A Method of Point Cloud Classification Using Multi-scale Dimensionality Features and Transductive Learning

Abstract

3D point cloud of environments relevant to recognition and monitoring problems in artificial intelligence often require classifying environment data into separated classes. This paper proposes a new classification approach for 3D lidar outdoor environment point clouds. The approach is based on morphological filter, multi-scale dimensionality features and support vector machine model. It can effectively separate the ground, trees and conventional constructions from downsampled point clouds. Compared with single traditional classification algorithm, such as supervoxel clustering algorithm and locally convex connected patches algorithm, the presented approach has good performance in objective and subjective assessments. The effectiveness of the proposed approach is verified based on a public data set collected from the International Society for Photogrammetry and Remote Sensing (ISPRS) and Stanford Modified Object Segmentation Database (MOSD).