Filtering of Airborne LiDAR Point Cloud with a Method Based on Kernel Density Estimation (KDE)

被引：0
|
作者：
Tian, X-R. [1 ,5 ]
Xu, L-J. [1 ,2 ]
Li, X-L. [1 ,3 ]
Xu, T. [1 ,3 ]
Yao, J-N. [4 ]
机构：
[1] Beihang Univ, Sch Instrument Sci & Optoelect Engn, Beijing 100191, Peoples R China
[2] Beihang Univ, Minist Educ, Key Lab Precis Optomechatron Technol, Beijing 100191, Peoples R China
[3] Beihang Univ, Key Lab Novel Inertial Instruments & Nav Syst Tec, Beijing 100191, Peoples R China
[4] Beihang Univ, Sch Phys & Nucl Energy Engn, Beijing 100191, Peoples R China
[5] Nanjing Univ Aeronaut & Astronaut, Coll Automat Engn, Nanjing 210016, Jiangsu, Peoples R China
来源：
LASERS IN ENGINEERING | 2016年 / 34卷 / 4-6期
基金：
中国国家自然科学基金;
关键词：
Airborne LiDAR; point cloud; filtering; kernel density estimation (KDE); statistical features;
D O I：
暂无
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
In this paper a new method is proposed for filtering airborne light detection and ranging (LiDAR) point cloud data based on kernel density estimation (KDE). The point cloud data is divided into a number of blocks at different sizes step by step. In each block, the kernel probability density of the elevation values of all points is estimated, and a threshold value is selected for data filtering by referring the elevation value of the maximum probability density point. The points whose elevation values are lower than the threshold are classified as ground points. Because the method does not focus on the calculation of individual points, the computation complexity is greatly reduced. Experimental results show that the filtering method is valid and efficient for massive point cloud filtering.
引用
收藏
页码：221 / 237
页数：17
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