Tree Height Extraction In Sparse Scenes Based On Uav Remote Sensing

In recent years, the method of obtaining ground object properties by processing and analyzing a 3D point cloud is prevalent. In this paper, a tree height estimation algorithm based on a 3D point cloud is proposed. This algorithm can calculate the height of trees in sparse scenes in batches, and it is also efficient. The key of the algorithm is to obtain the accurate point cloud cluster of the monomer tree. We achieve this by using a refined Density-based spatial clustering of applications with noise (DBSCAN). Then we estimate the ground height and treetop height, the height of the tree is their difference. In order to reduce the number of point clouds and maintain the overall structure, Voxel Grid filtering is also used. We apply this method to the 3D obtained point cloud, and good results prove our excellent work. Our code and point cloud data are available at https://github.com/yzfly/SimpleTreeHeight.