一种运用PCA的深度学习激光点云分类方法

为了提高机载激光雷达数据的分类精度和避免耗时的点云多特征提取，本文在点云去噪的基础上，对点云数据进行相对高程的特征提取，提出一种基于PCA数据降维与Point-Net相结合而形成的网络模型，并将获取的相对高程特征和原始特征经过降维处理后输入到网络中。运用Point-Net网络模型提取的全局特征进行点云分类，返回每个点分类后的标签，并根据点云的坐标信息和标签进行分类结果可视化，实现机载激光雷达点云数据的分类，最后再对得到的分类结果进行精度分析。分类实验表明，此方法获得的点云分类结果较好。

A Deep Learning Laser Point Cloud Data Classification Method Using PCA

In order to improve the classification accuracy of airborne LiDAR data and avoid the time-consuming point cloud multi-feature extraction, the article extracts the relative elevation feature of the point cloud data based on the point cloud denoising, and proposes a network model based on the combination of PCA data dimension reduction and Point-Net. The acquired relative elevation features and original features are input into the network after dimensionality reduction, and the global features extracted by the Point-Net network model are used for point cloud classification, and the label after each point classification is returned. The classification results are visualized according to the coordinate information and label of the point cloud, and the  classification of airborne LiDAR point cloud data is realized. Finally, the accuracy analysis of the classification results is carried out. The classification experiment shows that the point cloud classification results obtained by this method are better.