基于高程跳变的机载激光雷达点云数据滤波方法

Filtering method for airborne LiDAR point cloud data based on elevation jump

针对数字高程模型中相邻像元间高程值容易出现与周围地形特征不连续的突变现象,影响其数据滤波效果的问题,本次研究提出了一种基于高程跳变的机载激光雷达点云数据滤波方法,其将周围地形特征为机载激光雷达点云数据滤波的重要依据,对相邻像元间的高程数据进行处理,实现数据滤波。该方法先深入剖析了机载激光雷达点云的数据特征与各类地物的独特属性,为每一点云精准赋予相应的二进制信号,并将其引入BP神经网络,实现对机载激光雷达点云的精细分类。然后,依据点云分类的结果,结合不规则三角网的拓扑邻接关系,考虑高程因素与空间角度设定动态阈值,实现任意高程范围内地物数据的滤波处理。最后,引入了虚拟格网技术,对全部点云数据进行处理,结合局部自适应阈值方法,针对剩余的非地面点数据进行滤波。实验研究表明,所提方法具有比较强的适应性,可获取较好的机载激光雷达点云数据滤波效果,具有更大的应用价值。

In response to the problem that the elevation values between adjacent pixels in the digital elevation model are prone to discontinuity with the surrounding terrain features,which affects the data filtering effect,this study proposes an airborne LiDAR point cloud data filtering method based on elevation jump.The surrounding terrain features are an important basis for filtering airborne LiDAR point cloud data,and the elevation data between adjacent pixels is processed to achieve data filtering.This method first deeply analyzes the data characteristics of airborne LiDAR point clouds and the unique attributes of various ground objects,accurately assigns corresponding binary signals to each point cloud,and introduces them into the BP neural network to achieve fine classification of airborne LiDAR point clouds.Then,based on the results of point cloud classification,combined with the topological adjacency relationship of irregular triangulation networks,dynamic thresholds are set considering elevation factors and spatial angles to achieve filtering processing of ground feature data within any elevation range.Finally,virtual grid technology was introduced to process all point cloud data,combined with local adaptive threshold methods,to filter the remaining non ground point data.Experimental studies have shown that the proposed method has strong adaptability and can achieve good filtering effects on airborne LiDAR point cloud data,with greater application value.