Paper
3 June 2024 Influence of flattening point cloud on filtering accuracy
Bolin Chu, Shoujun Li, Jinyu Liu
Author Affiliations +
Abstract
Point cloud filtering is a very important link in Lidar data processing, that is, separating ground points and non-ground points in the point cloud, which lays a foundation for subsequent data processing. The filtering accuracy will directly affect the accuracy of the final DEM. Based on the "rotation invariance" of point cloud, this paper firstly uses Euler transformation, axis angle transformation and homogeneous transformation to flatten the target point cloud. Then the mainstream filtering methods such as cloth simulation filtering algorithm, statistical filtering method, bilateral filtering and slope filtering are used to filter the point clouds before and after flattening, and the filtering results are evaluated by ISRIS index. Finally, the corresponding DEM model is constructed through the filtering results, and the overall error of DEM constructed by each filtering algorithm is quantitatively analyzed through the evaluation indexes of DEM average error, median error and maximum error, so as to study the influence of point cloud flattening on point cloud filtering.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Bolin Chu, Shoujun Li, and Jinyu Liu "Influence of flattening point cloud on filtering accuracy", Proc. SPIE 13170, International Conference on Remote Sensing, Surveying, and Mapping (RSSM 2024), 131700O (3 June 2024); https://doi.org/10.1117/12.3032133
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KEYWORDS
Tunable filters

Point clouds

Digital filtering

Error analysis

Image filtering

Vegetation

Computer simulations

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