A Local Topological Information Aware Based Deep Learning Method for Ground Filtering from Airborne Lidar Data

Abstract

As a foundational preprocessing step for a lot of downstream tasks, ground filtering from airborne LiDAR data is designed to separate the ground points and preserve the off-ground points with complete shape information. However, because of the undulating terrain, it is still a challenge work to filter the ground under complex mountain regions. In this paper, we provide a deep learning based model to improve the ground filtering performance in abrupt slope using airborne LiDAR point clouds. Specifically, we first design a local topological information mining module to extract the local features. Then a modified graph convolutional networks (GCNs) is developed to fusion the local features and global features. Compared with most existing methods, our model not only enjoys the parameter-free advantage, which means it can be applied easily in various areas, but also obtains better ground filtering performance and can preserve more complete information contained in off-ground points. Experiments was implemented on seven forest areas. The proposed method obtains promising ground filtering results with mean total error of 6.46% and the mean kappa coefficient of 86.01%.