Hyperspectral Data Feature Extraction Using Rational Function Curve Fitting

Abstract

A feature reduction technique is proposed for the hyperspectral (HS) data classification problem. The new features have been developed through a curve fitting step which fits specific rational function approximations to every spectral response curve (SRC) of HS image pixels. Then, the coefficients of the numerator and denominator polynomials of these fitted functions are considered as new extracted features. The method concentrates on the geometrical nature of SRCs and is utilizing the information that exists in sequence discipline — ordinance of reflectance coefficients in SRC — which has not been addressed by many other statistical analysis based methods. Maximum likelihood (ML) classification results show that the proposed method provides better classification accuracies compared to some basic and state-of-the-art feature extraction methods. Moreover, the proposed algorithm has the capability of being applied individually and simultaneously to all pixels of image.