A multi-feature clustering-based hierarchical filtering method for airborne LiDAR point clouds in complex landscapes

doi:10.11947/j.AGCS.2023.20220371

Abstract

Abstract: Airborne LiDAR point cloud filtering is the key step in point cloud processing, and its computational accuracy significantly affects the granularities of subsequent applications. However, it is difficult for the existing filtering algorithms to effectively distinguish object points from ground points in complex areas. Thus, a multi-feature clustering-based hierarchical filtering method is proposed in this paper. The proposed method first performs multi-feature point cloud clustering based on the geometric and physical information of point clouds; then, a ground-cluster identification method was used to accurately capture ground points in the area with breaklines; finally, the ground reference surface was constructed through the initial ground points, and the multi-scale hierarchical filtering was employed to further identify missed ground points. The new method was used to handle the point clouds in four different areas, and the filtering results were comprehensively compared with six state-of-the-art filtering algorithms. Results show that the new method has the lowest average total error, the best filtering performance and the highest stability.

Key words: point cloud filtering, airborne LiDAR, point cloud clustering, multi-feature

CLC Number:

P237

GUO Jiaojiao, CHEN Chuanfa, YAO Xi, LIU Yan, LIU Yating, LIU Panpan. A multi-feature clustering-based hierarchical filtering method for airborne LiDAR point clouds in complex landscapes[J]. Acta Geodaetica et Cartographica Sinica, 2023, 52(10): 1724-1737.

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