Comparison and analysis of ground seed detectors and interpolation methods in airborne LiDAR filtering

Abstract

Ground seed detectors and interpolation methods are fundamental components of filtering algorithms. However, the performance of different detectors and interpolation methods typically varies, and few studies have been conducted on the adaptability of different detectors and interpolation methods to different terrains. Therefore, we compare three ground seed detectors (cylindrical neighborhood (CN), fixed grid (FG), and moving window (MW)) and three interpolation methods (triangulated irregular network (TIN), thin plate spline (TPS), and inverse distance weighting (IDW)). In addition, nine filters are constructed by combining the three ground seed detectors and the three interpolation methods to evaluate their comprehensive influences. To assess the performance of these detectors, interpolation methods, and filters, fifteen ISPRS-supplied light detection and ranging (LiDAR) benchmark datasets are utilized in the experiment. The findings indicate that the CN-TPS filter, which combines the CN detector and TPS method, achieves superior performance across mean P_g (99.16 % – correctly classified ground points divided by all extracted ground points), RMSE (0.44 m – root mean squared error), and total error (3.78 %). Moreover, the filtering methods are mainly affected by the performance of the ground seed detector and are less affected by the selected interpolation method. These results can be used to provide a valuable reference for designing an optimal LiDAR filtering algorithm for varied terrain types and applications.