CANOPY LIDAR POINT CLOUD DATA K-MEANS CLUSTERING WATERSHED SEGMENTATION METHOD

Abstract

Airborne laser LiDAR has widely applied in the accurate extraction of single tree canopy for inventory of precision forestry. Due to the over-segmentation phenomenon occurring in the traditional watershed single-wood segmentation, this paper presents a method, called K – means clustering watershed for single tree segmentation. This method consists of four aspects: The first step is to filter the point cloud to eliminate the interference factors such as ground elevation and other factors that interfere with the LiDAR point cloud segmentation; The second step is to optimize the generation of CHM, generate a CMM based on CHM variable window detection, and obtain the treetop position to provide the pixel center position for subsequent K – means cluster segmentation; The third step is to use the K – means clustering algorithm to perform initial cluster segmentation to extract the target pixels of interest. At this time, the local maximum value detected by the variable window in the second step is used as the center pixel of the cluster; In the fourth step, an improved watershed algorithm based on the similarity of 4 neighborhoods is proposed. The improved watershed algorithm is applied to the K – means initial clustering image to segment the target area, and the over-segmentation results are subsequently processed, and the over-segmentation blocks are combined according to certain criteria. Identify the contour of single canopy from the CHM images of the experimental forest data. The experimental results show that the proposed algorithm can effectively solve the over-segmentation problem happening the traditional watershed algorithm. The accuracy of F, R and P parameters can be improved by 7.1%, 11% and 9.8%.