Support Vector Machine Based Classification for Hyperspectral Remote Sensing Images after Minimum Noise Fraction Rotation Transformation

  • Authors:

  • Zhang Denghui;Yu Le

  • Affiliations:

  • -;-

  • Venue:
  • ICICIS '11 Proceedings of the 2011 International Conference on Internet Computing and Information Services
  • Year:
  • 2011

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Abstract

The component selection of minimum noise fraction (MNF) rotation transformation is analyzed in terms of classification accuracy using support vector machine (SVM) as a classifier for hyper spectral image. Five different group of different number of MNF components are evaluated using validation points and validation map. Further evaluation including classification error distribution and separation-class accuracies comparison are performed. The experimental result using AVIRIS hyper spectral data shows that keep about 1/10 MNF components could achieve best accuracies. However, for different target classes, the optimal number of MNF components is variance.