Support Vector Machine and Spectral Angle Mapper Classifications of High Resolution Hyper Spectral Aerial Image

被引：0
|
作者：
Enkhbaatar, Lkhagva [1 ]
Jayakumar, S. [1 ]
Heo, Joon [1 ]
机构：
[1] Yonsei Univ, Coll Engn, Sch Civil & Environm Engn, Geomat & Remote Sensing GRS Lab, Seoul, South Korea
来源：
KOREAN JOURNAL OF REMOTE SENSING | 2009年 / 25卷 / 03期
关键词：
supervised classification; support vector machine classifier; spectral angle mapper classifier; high resolution aerial image;
D O I：
暂无
中图分类号：
TP7 [遥感技术];
学科分类号：
081102 ; 0816 ; 081602 ; 083002 ; 1404 ;
摘要：
This paper presents two different types of supervised classifiers such as support vector machine (SVM) and spectral angle mapper (SAM). The Compact Airborne Spectrographic Imager (CASI) high resolution aerial image was classified with the above two classifier. The image was classified into eight land use /land cover classes. Accuracy assessment and Kappa statistics were estimated for SVM and SAM separately. The overall classification accuracy and Kappa statistics value of the SAM were 69.0% and 0.62 respectively, which were higher than those of SVM (62.5%, 0.54).
引用
收藏
页码：233 / 242
页数：10
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