{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:59:30Z","timestamp":1760147970616,"version":"build-2065373602"},"reference-count":43,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2023,3,18]],"date-time":"2023-03-18T00:00:00Z","timestamp":1679097600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Special Fund of Hubei Luojia Laboratory","award":["220100028","22YJC880058","2022010801020281","2021CFB539","CCNU22QN011","CCNU22QN019","202143"],"award-info":[{"award-number":["220100028","22YJC880058","2022010801020281","2021CFB539","CCNU22QN011","CCNU22QN019","202143"]}]},{"name":"Ministry of Education of the People\u2019s Republic of China","award":["220100028","22YJC880058","2022010801020281","2021CFB539","CCNU22QN011","CCNU22QN019","202143"],"award-info":[{"award-number":["220100028","22YJC880058","2022010801020281","2021CFB539","CCNU22QN011","CCNU22QN019","202143"]}]},{"name":"Knowledge Innovation Program of Wuhan-Shuguang Project","award":["220100028","22YJC880058","2022010801020281","2021CFB539","CCNU22QN011","CCNU22QN019","202143"],"award-info":[{"award-number":["220100028","22YJC880058","2022010801020281","2021CFB539","CCNU22QN011","CCNU22QN019","202143"]}]},{"DOI":"10.13039\/501100003819","name":"Hubei Provincial Natural Science Foundation","doi-asserted-by":"publisher","award":["220100028","22YJC880058","2022010801020281","2021CFB539","CCNU22QN011","CCNU22QN019","202143"],"award-info":[{"award-number":["220100028","22YJC880058","2022010801020281","2021CFB539","CCNU22QN011","CCNU22QN019","202143"]}],"id":[{"id":"10.13039\/501100003819","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Fundamental Research Funds for the Central Universities","award":["220100028","22YJC880058","2022010801020281","2021CFB539","CCNU22QN011","CCNU22QN019","202143"],"award-info":[{"award-number":["220100028","22YJC880058","2022010801020281","2021CFB539","CCNU22QN011","CCNU22QN019","202143"]}]},{"name":"University-level Educational Reformation Research Project for Undergraduate Education, Central China Normal University, China","award":["220100028","22YJC880058","2022010801020281","2021CFB539","CCNU22QN011","CCNU22QN019","202143"],"award-info":[{"award-number":["220100028","22YJC880058","2022010801020281","2021CFB539","CCNU22QN011","CCNU22QN019","202143"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>High-resolution remote sensing image change detection technology compares and analyzes bi-temporal or multitemporal high-resolution remote sensing images to determine the change areas. It plays an important role in land cover\/use monitoring, natural disaster monitoring, illegal building investigation, military target strike effect analysis, and land and resource investigation. The change detection of high-resolution remote sensing images has developed rapidly from data accumulation to algorithm models because of the rapid development of technologies such as deep learning and earth observation in recent years. However, the current deep learning-based change detection methods are strongly dependent on large sample data, and the training model has insufficient cross-domain generalization ability. As a result, a prior semantic information-guided change detection framework (PSI-CD), which alleviates the change detection model\u2019s dependence on datasets by making full use of prior semantic information, is proposed in this paper. The proposed method mainly includes two parts: one is a prior semantic information generation network that uses the semantic segmentation dataset to extract robust and reliable prior semantic information; the other is the prior semantic information guided change detection network that makes full use of prior semantic information to reduce the sample size of the change detection. To verify the effectiveness of the proposed method, we produced pixel-level semantic labels for the bi-temporal images of the public change detection dataset (LEVIR-CD). Then, we performed extensive experiments on the WHU and LEVIR-CD datasets, including comparisons with existing methods, experiments with different amounts of data, and ablation study, to show the effectiveness of the proposed method. Compared with other existing methods, our method has the highest IoU for all training samples and different amounts of training samples on WHU and LEVIR-CD, reaching a maximum of 83.25% and 83.80%, respectively.<\/jats:p>","DOI":"10.3390\/rs15061655","type":"journal-article","created":{"date-parts":[[2023,3,20]],"date-time":"2023-03-20T03:09:37Z","timestamp":1679281777000},"page":"1655","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Prior Semantic Information Guided Change Detection Method for Bi-temporal High-Resolution Remote Sensing Images"],"prefix":"10.3390","volume":"15","author":[{"given":"Shiyan","family":"Pang","sequence":"first","affiliation":[{"name":"Faculty of Artificial Intelligence in Education, Central China Normal University, 152 Luoyu Road, Wuhan 430079, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinyu","family":"Li","sequence":"additional","affiliation":[{"name":"Faculty of Artificial Intelligence in Education, Central China Normal University, 152 Luoyu Road, Wuhan 430079, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jia","family":"Chen","sequence":"additional","affiliation":[{"name":"Faculty of Artificial Intelligence in Education, Central China Normal University, 152 Luoyu Road, Wuhan 430079, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhiqi","family":"Zuo","sequence":"additional","affiliation":[{"name":"College of Informatics, Huazhong Agricultural University, Wuhan 430070, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"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"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.rse.2015.01.006","article-title":"A critical synthesis of remotely sensed optical image change detection techniques","volume":"160","author":"Tewkesbury","year":"2015","journal-title":"Remote Sens. 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