An Individual Tree Segmentation Method From Mobile Mapping Point Clouds Based on Improved 3-D Morphological Analysis

Abstract

Street tree extraction based on the 3D mobile mapping point cloud plays an important role in building smart cities and creating highly accurate urban street maps. Existing methods are often over- or under-segmented when segmenting overlapping street tree canopies and extracting geometrically complex trees. To address this problem, we propose a method based on improved 3D morphological analysis for extracting street trees from mobile laser scanner (MLS) point clouds. First, the 3D semantic point cloud segmentation framework based on Deep Learning is used for pre-classification of the original point cloud to obtain the vegetation point cloud in the scene. Considering the influence of terrain unevenness, the vegetation point cloud is de-terraformed and slice point cloud containing tree trunks is obtained through spatial filtering on height. On this basis, a voxel-based region growing method constrained with the changing rate of convex area is used to locate the stree trees. Then we propose an progressive tree crown segmentation method, which first completed the preliminary individual segmentation of the tree crown point cloud based on the voxel-based region growth constrained by the minimum increment rule, and then optimizes the crown edges by “valley” structure-based clustering. In this paper, the proposed method is validated and the accuracy is evaluated using three sets of MLS Datasets collected from different scenarios. The experimental results show that the method can effectively identify and localize street trees with different geometries and has a good segmentation effect for street trees with large adhesion between canopies. The accuracy and recall of tree localization are higher than 96.08% and 95.83%, respectively, and the average precision and recall of instance segmentation in three datasets are higher than 93.23% and 95.41%, respectively.