基于区域结构特征的城区LiDAR数据快速分类

机载激光雷达能够及时准确地获取大量具有精确三维位置信息的三维点云数据，在数字城市、森林防火、智能交通等领域有着广泛的应用。城市中心区域的三维点云数据往往会因为高大树木或植被的遮挡，造成建筑物等人造目标识别特别困难。本文通过直接的二次多项式拟合方式提取高大树木等植被与建筑物目标典型局部区域的区域信息，构建区域目标敏感的结构特征，进而，通过模糊逻辑即可完成三维点云数据的建筑物目标敏感的分类任务。实验结果表明，该方法能够快速有效地实现较大尺度范围内LiDAR点云数据的分类，具有较好的应用前景和推广能力。

Fast Classification of Urban LiDAR Data Based on Regional Structure Features

Airborne LiDAR can timely and accurately obtain a large number of 3D point cloud data with accurate 3D position information. It has a wide range of applications in digital cities, forest fire prevention, intelligent transportation, etc. Among them, the 3D point cloud data of urban areas, especially urban central areas with dense plants, is often occluded by tall trees or vegetation which make it difficult to recognize data belongs to man-made objects such as buildings. This paper uses a direct quadratic polynomial fitting method to extract regional information of typical local areas of vegetation and buildings, such as tall trees, and constructs sensitive structural features of regional targets. Furthermore, through fuzzy logic, the task 3D point cloud data classification especially designed to distinguish building targets and disturbances from trees can be completed. The experimental results show that this method can quickly and effectively realize the classification of LiDAR point cloud data, and the proposed method has the good application prospect and robustness for promotion.