Filtering Airborne Lidar Data by Modified White Top-Hat Transform with Directional Edge Constraints

A novel algorithm that employs modified white top-hat (MWTH) transform with directional edge constraints is proposed in this study to automatically extract ground points from airborne light detection and ranging (lidar) data. MWTH transform can effectively distinguish above-ground objects that are smaller than the window size and higher than the height difference threshold. Directional edge constraints significantly decrease omission errors from protruding ground features. Incorporating MWTH transform and directional edge constraints enables the simultaneous consideration of the size, height, and edge characteristics of lidar data for judging above-ground objects. Experimental results verify that the proposed algorithm exhibits promising performance and high accuracy in various complicated landscapes, even in areas with dramatic changes in elevation. The proposed algorithm has minimal omission and commission error oscillation for different test sites, thereby demonstrating its stability and reliability in a wide range of applications.