机载激光 LiDAR点云数据滤波和分类设计

Design of Airborne Laser LiDAR Point Cloud Data Filtering and Classification

为提高机载激光LiDAR点云数据处理的精确性与效率,本文详细阐述了噪声去除、规则格网化处理、高程突变点提取、二面角滤波处理及基于区域分割与决策树的分类设计。首先,本文有效去除了点云数据中的噪声,并通过规则格网化实现有序组织。其次,利用坡度阈值法成功提取高程突变点,并通过二面角滤波进一步提升了分类精度。最后,本文设计了基于区域分割与决策树的分类方法,实现了点云数据的准确分类。研究结果表明,本文方法能够高效去除噪声、提取关键特征,并实现高精度的点云数据分类,为机载激光LiDAR技术的应用提供了有力支撑。

In order to improve the accuracy and efficiency of airborne laser LiDAR point cloud data processing,this article elaborates in detail on noise removal,regular grid processing,elevation mutation point extraction,dihedral angle filtering processing,and classification design based on region segmentation and decision tree.Firstly,this article effectively removes noise from point cloud data and achieves orderly organization through rule-based gridding.Subsequently,the slope threshold method was successfully used to extract elevation mutation points,and the classification accuracy was further improved through dihedral angle filtering.Finally,this article designs a classification method based on region segmentation and decision trees,achieving accurate classification of point cloud data.The research results indicate that the method proposed in this paper can efficiently remove noise,extract key features,and achieve high-precision point cloud data classification,providing strong support for the application of airborne laser LiDAR technology.