摘要
机载LiDAR采集的点云数据中会存在一些局部区域地面点稀疏的情况,利用这些稀疏地面点构建DEM时会出现“三角面片化”的问题,严重影响DEM的质量。为此,本文提出了一种局部稀疏地面点云与已有DEM的融合方法:将稀疏点云作为高精度控制点,在尽量保持原始DEM的地形形态特征的前提下,通过高斯核函数加权迭代插值算法对DEM进行高程局部改正,实现稀疏点云与DEM的一致性融合。试验分析表明,融合后的点云数据得到了较好的补充,由此构建的DEM地形形态自然,在精度上相对于融合前的稀疏地面点云有一定改善,在弱精度区域的可靠性有显著提升。
There will be sparse ground points that point cloud datas produced by airborne LiDAR in some local areas.When we use them to construct DEM,the problem of“triangle patch”will occur,which seriously affects the quality of DEM.This paper proposes a fusion method of sparse ground point cloud datas and existing DEM.The sparse point cloud as a high-precision control points,on the premise of keeping the topographic features of the original DEM as much as possible,the local elevation correction of DEM is carried out through Gaussian kernel function weighted iterative interpolation algorithm to realize the consistent fusion of sparse point cloud and DEM.The experiment shows that the fused point cloud datas are well supplemented,the new DEM has a natural topography.At the sametime,the accuracy is improved to a certain extent after fusion,its reliability is significantly improved in weak precision areas.
作者
雷丽珍
林超
LEI Lizhen;LIN Chao(Land Resource and Information Center of Guangdong Province,Guangzhou 510075,China)
出处
《测绘通报》
CSCD
北大核心
2019年第9期77-81,共5页
Bulletin of Surveying and Mapping
关键词
DEM
稀疏点云
融合
高斯核函数
加权迭代
DEM
sparse ground points
fusion
Gaussian kernel function
weighted iteration
