Terrain Complexity and Maximal Poisson-Disk Sampling-Based Digital Elevation Model Simplification

Abstract

With the rapid development of lidar, the accuracy and density of the Digital Elevation Model (DEM) point clouds have been continuously improved. However, in some applications, dense point cloud has no practical meaning. How to effectively sample from the dense points and maximize the preservation of terrain features is extremely important. This paper will propose a DEM sampling algorithm that utilizes terrain complexity and maximal Poisson-disk sampling to extract key feature points for adaptive DEM sampling. The algorithm estimates terrain complexity based on local terrain variation and prioritizes points with high complexity for sampling. The sampling radius is inversely proportional to terrain complexity, while ensuring that points within the radius of accepted samples are not considered new samples. This way makes more points of concern in the rugged regions. The results show that the proposed algorithm has higher global accuracy than the classic six sampling methods.