Abstract
Airborne light detection and ranging (LiDAR) technology has become the mainstream data source in geosciences and environmental sciences. Point cloud filtering is a prerequisite for almost all LiDAR-based applications. However, it is challenging to select a suitable filtering algorithm for handling high-density point clouds over complex landscapes. Therefore, to determine an appropriate filter on a specific environment, this paper comparatively assessed the performance of five representative filtering algorithms on six study sites with different terrain characteristics, where three plots are located in urban areas and three in forest areas. The representative filtering methods include simple morphological filter (SMRF), multiresolution hierarchical filter (MHF), slope-based filter (SBF), progressive TIN densification (PTD) and segmentation-based filter (SegBF). Results demonstrate that SMRF performs the best in urban areas, and compared to MHF, SBF, PTD and SegBF, the total error of SMRF is reduced by 1.38%, 48.21%, 48.25% and 31.03%, respectively. MHF outperforms the others in forest areas, and compared to SMRF, SBF, PTD and SegBF, the total error of MHF is reduced by 1.98%, 35.87%, 45.11% and 9.42%, respectively. Moreover, both SMRF and MHF keep a good balance between type I and II errors, which makes the produced DEMs much similar to the references. Overall, SMRF and MHF are recommended for urban and forest areas, respectively, and MHF averagely performs slightly better than SMRF on all areas with respect to kappa coefficient.
Highlights
Airborne light detection and ranging (LiDAR) technology has been widely accepted as a powerful tool for generating high-quality digital elevation models (DEMs) [1,2]
1, while in forest areas, the kappa coefficient varies from on plot were empirically chosen in terms of the minimum total error on each plot
Numerous research works have been conducted to compare the performance of the state-of-the-art filtering algorithms
Summary
Airborne light detection and ranging (LiDAR) technology has been widely accepted as a powerful tool for generating high-quality digital elevation models (DEMs) [1,2].Compared to the classical surveying and mapping methods, LiDAR shows many promising merits, such as the high efficiency for collecting high-density and large-scale point clouds, which is conducive to the detailed representation of topography [3,4], and forest canopy penetration ability, which is valuable for forest inventory and management [5,6]. Airborne light detection and ranging (LiDAR) technology has been widely accepted as a powerful tool for generating high-quality digital elevation models (DEMs) [1,2]. The raw LiDAR point clouds include the bare earth (BE) points, and the object (OBJ) points, which make point cloud filtering indispensable for almost all LiDAR-based applications, such as landslide detection [7], erosion and deposition quantification [8], channel-bed morphology recognition [9] and individual tree position extraction [10]. Each method has its strengths and weaknesses for handling different landscapes, and the performances of these filtering algorithms vary from one scene to another. Performance comparison between the filtering algorithms is highly beneficial for the choice of an appropriate filter, especially for inexperienced users
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