  Airborne light detection and ranging (LiDAR) plays an increasingly
significant role in urban planning, topographic mapping, environmental
monitoring, power line detection and other fields thanks to its capability to
quickly acquire large-scale and high-precision ground information. To achieve
point cloud classification, previous studies proposed point cloud deep learning
models that can directly process raw point clouds based on PointNet-like
architectures. And some recent works proposed graph convolution neural network
based on the inherent topology of point clouds. However, the above point cloud
deep learning models only pay attention to exploring local geometric
structures, yet ignore global contextual relationships among all points. In
this paper, we present a graph attention convolution neural network (GACNN)
that can be directly applied to the classification of unstructured 3D point
clouds obtained by airborne LiDAR. Specifically, we first introduce a graph
attention convolution module that incorporates global contextual information
and local structural features. Based on the proposed graph attention
convolution module, we further design an end-to-end encoder-decoder network,
named GACNN, to capture multiscale features of the point clouds and therefore
enable more accurate airborne point cloud classification. Experiments on the
ISPRS 3D labeling dataset show that the proposed model achieves a new
state-of-the-art performance in terms of average F1 score (71.5\%) and a
satisfying overall accuracy (83.2\%). Additionally, experiments further
conducted on the 2019 Data Fusion Contest Dataset by comparing with other
prevalent point cloud deep learning models demonstrate the favorable
generalization capability of the proposed model.
