Building Change Detection from Bi-Temporal Dense-Matching Point Clouds and Aerial Images
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 no...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2018-03-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | http://www.mdpi.com/1424-8220/18/4/966 |
| _version_ | 1798039990815424512 |
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| author | Shiyan Pang Xiangyun Hu Zhongliang Cai Jinqi Gong Mi Zhang |
| author_facet | Shiyan Pang Xiangyun Hu Zhongliang Cai Jinqi Gong Mi Zhang |
| author_sort | Shiyan Pang |
| collection | DOAJ |
| description | 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 “newly built”, “taller”, “demolished”, and “lower” 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. |
| first_indexed | 2024-04-11T22:01:19Z |
| format | Article |
| id | doaj.art-67893b59b1ce45e7a859f6886e2bbfbc |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T22:01:19Z |
| publishDate | 2018-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-67893b59b1ce45e7a859f6886e2bbfbc2022-12-22T04:00:56ZengMDPI AGSensors1424-82202018-03-0118496610.3390/s18040966s18040966Building Change Detection from Bi-Temporal Dense-Matching Point Clouds and Aerial ImagesShiyan Pang0Xiangyun Hu1Zhongliang Cai2Jinqi Gong3Mi Zhang4Collaborative Innovation Center of Geospatial Technology, Wuhan University, Wuhan 430079, ChinaCollaborative Innovation Center of Geospatial Technology, Wuhan University, Wuhan 430079, ChinaSchool of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, ChinaSchool of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, ChinaSchool of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, ChinaIn 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 “newly built”, “taller”, “demolished”, and “lower” 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.http://www.mdpi.com/1424-8220/18/4/966building change detectiondigital surface modelstructural featurepoint cloudaerial images |
| spellingShingle | Shiyan Pang Xiangyun Hu Zhongliang Cai Jinqi Gong Mi Zhang Building Change Detection from Bi-Temporal Dense-Matching Point Clouds and Aerial Images Sensors building change detection digital surface model structural feature point cloud aerial images |
| title | Building Change Detection from Bi-Temporal Dense-Matching Point Clouds and Aerial Images |
| title_full | Building Change Detection from Bi-Temporal Dense-Matching Point Clouds and Aerial Images |
| title_fullStr | Building Change Detection from Bi-Temporal Dense-Matching Point Clouds and Aerial Images |
| title_full_unstemmed | Building Change Detection from Bi-Temporal Dense-Matching Point Clouds and Aerial Images |
| title_short | Building Change Detection from Bi-Temporal Dense-Matching Point Clouds and Aerial Images |
| title_sort | building change detection from bi temporal dense matching point clouds and aerial images |
| topic | building change detection digital surface model structural feature point cloud aerial images |
| url | http://www.mdpi.com/1424-8220/18/4/966 |
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