methods, we still lack a deep review of the recent progress concerning the latest deep learning methods in change detection. To this end, the main purpose of this paper is to provide a review of the available deep learning-based change detection algorithms using HR remote sensing images. The paper first describes the change detection framework and classifies the methods from the perspective of the deep network architectures adopted. Then, we review the latest progress in the application of deep learning in various granularity structures for change detection. Further, the paper provides a summary of HR datasets derived from different sensors, along with information related to change detection, for the potential use of researchers. Simultaneously, representative evaluation metrics for this task are investigated. Finally, a conclusion of the challenges for change detection using HR remote sensing images, which must be dealt with in order to improve the model\u2019s performance, is presented. In addition, we put forward promising directions for future research in this area.<\/jats:p>","DOI":"10.3390\/rs14071552","type":"journal-article","created":{"date-parts":[[2022,3,23]],"date-time":"2022-03-23T22:08:06Z","timestamp":1648073286000},"page":"1552","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":247,"title":["A Survey on Deep Learning-Based Change Detection from High-Resolution Remote Sensing Images"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1560-5577","authenticated-orcid":false,"given":"Huiwei","family":"Jiang","sequence":"first","affiliation":[{"name":"National Geomatics Center of China, Beijing 100830, China"},{"name":"School of Remote Sensing and Information Engineering, Wuhan University, 129 Luoyu Road, Wuhan 430079, China"}]},{"given":"Min","family":"Peng","sequence":"additional","affiliation":[{"name":"Geotechnical Investigation & Surveying Research Institute Co., Ltd., Shenyang 110004, China"}]},{"given":"Yuanjun","family":"Zhong","sequence":"additional","affiliation":[{"name":"Guangdong Surveying and Mapping Institute of Lands and Resource Department, No. 13 Guangpu Middle Road, Huangpu District, Guangzhou 510663, China"}]},{"given":"Haofeng","family":"Xie","sequence":"additional","affiliation":[{"name":"School of Remote Sensing and Information Engineering, Wuhan University, 129 Luoyu Road, Wuhan 430079, China"}]},{"given":"Zemin","family":"Hao","sequence":"additional","affiliation":[{"name":"Geotechnical Investigation & Surveying Research Institute Co., Ltd., Shenyang 110004, China"}]},{"given":"Jingming","family":"Lin","sequence":"additional","affiliation":[{"name":"School of Remote Sensing and Information Engineering, Wuhan University, 129 Luoyu Road, Wuhan 430079, China"}]},{"given":"Xiaoli","family":"Ma","sequence":"additional","affiliation":[{"name":"Guangdong Surveying and Mapping Institute of Lands and Resource Department, No. 13 Guangpu Middle Road, Huangpu District, Guangzhou 510663, China"}]},{"given":"Xiangyun","family":"Hu","sequence":"additional","affiliation":[{"name":"School of Remote Sensing and Information Engineering, Wuhan University, 129 Luoyu Road, Wuhan 430079, China"},{"name":"Institute of Artificial Intelligence in Geomatics, Wuhan University, 129 Luoyu Road, Wuhan 430079, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,3,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"989","DOI":"10.1080\/01431168908903939","article-title":"Digital change detection techniques using remotely-sensed data","volume":"10","author":"Singh","year":"1989","journal-title":"Int. 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