DGCNN Network Architecture With Densely Connected Point Pairs in Multiscale Local Regions for ALS Point Cloud Classification

Abstract

Deep learning in 3-D classification tasks focuses on the designs of comprehensive local aggregation operators. Airborne laser scanning (ALS) point clouds have their own characteristics: 1) the object overlaps in the vertical direction; 2) the spatial density is uneven; and 3) the objects present large-scale variations between different categories. However, the dynamic graph convolutional neural network (DGCNN) ignores the inherent properties of ALS point clouds. This study modifies the DGCNN network and proposes a new neural network module called Webconv, which densely connects point pairs in multiscale local regions to learn contextual information. One modified cross entropy loss function with variable weight is proposed to solve the problem of uneven category distributions in ALS points. Because preprocessing such as block partition ignores the context information of the whole region, the conditional random field is used to refine the point cloud. Our approach achieves state-of-the-art performance on the dataset of the 2019 IEEE GRSS Data Fusion Contest 3-D Point Cloud Classification Challenge and can be widely used for ALS point classification.