Korean Journal of Remote Sensing (대한원격탐사학회지)

Korean Society of Remote Sensing (대한원격탐사학회)
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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)
  • Published : 2009.06.28
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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).

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References

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