Hyperspectral Data Feature Extraction Using Rational Function Curve Fitting

In this paper, a new feature reduction technique for hyperspectral data classification problem is proposed based on extracting new features with smaller dimension with respect to the original data. For each pixel of a hyperspectral image, a specific rational function approximation is developed to fit its own spectral response curve (SRC) and the coefficients of the numerator and denominator polynomials of this function are considered as new extracted features. The method focuses on geometrical nature of SRCs and relies on the fact that the sequence discipline - ordinance of reflectance coefficients in spectral response curve - contains some information which has not been addressed by many other existing methods that are based on statistical analysis of data. Maximum likelihood classification results demonstrate that our method provides better classification accuracies compared to many competing feature extraction algorithms. In addition, the proposed algorithm has the possibility to be applied to all pixels of image individually and simultaneously.