A hybrid approach for tree classification in airborne LIDAR data

Author

XiaoLing Li ; Wenjun Zeng ; Ye Duan

Author_Institution

Dept. of Comput. Sci., Univ. of Missouri, Columbia, MO, USA

fYear

2013

Firstpage

2183

Lastpage

2187

Abstract

In this paper we propose a hybrid approach for tree classification in airborne LIDAR (Light Detection and Ranging) data by integrating the point based supervised classification with region-based unsupervised clustering method. Furthermore we propose a novel 3D robust statistics-based shape feature that can overcome the limitations of existing methods in separating building boundary points from tree points. Experimental results show the new algorithm is very effective and can achieve very high accuracy.

Keywords

airborne radar; electrical engineering computing; optical radar; pattern classification; pattern clustering; statistical analysis; support vector machines; unsupervised learning; 3D robust statistics-based shape feature; airborne LIDAR data; building boundary point; light detection and ranging; point based supervised classification; region-based unsupervised clustering method; support vector machine; tree classification; Accuracy; Buildings; Laser radar; Shape; Three-dimensional displays; Training data; Vegetation; Airborne LIDAR; Classification; Robust Statistics; Tree;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on

Conference_Location

Vancouver, BC

ISSN

1520-6149

Type

conf

DOI

10.1109/ICASSP.2013.6638041

Filename

6638041