EXTRACTION OF STREET TREES FROM MOBILE LASER SCANNING POINT CLOUDS BASED ON SUBDIVIDED DIMENSIONAL FEATURES

This paper proposes a method for automated extraction of street trees in a typical urban environment from 3D point cloud data acquired by the mobile laser scanning system. First, the algorithm utilizes the voxel-based method to remove the ground points from the scene. Second, the Euclidean distance clustering is adopted to cluster points into individual objects. The eigenvalues of neighborhood covariance matrix and the corresponding normalized centroid distance are computed for each point to obtain the subdivided dimensional features. Finally, the statistical component features and horizontal information are calculated for object detection. The experiment results show the feasibility of the proposed algorithm.