Airborne LiDAR point cloud classification has been a long-standing problem in photogrammetry and remote sensing. Early efforts either combine hand-crafted feature engineering with machine learning-based classification models or leverage the power of conventional convolutional neural networks (CNNs) on projected feature images. Recent proposed deep learning-based methods tend to develop new convolution operators which can be directly applied on raw point clouds for representative point feature learning. Although these methods have achieved satisfying performance for the classification of airborne LiDAR point clouds, they cannot adequately recognize fine-grained local structures due to the uneven density distribution of 3D point clouds. In this paper, to address this challenging issue, we introduce a density-aware convolution module which uses the point-wise density to reweight the learnable weights of convolution kernels. The proposed convolution module can approximate continuous convolution on unevenly distributed 3D point sets. Based on this convolution module, we further develop a multi-scale CNN model with downsampling and upsampling blocks to perform per-point semantic labeling. In addition, to regularize the global semantic context, we implement a context encoding module to predict a global context encoding and formulated a context encoding regularizer to enforce the predicted context encoding to be aligned with the ground truth one. The overall network can be trained in an end-to-end fashion and directly produces the desired classification results in one network forward pass. Experiments on the ISPRS 3D Labeling Dataset and 2019 Data Fusion Contest Dataset demonstrate the effectiveness and superiority of the proposed method for airborne LiDAR point cloud classification.

机载LiDAR点云分类一直是摄影测量和遥感领域的长期问题。早期的努力要么将手工特征工程与基于机器学习的分类模型相结合，要么在投影特征图像上利用传统卷积神经网络（CNN）的功能。最近提出的基于深度学习的方法趋向于开发新的卷积算子，该算子可以直接应用于原始点云以进行代表性点特征学习。尽管这些方法在机载LiDAR点云分类方面取得了令人满意的性能，但是由于3D点云的密度分布不​​均匀，它们无法充分识别细粒度的局部结构。在本文中，为了解决这一具有挑战性的问题，我们介绍了一种密度感知卷积模块，该模块使用点密度来对卷积核的可学习权重进行加权。所提出的卷积模块可以近似地对不均匀分布的3D点集进行连续卷积。基于此卷积模块，我们进一步开发了具有下采样和上采样块的多尺度CNN模型，以执行每点语义标记。此外，为了规范化全局语义上下文，我们实现了一个上下文编码模块来预测全局上下文编码，并制定了一个上下文编码正则化器以强制将所预测的上下文编码与基本事实对齐。可以以端到端的方式训练整个网络，并在一个网络前向通过中直接产生所需的分类结果。