A Convolutional Neural Network-Based 3D Semantic Labeling Method for ALS Point Clouds

Abstract

3D semantic labeling is a fundamental task in airborne laser scanning (ALS) point clouds processing. The complexity of observed scenes and the irregularity of point distributions make this task quite challenging. Existing methods rely on a large number of features for the LiDAR points and the interaction of neighboring points, but cannot exploit the potential of them. In this paper, a convolutional neural network (CNN) based method that extracts the high-level representation of features is used. A point-based feature image-generation method is proposed that transforms the 3D neighborhood features of a point into a 2D image. First, for each point in the ALS data, the local geometric features, global geometric features and full-waveform features of its neighboring points within a window are extracted and transformed into an image. Then, the feature images are treated as the input of a CNN model for a 3D semantic labeling task. Finally, to allow performance comparisons with existing approaches, we evaluate our framework on the publicly available datasets provided by the International Society for Photogrammetry and Remote Sensing Working Groups II/4 (ISPRS WG II/4) benchmark tests on 3D labeling. The experiment results achieve 82.3% overall accuracy, which is the best among all considered methods.

Keywords

• deep convolutional neural network
• ALS point clouds
• semantic 3D labeling
• feature image