Performance Comparison of Filtering Algorithms for High-Density Airborne LiDAR Point Clouds over Complex LandScapes

Airborne light detection and ranging (LiDAR) technology has become the mainstream data source in geosciences environmental sciences. Point cloud filtering is a prerequisite for almost all LiDAR-based applications. However, it challenging to select suitable algorithm handling high-density point clouds over complex landscapes. Therefore, determine an appropriate filter on specific environment, this paper comparatively assessed performance of five representative algorithms six study sites with different terrain characteristics, where three plots are located urban areas forest areas. The methods include simple morphological (SMRF), multiresolution hierarchical (MHF), slope-based (SBF), progressive TIN densification (PTD) segmentation-based (SegBF). Results demonstrate that SMRF performs best areas, compared MHF, SBF, PTD SegBF, total error reduced by 1.38%, 48.21%, 48.25% 31.03%, respectively. MHF outperforms others SMRF, 1.98%, 35.87%, 45.11% 9.42%, Moreover, both keep good balance between type I II errors, which makes produced DEMs much similar references. Overall, recommended respectively, averagely slightly better than respect kappa coefficient.