Surface feature analysis of ground based LiDAR data

Yo Wei Liu; Yi-Hsing Tseng; Ying Zhe Luo

Surface feature analysis of ground based LiDAR data

Yo Wei Liu, Yi-Hsing Tseng, Ying Zhe Luo

Department of Geomatics

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Citation (Scopus)

Abstract

Ground-based LiDAR shows the potentiality of highly automatic building detection and reconstruction. Due to the main structure of the building are the walls. When a surface feature is extracted from a point cloud, the discrepancies of points to the surface are resulted from the undulation of the scanned object surface. We focus on the analysis of the characteristics of surface features. We use some methods like least squares fitting (LSF) and principal component analysis (PCA) to discuss the different factors such as point density, distribution, noise. The goal of this paper is to investigate the different scanning characteristics how to influence the extraction of the walls. Experimental results of computations with simulated data are shown for analysis.

Original language	English
Title of host publication	30th Asian Conference on Remote Sensing 2009, ACRS 2009
Pages	1788-1793
Number of pages	6
Publication status	Published - 2009 Dec 1
Event	30th Asian Conference on Remote Sensing 2009, ACRS 2009 - Beijing, China Duration: 2009 Oct 18 → 2009 Oct 23

Publication series

Name	30th Asian Conference on Remote Sensing 2009, ACRS 2009
Volume	3

Other

Other	30th Asian Conference on Remote Sensing 2009, ACRS 2009
Country/Territory	China
City	Beijing
Period	09-10-18 → 09-10-23

All Science Journal Classification (ASJC) codes

Computer Networks and Communications

Cite this

@inproceedings{becabae838a243949e05b7da6e81d99a,

title = "Surface feature analysis of ground based LiDAR data",

abstract = "Ground-based LiDAR shows the potentiality of highly automatic building detection and reconstruction. Due to the main structure of the building are the walls. When a surface feature is extracted from a point cloud, the discrepancies of points to the surface are resulted from the undulation of the scanned object surface. We focus on the analysis of the characteristics of surface features. We use some methods like least squares fitting (LSF) and principal component analysis (PCA) to discuss the different factors such as point density, distribution, noise. The goal of this paper is to investigate the different scanning characteristics how to influence the extraction of the walls. Experimental results of computations with simulated data are shown for analysis.",

author = "Liu, {Yo Wei} and Yi-Hsing Tseng and Luo, {Ying Zhe}",

year = "2009",

month = dec,

day = "1",

language = "English",

isbn = "9781615679843",

series = "30th Asian Conference on Remote Sensing 2009, ACRS 2009",

pages = "1788--1793",

booktitle = "30th Asian Conference on Remote Sensing 2009, ACRS 2009",

note = "30th Asian Conference on Remote Sensing 2009, ACRS 2009 ; Conference date: 18-10-2009 Through 23-10-2009",

}

TY - GEN

T1 - Surface feature analysis of ground based LiDAR data

AU - Liu, Yo Wei

AU - Tseng, Yi-Hsing

AU - Luo, Ying Zhe

PY - 2009/12/1

Y1 - 2009/12/1

N2 - Ground-based LiDAR shows the potentiality of highly automatic building detection and reconstruction. Due to the main structure of the building are the walls. When a surface feature is extracted from a point cloud, the discrepancies of points to the surface are resulted from the undulation of the scanned object surface. We focus on the analysis of the characteristics of surface features. We use some methods like least squares fitting (LSF) and principal component analysis (PCA) to discuss the different factors such as point density, distribution, noise. The goal of this paper is to investigate the different scanning characteristics how to influence the extraction of the walls. Experimental results of computations with simulated data are shown for analysis.

AB - Ground-based LiDAR shows the potentiality of highly automatic building detection and reconstruction. Due to the main structure of the building are the walls. When a surface feature is extracted from a point cloud, the discrepancies of points to the surface are resulted from the undulation of the scanned object surface. We focus on the analysis of the characteristics of surface features. We use some methods like least squares fitting (LSF) and principal component analysis (PCA) to discuss the different factors such as point density, distribution, noise. The goal of this paper is to investigate the different scanning characteristics how to influence the extraction of the walls. Experimental results of computations with simulated data are shown for analysis.

UR - http://www.scopus.com/inward/record.url?scp=84866104471&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84866104471&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:84866104471

SN - 9781615679843

T3 - 30th Asian Conference on Remote Sensing 2009, ACRS 2009

SP - 1788

EP - 1793

BT - 30th Asian Conference on Remote Sensing 2009, ACRS 2009

T2 - 30th Asian Conference on Remote Sensing 2009, ACRS 2009

Y2 - 18 October 2009 through 23 October 2009

ER -

Surface feature analysis of ground based LiDAR data

Abstract

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All Science Journal Classification (ASJC) codes

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