Automatic tree extraction method from laser scanning point cloud based on local interval maximum

Abstract

The invention discloses a universal fast and automatic method for extracting trees from laser scanning point clouds based on local interval maximum values. The method is directly based on three-dimensional laser point cloud data, and defines and calculates elevation intervals by dividing horizontal grids. The cumulative energy of the tree is obtained by using the non-local maximum suppression method to obtain the potential position of the tree, so as to perform automatic segmentation and extraction. This method makes full use of the salience of the trunk structure of trees to carry out grid statistical description, and overcomes the problems of difficult feature description and unstable feature calculation results caused by the different shapes and sizes of trunks and crowns (different tree species, different tree ages). At the same time, this method has nothing to do with the density and is not sensitive to the partial loss of tree trunks. It overcomes the problem of difficult extraction of long-distance trees due to low density or occlusion loss. This method does not depend on the position of the scanning device and can adapt to complex scanning. surroundings. This method does not need to set a priori fitting model, so it is not sensitive to noise, suitable for complex and dense forest environment, and can exert better stability in forestry surveys.

Description

technical field [0001] The invention relates to a method for automatically extracting trees from laser scanning point clouds based on local interval maximum values. Background technique [0002] Using laser scanning technology for forest surveys can safely and efficiently obtain complete 3D point cloud data within the scanned plot. The point cloud data reflects the complete morphological structure of vegetation, trees, terrain and other objects in the scanned plot, providing reliable data support for tree parameter investigation. Single tree extraction in 3D point cloud data is the basis and premise of tree parameter investigation and forest resource investigation, and the result directly determines the correctness of subsequent investigation and calculation. However, the amount of data in the point cloud forest scene is huge, and the visibility of the scene is seriously disturbed by the forest canopy, which is not conducive to direct observation as a whole. In addition, t...

AI Technical Summary

Problems solved by technology

[0005] At present, the existing automatic single tree extraction methods for 3D laser point clouds are mainly divided into two types: airborne point cloud extraction methods and ground point cloud extraction methods: the extraction method for airborne laser point cloud data mainly considers The extraction of tree crowns is due to the characteristics of airborne point cloud overlooking scanning, which makes tree information more manifested in the tree crown structure. The commonly used methods are based on the local neighborhood principal component analysis (PCA) method, based on the method of watershed, The method based on geometric segmentation rules, etc., has the disadvantage that it is difficult to overcome the situation that the top of the tree crown is blocked by the canopy, and it is difficult to ensure the complete extraction of the trunk by using the tree crown.

[0006] 1. The method of using the fixed-height cross-section fitting circle judgment method is easy to encounter difficulties when the data is partially occluded and missing. The forest vegetation is dense. When the data is acquired, the trees in the distance will cause part of the trunk to be missing due to the occlusion of the front object, resulting in the inability to Fitting a circle, eventually losing trees;

Images