Classification of terrestrial laser scanning data based on relative projection density

Abstract

Point cloud classification is a critical step in ground 3D scene analysis. The density of large-scale terrestrial laser scanning data will decrease rapidly with the increase of distance, which will affect features extraction. Focusing on this problem, we propose a grid features based on the relative projection density for point cloud classification in this paper. Geometric features are constructed based on the neighborhood covariance eigenvalue of each point. In grid feature extraction, the relative projection density is used to replace the number of projection points as grid density feature directly. In an outdoor scene obtained by the panoramic scanning of Reigl-VZ400 scanner, the grid features based on the relative projection density and the traditional projection density features are compared and analyzed. Based on the Random Forest for classification, the result shows that the relative projection density features with overall accuracy of 96.51%. Compared with traditional projection density feature, it is more accurate in classification, and it also performs relatively well in the extraction of cars, pedestrian and pole.