A HYBRID APPROACH FOR TREE CLASSIFICATION IN AIRBORNE LIDAR DATA

被引：0

作者：

Li, Xiaoling ^{[1
]}

Zeng, Wenjun ^{[1
]}

Duan, Ye ^{[1
]}

机构：

[1] Univ Missouri, Dept Comp Sci, Columbia, MO 65211 USA

来源：

2013 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP) | 2013年

关键词：

Airborne LIDAR; Classification; Tree; Robust Statistics;

D O I：

暂无

中图分类号：

O42 [声学];

学科分类号：

070206 ; 082403 ;

摘要：

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.

引用

页码：2183 / 2187

页数：5

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