Terrestrial laser scanning point cloud data tree-shrub separation method research
For the current terrestrial laser scanning LiDAR point cloud data,it is difficult to accurately realize the separation of trees and shrubs point cloud in different environments. This paper proposes a combination of coarse and fine separation methods:firstly,the point cloud data are preprocessed;then combined with normal vectors and support vector machines for coarse separation;finally,the RANSAC algorithm is utilized to fit the columns,combined with the standard deviation of the number of point clouds in the grids to screen the arbor and adaptive DBSCAN based on the standard deviation of the number of cloud points to carry out the final fine separation. In this paper,eight sample plots with different growing environments were selected for testing,and the accuracy of the number of trees extracted was higher than 93％ in all cases. The experimental results show that the method can realize efficient and accurate separation of trees and shrubs from ground LiDAR point clouds in different environments,and has higher precision and efficiency and stronger universality than the existing traditional methods,which lays the foundation for the accurate extraction of the subsequent vegetation parameters and the number of forest tree strains,and has a wide range of application prospects.
万方数据
维普
