Comparison of the performances of ground filtering algorithms and DTM generation from a UAV-based point cloud

Abstract

Ground filtering algorithms mainly focus on filtering LiDAR (Light Detection and Ranging) point clouds owing to their intrinsic characteristics to classify ground and non-ground points. However, the acquisition and processing of LiDAR data is still costly. Compared to LiDAR technology, UAVs (Unmanned Aerial Vehicle) are cheap and easy to use. In this study, the performances of five widely used ground filtering algorithms (Progressive Morphological 1D/2D, Maximum Local Slope, Elevation Threshold with Expand Window, and Adaptive TIN) were investigated by conducting qualitative and quantitative evaluations on UAV-based point clouds. Evaluation results indicated that the Adaptive TIN algorithm presented the best performance. The result of the Adaptive TIN algorithm was interpolated by using a MATLAB script to generate the DTM (Digital Terrain Model). Field measurements indicated that using UAV-based point clouds may be a reasonable alternative for LiDAR data, depending on the characteristics of the study area.