Ground object classification based on height-aware multi-scale graph convolution network

The point cloud acquired by airborne LiDAR has the complex characteristics of uneven distribution of categories and large differences in sample elevation. Existing methods are difficult to fully identify fine-grained local structures. This paper proposes an end-to-end network for airborne LiDAR point cloud classification after employing stacked multi-layer edge convolution operators to simultaneously extract local and global information. It also introduces elevation attention weights as a supplement to feature extraction. First, the original point cloud is divided into sub-blocks and sampled to a fixed number of points. Then the multi-scale edge convolution operator is used to extract multi-scale local-global features which are merged thereafter, at the same time, the height-aware module is used to generate attention weights and applied to the feature extraction network. Finally, the improved focus loss function is used to further solve the problem of uneven distribution of categories and complete the classification. The standard test data set provided by the International Society for Photogrammetry and Remote Sensing (ISPRS) was used to verify the proposed method. Overall, 85.9% of the classifications were accurate. The single-category classification accuracy, especially the roof, was increased by 4.6% than the best result published in the ISPRS competition. The research results have reference significance for practical applications and network design optimization. © 2023 Beijing University of Aeronautics and Astronautics (BUAA). All rights reserved.