为了提高城区机载激光雷达点云数据分类算法的自动化程度和分类精度,提出一种基于渐进加密三角网和双层支持向量机的点云分类算法。采用渐进加密三角网滤波提取地面点,在地面点的基础上对地物点进行归一化处理。对点云特征有效性进行评估,选取特征向量并用最邻近支持向量机(NN-SVM)对地物点进行分类,实现对城区点云数据的多元分类。利用城区点云数据验证该算法,通过分析分类精度对分类效果进行评价。结果表明,该算法有效提高了点云数据分类精度,实现了对城区点云数据的有效分类。

Herein, to improve the automation and accuracy of the airborne LiDAR point cloud data classification algorithm, a classification algorithm for point clouds based on improved progressive triangulated irregular network densification (IPTD) and a double-layer support vector machine (SVM) was proposed; the classification effect of the algorithm on urban point cloud data was tested as follows. The IPTD filter method was used to extract ground points, and ground points were normalized based on ground points. Then, the effectiveness of point cloud features was evaluated to select eigenvectors, and nearest-neighbor SVM (NN-SVM) was used to classify the ground feature points, realizing the multiple classification of the urban point cloud data. Furthermore, the classification algorithm was verified using point cloud data from urban regions, and the classification effect was evaluated by analyzing the classification accuracy. The experimental results show that this algorithm can effectively improve the classification accuracy and classify point cloud data in urban areas.