Using the one-dimensional S-transform as a discrimination tool in classification of hyperspectral images

A standard part of processing remote sensing data is image classification, in which we assume each pixel belongs to a class or theme with a unique spectral signature. Discrimination may be defined as the phenomenon where multiple themes exhibit very similar spectral patterns within a wavelength range of interest and is a common challenge in remote sensing. As a result, researchers may not achieve the desired classification accuracy. A robust discrimination technique must be capable of detecting very minor spectral differences between classes with similar spectral signatures. Using the one-dimensional S-transform, a spectral localization technique to discriminate similar lithologic classes on a hyperspectral satellite image, we investigated the S-amplitude spectra efficiency in enhancing the spectral information of each pixel of a known class. We compared the overall accuracy of classified themes using a support vector classification (SVC) scheme, with and without using the enhanced spectral information. We found that SVC aided by spectral enhancement from the S-transform provided better classification accuracy. Thus, this method may prove very useful in scenarios where pixels of a known class are sparse and not easily separable.