Tree segmentation method based on laser radar point cloud and single tree extraction method based on laser radar point cloud

Abstract

The present invention relates to a tree segmentation method based on a laser radar point cloud and a single tree extraction method based on the laser radar point cloud. A tree segmentation process is characterized by 1) separating the point clouds of a tree from an original laser radar point cloud; 2) projecting the three dimensional original laser radar point cloud to a two dimensional plane, and utilizing an image segmentation algorithm to segment the point clouds of the tree initially; 3) transferring a two dimensional initial segmentation result to a three dimensional grid environment to determine the root grids and the branch grids of each segmentation block, and utilizing a 3D grid fusion algorithm to fuse the segmented point clouds. According to the present invention, the image segmentation algorithm is utilized to obtain the initial segmentation result, then the over-segmentation is fused by the 3D grid fusion algorithm, the single tree is extracted by utilizing a three-dimensional model of the single tree, and the height, the size and the crown area of each tree are obtained, so that the over-segmentation problem in the conventional image segmentation algorithm is corrected, and the accurate single tree extraction results are provided. Moreover, the tree segmentation method based on the laser radar point cloud and the single tree extraction method based on the laser radar point cloud are not limited by the point cloud density, and the manual intervention is reduced greatly.

Description

technical field [0001] The invention relates to a tree segmentation method and a single tree extraction method based on a laser radar point cloud, and belongs to the technical field of laser radar point cloud data processing. Background technique [0002] Airborne lidar data is generally used for 3D model reconstruction of urban scenes and natural environments. However, current 3D reconstruction methods based on lidar data require heavy manual intervention. The model generated by the automatic algorithm will produce many errors, which also need to be corrected manually. [0003] In the process of 3D tree model reconstruction based on lidar data, one of the most difficult parts is how to accurately identify and extract individual trees in the lidar point cloud for subsequent tree modeling. The accurate tree model is not only related to highly restored urban and natural scenes, but also has vital significance for the application of forestry, agriculture and other industries....

AI Technical Summary

Problems solved by technology

[0004] However, the current traditional methods are difficult to meet the needs of accurately identifying a single tree, although these methods can more accurately distinguish trees and other ground objects such as buildings

However, due to the influence of point cloud density, these methods cannot well segment the point cloud of a single tree from the point cloud of a group of trees.

In particular, in the case of dense canopy and overlapping leaves, the traditional method will cause multiple trees to be considered as one tree, resulting in inaccurate calculation results of the final tree parameters

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