无人机成像点云与LiDAR点云用于DEM构建精度对比分析  

Comparison and Analysis of DEM Construction Accuracy Between UAV Imaging Point Clouds and LiDAR Point Clouds

作　　者：杨在松 甘淑[1,2] 袁希平 高莎 罗为东 和文斌 YANG Zaisong;GAN Shu;YUAN Xiping;GAO Sha;LUO Weidong;HE Wenbin(Faculty of Land Resources and Engineering,Kunming University of Science and Technology,Kunming 650093,China;Application Engineering Research Center of Spatial Information Surveying and Mapping Technology in Plateau and Mountainous Areas Set by Universitiesin Yunnan Province,Kunming 650093,China;School of Earth Sciences and Engineering,West Yunnan University of Applied Sciences,Dali 671006,China)

机构地区：[1]昆明理工大学国土资源工程学院,云南昆明650093 [2]云南省高校高原山区空间信息测绘技术应用工程研究中心,云南昆明650093 [3]滇西应用技术大学地球科学与工程技术学院,云南大理671006

出　　处：《城市勘测》2023年第4期78-83,共6页Urban Geotechnical Investigation & Surveying

基　　金：国家自然科学基金项目(41861054);国家自然科学基金项目(62266026)。

摘　　要：针对传统的地面调查及卫星遥感手段构建DEM存在测量难度大或制图分辨率低等问题,通过无人机(Unmanned Aerial Vehicle,UAV)成像点云与机载激光雷达(Light Detection and Ranging,LiDAR)点云构建高分辨率DEM,并对比分析DEM产品的误差及其空间分布差异。采用多种滤波算法分类成像点云与LiDAR点云中的地面点云,结合点云内插的误差分析实验,得出以下结论:①利用点云的光谱信息能增强改进的渐进三角网滤波算法的效果;②成像点云和LiDAR点云构建DEM的最佳点云密度分别为27.70点/m^(2)和16.44点/m^(2);③成像点云X和Y方向的误差集中在±0.1 m之间,Z方向的误差离散程度大,集中在±0.6 m之间,LiDAR点云X和Y方向的误差集中在±0.3 m之间,Z方向的误差集中在±0.5 m之间。研究结果可为点云用于地形复杂区域的高精度地形建模提供参考。In response to the problems of the traditional ground survey and satellite remote sensing means to construct DEM with high measurement difficulty or low mapping resolution,we construct high-resolution DEM by Unmanned Aerial Vehicle(UAV)imaging point clouds and airborne LiDAR(Light Detection and Ranging,LiDAR)point clouds,and compare the errors of DEM products and their spatial distribution differences are analyzed.Multiple filtering algorithms were used to classify the ground point clouds in the imaging point clouds and LiDAR point clouds,and the following conclusions were drawn from the error analysis experiments combined with point clouds interpolation:①Using the spectral information of the point clouds can enhance the effect of the improved progressive triangular mesh filtering algorithm.②The optimal point clouds densities for imaging point clouds and LiDAR point clouds for constructing DEMs are 27.70 points/m^(2)and 16.44 points/m^(2),respectively.③The errors in the X and Y directions of the imaging point clouds are concentrated between±0.1m,the errors in the Z direction are discrete and concentrated between±0.6m,the errors in the X and Y directions of the LiDAR point clouds are concentrated between±0.3m and the errors in the Z direction are concentrated between±0.5m.The results of the study can provide a reference for point clouds to be used for high-precision terrain modeling in areas with complex topography.

关 键 词：无人机 激光雷达 点云 DEM 精度分析 

分 类 号：P225.2[天文地球—大地测量学与测量工程] P237[天文地球—测绘科学与技术]