Abstract
Abstract. Digital Elevation Model (DEM) generation is one of the leading application areas in geomatics. Since a DEM represents the bare earth surface, the very first step of generating a DEM is to separate the ground and non-ground points, which is called ground filtering. Once the point cloud is filtered, the ground points are interpolated to generate the DEM. LiDAR (Light Detection and Ranging) point clouds have been used in many applications thanks to their success in representing the objects they belong to. Hence, in the literature, various ground filtering algorithms have been reported to filter the LiDAR data. Since the LiDAR data acquisition is still a costly process, using point clouds generated from the UAV images to produce DEMs is a reasonable alternative. In this study, point clouds with three different densities were generated from the aerial photos taken from a UAV (Unmanned Aerial Vehicle) to examine the effect of point density on filtering performance. The point clouds were then filtered by means of five different ground filtering algorithms as Progressive Morphological 1D (PM1D), Progressive Morphological 2D (PM2D), Maximum Local Slope (MLS), Elevation Threshold with Expand Window (ETEW) and Adaptive TIN (ATIN). The filtering performance of each algorithm was investigated qualitatively and quantitatively. The results indicated that the ATIN and PM2D algorithms showed the best overall ground filtering performances. The MLS and ETEW algorithms were found as the least successful ones. It was concluded that the point clouds generated from the UAVs can be a good alternative for LiDAR data.
Highlights
It should be noted that the circles in this figure indicate some of the commission errors whereas the rectangles show some of the omission errors
The performances of ground filtering algorithms, which were mainly developed to filter LiDAR point clouds, were investigated by using the point cloud extracted from the aerial images taken from a UAV
The generated raw point cloud was densified to generate the medium-density and high-density point clouds to investigate the effects of point density on filtering performance
Summary
LiDAR sensors generate multiple returns (first return, last return etc.). This is a huge advantage when separating the ground and non-ground points. Ground filtering algorithms were mainly developed to filter LiDAR point clouds. As an alternative for LiDAR point clouds, it is possible to generate very dense point clouds by using overlapped aerial photos taken from UAVs. The aim of this study is to investigate the performances of ground filtering algorithms, mainly developed for LiDAR point clouds, for UAV-based point clouds. The density of a point cloud effects the filtering result. In this study, denser point clouds were generated to investigate the effect of point density on filtering performance
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