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 global-local 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. The global attention module examines spatial relationships among all points, while the local attention module can dynamically learn convolution weights with regard to the spatial position of the local neighboring points and reweight the convolution weights by inspecting the density of each local region. 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.

机载光检测和测距（LiDAR）能够快速获取大规模和高精度的地面信息，因此在城市规划，地形图，环境监测，电力线检测和其他领域中发挥着越来越重要的作用。为了实现点云分类，先前的研究提出了点云深度学习模型，该模型可以直接基于类似PointNet的架构处理原始点云。最近的一些工作提出了基于点云固有拓扑的图卷积神经网络。但是，以上的点云深度学习模型仅关注探索局部几何结构，而忽略所有点之间的全局上下文关系。在本文中，我们提出了一种全球局部图注意力卷积神经网络（GACNN），该网络可直接应用于机载LiDAR获得的非结构化3D点云的分类。具体来说，我们首先介绍一个图注意力卷积模块，该模块结合了全局上下文信息和局部结构特征。全局注意力模块检查所有点之间的空间关系，而局部注意力模块可以动态地学习关于局部相邻点的空间位置的卷积权重，并通过检查每个局部区域的密度来对卷积权重进行加权。在提出的图注意力卷积模块的基础上，我们进一步设计了端到端的编解码器网络，称为GACNN，捕获点云的多尺度特征，从而实现更准确的空中点云分类。在ISPRS 3D标记数据集上进行的实验表明，该模型在平均F1得分（71.5％）和令人满意的总体准确性（83.2％）方面达到了新的先进水平。此外，通过与其他流行点云深度学习模型进行比较，对2019年数据融合竞赛数据集进一步进行的实验证明了该模型具有良好的泛化能力。