Abstract
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.
Highlights
Change detection is a major topic of research in remote sensing, which plays an essential role in various tasks such as urban planning and environmental monitoring [1,2,3,4].Three-dimensional change detection is a relatively new topic that extends change detection on 2D data to a 3D space
With the rapid development of three-dimensional laser scanning technology and the perfect progress of point cloud processing, point clouds acquired by airborne laser scanning and mobile laser scanning have been increasingly adopted in 3D change detection [11,12,13,14,15,16,17]
Some studies extracted changes by classifying digital surface models (DSMs) derived from point clouds
Summary
Change detection is a major topic of research in remote sensing, which plays an essential role in various tasks such as urban planning and environmental monitoring [1,2,3,4]. Similar technologies have been presented in other studies, such as Chaabouni-Chouayakh et al [27], Stal et al [12], and Yamzaki et al [28] This method cannot detect changes in an object’s profile information since this technology mainly achieves change detection through height difference from DSMs. Some studies extracted changes by classifying DSMs derived from point clouds. (2) Point-based comparison: This approach directly calculates distances between points in LiDAR data. Performed direct comparisons with the octree structure It is more practical for point cloud data because 3D changes can be obtained directly by point-based comparison. The certain threshold is not appropriate for change detection from the point clouds of density variation. Adaptive thresholds based on local point cloud density are presented for point-based comparison. The threshold values are defined based on the k-neighboring average distance and the local point cloud density. The influencing factors including threshold, registration error, and neighboring number of 3D change detection are analyzed in the experiments
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