Semantic Segmentation of Coastal Zone on Airborne Lidar Bathymetry Point Clouds

Large-scale semantic segmentation point cloud is an ongoing research topic for on-land environments. However, there is a rare deep learning research study for the sub-surface environment. Although, PointNet and its successor PointNet++ have become the cornerstone of point cloud segmentation. However, these techniques handle a relatively small number of points. This poses a natural difficulty in a large spatial scene with millions of possible points. In particular, for shallow water of coastal zone, the small number of points where the seabed and water surface meet, close points may belong to different classes. In our work, we present the semantic segmentation on a large-scale airborne Lidar bathymetry (ALB) point cloud containing millions of sample points into two classes of water surface and seabed with the voxel sampling pre-processing (VSP) approach. The proposed approach will allow us to capture the complicated outdoor natural scene components of water surface and seabed more accurately and more realistic through nonuniform voxelization in the mixture of dense and sparse points of the ALB point cloud. The performance of validation results show improvement in a per-point accuracy of 72.45% compared with other state-of-the-art deep learning-based methods.