{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T04:16:26Z","timestamp":1760242586828,"version":"build-2065373602"},"reference-count":59,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2017,11,29]],"date-time":"2017-11-29T00:00:00Z","timestamp":1511913600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61631011","61331016"],"award-info":[{"award-number":["61631011","61331016"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Change detection has been widely used in remote sensing, such as for disaster assessment and urban expansion detection. Although it is convenient to use unsupervised methods to detect changes from multi-temporal images, the results could be further improved. In supervised methods, heavy data labelling tasks are needed, and the sample annotation process with real categories is tedious and costly. To relieve the burden of labelling and to obtain satisfactory results, we propose an interactive change detection framework based on active learning and Markov random field (MRF). More specifically, a limited number of representative objects are found in an unsupervised way at the beginning. Then, the very limited samples are labelled as \u201cchange\u201d or \u201cno change\u201d to train a simple binary classification model, i.e., a Gaussian process model. By using this model, we then select and label the most informative samples by \u201cthe easiest\u201d sample selection strategy to update the former weak classification model until the detection results do not change notably. Finally, the maximum a posteriori (MAP) change detection is efficiently computed via the min-cut-based integer optimization algorithm. The time consuming and laborious manual labelling process can be reduced substantially, and a desirable detection result can be obtained. The experiments on several WorldView-2 images demonstrate the effectiveness of the proposed method.<\/jats:p>","DOI":"10.3390\/rs9121233","type":"journal-article","created":{"date-parts":[[2017,11,30]],"date-time":"2017-11-30T03:15:15Z","timestamp":1512011715000},"page":"1233","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":37,"title":["Change Detection Using High Resolution Remote Sensing Images Based on Active Learning and Markov Random Fields"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6043-3412","authenticated-orcid":false,"given":"Huai","family":"Yu","sequence":"first","affiliation":[{"name":"School of Electronic Information, Wuhan University, Wuhan 430072, China"},{"name":"The CETC Key Laboratory of Aerospace Information Applications, Shijiazhuang 050081, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3263-8768","authenticated-orcid":false,"given":"Wen","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Electronic Information, Wuhan University, Wuhan 430072, China"},{"name":"The CETC Key Laboratory of Aerospace Information Applications, Shijiazhuang 050081, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guang","family":"Hua","sequence":"additional","affiliation":[{"name":"School of Electronic Information, Wuhan University, Wuhan 430072, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hui","family":"Ru","sequence":"additional","affiliation":[{"name":"School of Electronic Information, Wuhan University, Wuhan 430072, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pingping","family":"Huang","sequence":"additional","affiliation":[{"name":"College of Information Engineer, Inner Mongolia University of Technology, Hohhot 101051, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2017,11,29]]},"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":"1988","journal-title":"Int. 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