地理空间信息2024，Vol.22Issue(1)：13-15,28,4.DOI:10.3969/j.issn.1672-4623.2024.01.003
一种DEM辅助下的LiDAR点云PTD滤波改进算法
Improved LiDAR Point Cloud PTD Filtering Algorithm Assisted by DEM
摘要
Abstract
In view of the limitations of traditional progressive TIN densification(PTD)filtering algorithm,which needed to repeatedly debug the ground point judgment parameters to obtain better results in complex terrain environment,we took the terrain elevation and terrain gradient ex-tracted from previous DEM data as an auxiliary to improve the selection method of initial ground seed points in the PTD and optimize the ground point judgment parameters.Then,we detected and processed the terrain changes between previous DEM data and current LiDAR point cloud da-ta.This algorithm is suitable for complex terrain with different slope terrain conditions,and the filtering effect is good.Through the comparative analysis of experimental data accuracy,the algorithm can effectively reduce Class Ⅰ and Class Ⅱ errors,and the sample classification accuracy is more than 90％,which proves that the filtering accuracy of PTD algorithm can be effectively improved based on DEM assistance.关键词
LiDAR点云/PTD滤波/DEM辅助分类Key words
LiDAR point cloud/PTD filtering/DEM assisted classification分类
天文与地球科学引用本文复制引用
郑斌,邹学忠,李小昱..一种DEM辅助下的LiDAR点云PTD滤波改进算法[J].地理空间信息,2024,22(1):13-15,28,4.基金项目
江苏省水利科技资助项目(2020061). （2020061）
