Extraction of individual tree crowns from airborne LiDAR data in human settlements

Abstract

Extraction of individual tree crowns is meaningful for many applications. In this paper, a new method is proposed to extract individual trees from airborne LiDAR point clouds in human settlements. In the process of extraction, an improved slope-based filter is employed to separate the non-ground measurements from the ground measurements, the surface growing algorithm is utilized to segment the point clouds into segments, multiple echo information is used to distinguish the tree points from other types of non-ground measurements, and the spoke wheel algorithm is employed to get the accurate edges of each tree at last. Two datasets are employed to test the above method. Experiments show that our approach is capable of extracting more than 85% trees from the point clouds with accuracy higher than 95%, which suggests the promising applications.