关键词: 激光雷达, 点云分类, Point-Net, KD树, PCA

Abstract: In order to improve the classification accuracy of airborne LiDAR data and avoid the time-consuming point cloud multi-feature extraction, the article extracts the relative elevation feature of the point cloud data based on the point cloud denoising, and proposes a network model based on the combination of PCA data dimension reduction and Point-Net. The acquired relative elevation features and original features are input into the network after dimensionality reduction, and the global features extracted by the Point-Net network model are used for point cloud classification, and the label after each point classification is returned. The classification results are visualized according to the coordinate information and label of the point cloud, and the 
classification of airborne LiDAR point cloud data is realized. Finally, the accuracy analysis of the classification results is carried out. The classification experiment shows that the point cloud classification results obtained by this method are better.

Key words: LiDAR, point cloud classification, Point-Net, k-d tree, PCA