A Method of Airborne LiDAR Data Classification Based on Curved Surface Approximation

Abstract

Current airborne laser scanning system is able to acquire three dimensional geographic information of areas and objects on the ground quickly with the form of three dimensional discrete point clouds which densities is up to 40 points per square meter. The main problem is the classification and identification of these objects in the point cloud. In this paper, we use the curved surface approximation method to classify point clouds into terrain and off-terrain points. A dimensionality fuzzy clustering method is introduced to classify buildings and vegetations. The method of surface approximation makes a distinguishing surface from the scanned data themselves, and makes the surface approach the ground by iteration. The surface made by his method fits the shape of the ground better. This paper compares the distinguishing effects of the method with and without dimensionality reduction, and result showed that the method with the dimensionality reduction is effective.