Integrative Clustering Filtering of Airborne LiDAR Data for Digital Terrain Model Generation
Abstract This paper presents an efficient method, based on integrative clustering filtering (ICF), for an accurate terrain assessment in complex areas. Firstly, the point cloud data is described as the Octree index structure, and then segmented based on planarity. Then a coarse spatial clustering process, based on dual distance is implemented. Secondly, a triangulated irregular network (TIN) is built based on the classic progressive densification method, and the rest of the valid point clouds are classified iteratively. Finally, the experimental results show that the ICF method proposed in this paper is capable of producing reliable DTMs in the discontinuous areas, and all of the above contribute to improving the reliability of the filtering result.
Key words :
airborne LiDAR
ground filtering
integrative clustering
digital terrain model
point cloud
Cite this article:
XU Ying,YUE Dongjie. Integrative Clustering Filtering of Airborne LiDAR Data for Digital Terrain Model Generation
[J]. jgg, 2017, 37(1): 93-96.
XU Ying,YUE Dongjie. Integrative Clustering Filtering of Airborne LiDAR Data for Digital Terrain Model Generation
[J]. jgg, 2017, 37(1): 93-96.
URL:
http://www.jgg09.com/EN/ OR http://www.jgg09.com/EN/Y2017/V37/I1/93
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