Bare-earth identification selects points from a LiDAR point cloud so that they can be interpolated to form a representation of the ground surface from which structures, vegetation, and other cover have been removed. We triangulate the point cloud and segment the triangles into flat and steep triangles using a Discriminative Random Field (DRF) that uses a data-dependent label smoothness term.Regions are classified into ground and non-ground based on steepness in the regions and ground points are selected as points on ground triangles. Various post-processing steps are used to further identify flat regions as rooftops and treetops, and eliminate isolated features that affect the surface interpolation.The performance of our algorithm is evaluated in its effectiveness at labeling ground points and, more importantly, at determining the extracted bare-earth surface. Extensive comparison shows the effectiveness of the strategy at selecting ground points leading to good fit in the triangulated mesh derived from the ground points.
['Wei-Lwun Lu', 'James J. Little', 'Alla Sheffer', 'Hongbo Fu']
Deforestation: Extracting 3D Bare-Earth Surface from Airborne LiDAR Data