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http://dx.doi.org/10.7780/kjrs.2009.25.3.233

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

Enkhbaatar, Lkhagva (Geomatics and Remote Sensing (GRS) Lab, School of Civil & Environmental Engineering, College of Engineering, Yonsei University)
Jayakumar, S. (Geomatics and Remote Sensing (GRS) Lab, School of Civil & Environmental Engineering, College of Engineering, Yonsei University)
Heo, Joon (Geomatics and Remote Sensing (GRS) Lab, School of Civil & Environmental Engineering, College of Engineering, Yonsei University)
Publication Information
Korean Journal of Remote Sensing / v.25, no.3, 2009 , pp. 233-242 More about this Journal
Abstract
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).
Keywords
supervised classification; support vector machine classifier; spectral angle mapper classifier; high resolution aerial image;
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Times Cited By KSCI : 1  (Citation Analysis)
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