{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,20]],"date-time":"2026-04-20T15:58:45Z","timestamp":1776700725592,"version":"3.51.2"},"reference-count":34,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2018,3,24]],"date-time":"2018-03-24T00:00:00Z","timestamp":1521849600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Nature Science Foundation of China","award":["41701389"],"award-info":[{"award-number":["41701389"]}]},{"name":"National Nature Science Foundation of China","award":["41771363"],"award-info":[{"award-number":["41771363"]}]},{"name":"China Postdoctoral Science Foundation funded project","award":["2016M602363"],"award-info":[{"award-number":["2016M602363"]}]},{"name":"the research funding by Guangzhou city of China","award":["201604010123"],"award-info":[{"award-number":["201604010123"]}]},{"name":"the Fundamental Research Funds for the Central Universities","award":["2042017kf0023"],"award-info":[{"award-number":["2042017kf0023"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In this work, a novel building change detection method from bi-temporal dense-matching point clouds and aerial images is proposed to address two major problems, namely, the robust acquisition of the changed objects above ground and the automatic classification of changed objects into buildings or non-buildings. For the acquisition of changed objects above ground, the change detection problem is converted into a binary classification, in which the changed area above ground is regarded as the foreground and the other area as the background. For the gridded points of each period, the graph cuts algorithm is adopted to classify the points into foreground and background, followed by the region-growing algorithm to form candidate changed building objects. A novel structural feature that was extracted from aerial images is constructed to classify the candidate changed building objects into buildings and non-buildings. The changed building objects are further classified as \u201cnewly built\u201d, \u201ctaller\u201d, \u201cdemolished\u201d, and \u201clower\u201d by combining the classification and the digital surface models of two periods. Finally, three typical areas from a large dataset are used to validate the proposed method. Numerous experiments demonstrate the effectiveness of the proposed algorithm.<\/jats:p>","DOI":"10.3390\/s18040966","type":"journal-article","created":{"date-parts":[[2018,3,26]],"date-time":"2018-03-26T03:43:29Z","timestamp":1522035809000},"page":"966","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Building Change Detection from Bi-Temporal Dense-Matching Point Clouds and Aerial Images"],"prefix":"10.3390","volume":"18","author":[{"given":"Shiyan","family":"Pang","sequence":"first","affiliation":[{"name":"Collaborative Innovation Center of Geospatial Technology, Wuhan University, Wuhan 430079, China"},{"name":"School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiangyun","family":"Hu","sequence":"additional","affiliation":[{"name":"Collaborative Innovation Center of Geospatial Technology, Wuhan University, Wuhan 430079, China"},{"name":"School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhongliang","family":"Cai","sequence":"additional","affiliation":[{"name":"School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinqi","family":"Gong","sequence":"additional","affiliation":[{"name":"School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mi","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2018,3,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1016\/j.isprsjprs.2003.09.007","article-title":"Object-based classification of remote sensing data for change detection","volume":"58","author":"Walter","year":"2004","journal-title":"ISPRS J. 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