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

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.