GOGCN: an attentional network with geometry and orientation-awareness for airborne LiDAR point cloud classification

Abstract

The airborne LiDAR point cloud has its own characteristics, however, the classification method always fails to capture these characteristics. In this paper, a classification method named GOGCN was designed that adopts a U-Net network structure and uses a directionally constrained nearest neighbourhood search during down-sampling to generate the directionally aware feature. The point cloud geometric structure is obtained through geometry-aware information extraction, and then a graph attention convolution is utilised to learn the most representative features. A comparative experiment on GML(B) dataset and one engineering dataset demonstrated that GOGCN network have well performance and can be widely used in classification.