Comparison of heuristic and deep learning-based methods for ground classification from aerial point clouds

Abstract

The automatic definition of the ground from 3D point clouds has been a common process for the last two decades, with many different approaches and applications that can be found in a vast literature. This paper presents a comparison of three different methodological concepts for ground classification, in order to establish the advantages and drawbacks of each method. First, a heuristic method, based on previous knowledge of the geometry and context of the 3D data. Secondly, a Deep Convolutional Network based on SegNet that classifies 2D images generated from the 3D point cloud. Finally, the third method applies a Deep Learning classification based on PointNet, which takes 3D points directly as inputs. To validate each method and compare them, public and labelled point clouds frothe m the Actueel Hoogtebestand Nederland dataset are employed. Furthermore, the three methods are validated against the ISPRS 3D Semantic Labeling Contest benchmark. The results obtained show that the deep learning based approaches outperform the heuristic method, with F-scores above 96%. The best results were obtained using a shallower version of SegNet, with F-score above 97%.