Research on Semantic Segmentation of Airborne LiDAR Point Cloud Based on Spatial Position Attention Mechanism

Abstract

Airborne lidar detection technology can quickly and efficiently observe the earth, actively, real-time and directly obtain the three-dimensional information of a large range of ground objects, and generate a large range of LiDAR point clouds. However, complex categories and uneven heights of ground objects lead to difficulty on the semantic segmentation. To overcome this difficulty, this paper proposes an new semantic segmentation network for point clouds based on the spatial position attention mechanism and deep learning network. This network can directly process original point cloud without converting the original point cloud data into groups of 2D feature images or 3D voxel grids, avoiding information loss. The semantic segmentation model uses the encoder and decoder to extract multiple scale features, and uses the multiple layer perceptron to realize high precision segmentation. By the spatial position attention mechanism, this network can strengthen or weaken weights of the convolution kernel to automatically adapt to spatial structures of point cloud objects. In this paper, the ISPRS dataset provided by the International Society for Photogrammetry and Remote Sensing is used to carry out experiments. The experiment results indicate the proposed network can effectively identify various ground objects and has higher semantic segmentation accuracy than other popular methods.