Serifoglu Ç., Gungor O., Yilmaz V.

23rd Congress of the International-Society-for-Photogrammetry-and-Remote-Sensing (ISPRS), Prague, Czech Republic, 12 - 19 July 2016, vol.41, pp.245-251 identifier identifier

  • Publication Type: Conference Paper / Full Text
  • Volume: 41
  • Doi Number: 10.5194/isprsarchives-xli-b1-245-2016
  • City: Prague
  • Country: Czech Republic
  • Page Numbers: pp.245-251
  • Keywords: Point Cloud, Ground Filtering, Unmanned Aerial Vehicle, Aerial Photo, Digital Elevation Model, Adaptive TIN, AIRBORNE LIDAR DATA, MORPHOLOGICAL FILTER, CLASSIFICATION, AGREEMENT
  • Karadeniz Technical University Affiliated: Yes


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.