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3D Change Detection Using Adaptive Thresholds Based on Local Point Cloud Density
| Content Provider | MDPI |
|---|---|
| Author | Liu, Dan Li, Dajun Wang, Meizhen Wang, Zhiming |
| Copyright Year | 2021 |
| Description | In recent years, because of highly developed LiDAR (Light Detection and Ranging) technologies, there has been increasing demand for 3D change detection in urban monitoring, urban model updating, and disaster assessment. In order to improve the effectiveness of 3D change detection based on point clouds, an approach for 3D change detection using point-based comparison is presented in this paper. To avoid density variation in point clouds, adaptive thresholds are calculated through the k-neighboring average distance and the local point cloud density. A series of experiments for quantitative evaluation is performed. In the experiments, the influencing factors including threshold, registration error, and neighboring number of 3D change detection are discussed and analyzed. The results of the experiments demonstrate that the approach using adaptive thresholds based on local point cloud density are effective and suitable. |
| Starting Page | 127 |
| e-ISSN | 22209964 |
| DOI | 10.3390/ijgi10030127 |
| Journal | ISPRS International Journal of Geo-Information |
| Issue Number | 3 |
| Volume Number | 10 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2021-03-02 |
| Access Restriction | Open |
| Subject Keyword | ISPRS International Journal of Geo-Information Isprs International Journal of Geo-information Imaging Science Remote Sensing 3d Change Detection Adaptive Thresholds Point-based Comparison Point Clouds |
| Content Type | Text |
| Resource Type | Article |
