Using Octrees to Detect Changes to Buildings and Trees in the Urban Environment from Airborne LiDAR Data
Change detection is a major issue for urban area monitoring. In this paper, a new three-step point-based method for detecting changes to buildings and trees using airborne light detection and ranging (LiDAR) data is proposed. First, the airborne LiDAR data from two dates are accurately registered us...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2015-07-01
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| Series: | Remote Sensing |
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| Online Access: | http://www.mdpi.com/2072-4292/7/8/9682 |
| _version_ | 1818998352689233920 |
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| author | Hao Xu Liang Cheng Manchun Li Yanming Chen Lishan Zhong |
| author_facet | Hao Xu Liang Cheng Manchun Li Yanming Chen Lishan Zhong |
| author_sort | Hao Xu |
| collection | DOAJ |
| description | Change detection is a major issue for urban area monitoring. In this paper, a new three-step point-based method for detecting changes to buildings and trees using airborne light detection and ranging (LiDAR) data is proposed. First, the airborne LiDAR data from two dates are accurately registered using the iterative closest point algorithm, and a progressive triangulated irregular network densification filtering algorithm is used to separate ground points from non-ground points. Second, an octree is generated from the non-ground points to store and index the irregularly-distributed LiDAR points. Finally, by comparing the LiDAR points from two dates and using the AutoClust algorithm, those areas of buildings and trees in the urban environment that have changed are determined effectively and efficiently. The key contributions of this approach are the development of a point-based method to effectively solve the problem of objects at different scales, and the establishment of rules to detect changes in buildings and trees to urban areas, enabling the use of the point-based method over large areas. To evaluate the proposed method, a series of experiments using aerial images are conducted. The results demonstrate that satisfactory performance can be obtained using the proposed approach. |
| first_indexed | 2024-12-20T22:00:10Z |
| format | Article |
| id | doaj.art-e92c94921e094e28b673dc12d99c0425 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-12-20T22:00:10Z |
| publishDate | 2015-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-e92c94921e094e28b673dc12d99c04252022-12-21T19:25:22ZengMDPI AGRemote Sensing2072-42922015-07-01789682970410.3390/rs70809682rs70809682Using Octrees to Detect Changes to Buildings and Trees in the Urban Environment from Airborne LiDAR DataHao Xu0Liang Cheng1Manchun Li2Yanming Chen3Lishan Zhong4Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Nanjing University, Nanjing 210093, ChinaJiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Nanjing University, Nanjing 210093, ChinaJiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Nanjing University, Nanjing 210093, ChinaJiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Nanjing University, Nanjing 210093, ChinaJiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Nanjing University, Nanjing 210093, ChinaChange detection is a major issue for urban area monitoring. In this paper, a new three-step point-based method for detecting changes to buildings and trees using airborne light detection and ranging (LiDAR) data is proposed. First, the airborne LiDAR data from two dates are accurately registered using the iterative closest point algorithm, and a progressive triangulated irregular network densification filtering algorithm is used to separate ground points from non-ground points. Second, an octree is generated from the non-ground points to store and index the irregularly-distributed LiDAR points. Finally, by comparing the LiDAR points from two dates and using the AutoClust algorithm, those areas of buildings and trees in the urban environment that have changed are determined effectively and efficiently. The key contributions of this approach are the development of a point-based method to effectively solve the problem of objects at different scales, and the establishment of rules to detect changes in buildings and trees to urban areas, enabling the use of the point-based method over large areas. To evaluate the proposed method, a series of experiments using aerial images are conducted. The results demonstrate that satisfactory performance can be obtained using the proposed approach.http://www.mdpi.com/2072-4292/7/8/9682change detectionoctreeairborne LiDARbuildingstreesurban environment |
| spellingShingle | Hao Xu Liang Cheng Manchun Li Yanming Chen Lishan Zhong Using Octrees to Detect Changes to Buildings and Trees in the Urban Environment from Airborne LiDAR Data Remote Sensing change detection octree airborne LiDAR buildings trees urban environment |
| title | Using Octrees to Detect Changes to Buildings and Trees in the Urban Environment from Airborne LiDAR Data |
| title_full | Using Octrees to Detect Changes to Buildings and Trees in the Urban Environment from Airborne LiDAR Data |
| title_fullStr | Using Octrees to Detect Changes to Buildings and Trees in the Urban Environment from Airborne LiDAR Data |
| title_full_unstemmed | Using Octrees to Detect Changes to Buildings and Trees in the Urban Environment from Airborne LiDAR Data |
| title_short | Using Octrees to Detect Changes to Buildings and Trees in the Urban Environment from Airborne LiDAR Data |
| title_sort | using octrees to detect changes to buildings and trees in the urban environment from airborne lidar data |
| topic | change detection octree airborne LiDAR buildings trees urban environment |
| url | http://www.mdpi.com/2072-4292/7/8/9682 |
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