A Classification-oriented Method of Feature Image Generation for Vehicle-borne Laser Scanning Point Clouds

An efficient method of feature image generation of point clouds to automatically classify dense point clouds into different categories is proposed, such as terrain points, building points. The method first uses planar projection to sort points into different grids, then calculates the weights and feature values of grids according to the distribution of laser scanning points, and finally generates the feature image of point clouds. Thus, the proposed method adopts contour extraction and tracing means to extract the boundaries and point clouds of man-made objects (e.g. buildings and trees) in 3D based on the image generated. Experiments show that the proposed method provides a promising solution for classifying and extracting man-made objects from vehicle-borne laser scanning point clouds