Classification method of LiDAR point cloud based on threedimensional convolutional neural network

Abstract

Aiming at the classification problem of ground objects in complex scenes of airborne LiDAR data, this paper proposes an algorithm based on three-dimensional convolutional neural network (3D-CNN). The algorithm improves the pre-processing method of LiDAR point cloud and realize automatically classification of LiDAR point cloud in complex scenes. Based on the input of 3D-CNN is voxel grid, this paper selects multi-scales to construct voxel grids for each point in the point cloud, trains them with the network, and then combines the features of multi - scales through the fully connected layer. Finally, the network returns the category score of each point to complete the classification of LiDAR point cloud. The algorithm is verified by the Vaihingen dataset provided by ISPRS. The experimental results show that the proposed algorithm can achieve higher classification accuracy than other convolutional neural networks dealing with point clouds.