A curvature-based statistical method for generating DTM from LiDAR Point Cloud

Author

Wan, Jianhua ; Huang, Ronggang ; Zeng, Zhe ; Sun, Shujuan

Author_Institution

Sch. of Geosci., China Univ. of Pet., Qingdao, China

fYear

2012

fDate

15-17 June 2012

Firstpage

1

Lastpage

4

Abstract

The result of Skewness Balancing based on elevation(SBE) contains non-ground points, which involve low vegetation, vehicle, and side of buildings. According to this problem, this paper proposes an algorithm to improve the precision of automatic filter and reduce the commission error. The algorithm is Curvature-based Statistical Method (CSM), which is based on the Skewness Balancing, and introduces curvature into Skewness Balancing. Compared with the result of Skewness Balancing based on elevation, the experiment demonstrates that the algorithm performs better.

Keywords

digital elevation models; geophysical techniques; optical radar; remote sensing by radar; DTM point cloud; LiDAR point cloud; automatic filter precision; curvature-based statistical method; digital elevation model; nonground points; remote sensing technology; skewness balancing; Cities and towns; Curvature-based Statistical Method; Light Detection and Ranging; Skewness Balancing;

fLanguage

English

Publisher

ieee

Conference_Titel

Geoinformatics (GEOINFORMATICS), 2012 20th International Conference on

Conference_Location

Hong Kong

ISSN

2161-024X

Print_ISBN

978-1-4673-1103-8

Type

conf

DOI

10.1109/Geoinformatics.2012.6270297

Filename

6270297