Combining geometrical and intensity information to recognize vehicles from super-high density UAV-LiDAR point clouds

The traditional airborne LiDAR point clouds vehicle recognition algorithms only utilize the contained geometric information and are not sufficient to recognize vehicles accurately, especially in complex urban scenes. And their applicability in super-high density UAV-LiDAR point clouds is unknown. Therefore, a 3-D algorithm combining geometric and intensity information from super-high density UAV-LiDAR point clouds is presented. The proposed algorithm firstly converts the original point clouds into a 3D multivalued image which fuses intensity, elevation and density information simultaneously. Thereafter, the potential vehicle voxels are extracted according to the intensity, elevation and density consistency of vehicles. Subsequently, individual vehicles are recognized using the potential vehicle voxel's spatially connected set with vehicle size constraint. Finally, the quantified attribute information of each individual vehicle, containing the spatial location, type, and size, is determined. The results for recognized vehicles are evaluated using UAV-LiDAR data with different densities and demonstrate an average quality (Kappa coefficient) of 96.58% (96.04%) without being significantly affected by occlusion and the very close vehicle arrangement.