DEM Extraction from LIDAR Data by Morphological Gradient

Digital elevation model (DEM) is essential for most geographic information system (GIS) applications and topographic analysis. The technology of airborne light detection and ranging (LIDAR) provides a powerful support for generating high-resolution DEM. DEM extraction from LIDAR data includes filtering and DEM reconstruction. Recent years witnessed many kinds of filters designed and developed. However, filtering is still a practicably difficult, especially for scene complex area. In this paper, a new method of DEM extraction from LIDAR data based on morphological gradient is proposed. Firstly, point clouds are divided by an index mesh. Then, the morphological gradient of each point is calculated using the method suitable for filtering. And, objects are removed gradually by choosing some points based on gradients to carry on an improved opening operation iteratively. Finally, DEM is three-dimensional reconstructed through interpolating by inversing distance to a power. This method is tested with 15 sample data sets which are released by ISPRS and compared with other filtering methods qualitatively and quantitatively. The experimental results show that this method has high robustness in all kinds of complex scenes, which can reduce the nonessential computation as well as the possibility that all types of error happen. So the method has good adaptability and practicability.