Title :
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
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;
Conference_Titel :
Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on
Conference_Location :
Vancouver, BC
DOI :
10.1109/ICASSP.2013.6638041
