基于Python的点云分类异常地面点自动探测方法

Automatic Detection of Abnormal Ground Points by Point Cloud Classification Based on Python

在应用LiDAR点云数据生产DEM过程中,由于滤波算法的局限性和人工编辑误操作,点云分类过程中会产生影响DEM精度的异常地面点。针对该问题,本文提出了一种基于Python语言的点云分类异常地面点自动探测的方法,通过实践验证了该方法的可行性和有效性,在大规模DEM生产中具有实际应用价值。

In the process of using LiDAR point cloud data to produce DEM,because of the limitation of filtering algorithm and misoperation of manual editing,abnormal ground points affecting the accuracy of DEM will be generated in the process of point cloud classification.Aiming at this problem,this paper presents a method of automatic detection of abnormal ground points based on Python language.The feasibility and validity of this method are verified by experiments,and it has practical application value in large-scale DEM production.