Application of 1D S-Transform in Discrimination Problems in Remote Sensing

Image classification is a standard part of processing remote sensing data and is based on the assumption that each pixel belongs to a class or theme with a unique spectral signature. However, a common challenge in remote sensing is image discrimination, also known as the "colinearity". It may be defined as the phenomenon where multiple themes exhibit very similar spectral patterns within a wavelength-range of interest. As a result, the desired classification accuracy might not be achieved. A robust discrimination technique must have the capability to detect very minor spectral differences between classes with similar spectral signatures. One-dimensional S-Transform, a spectral localization technique, was used to discriminate similar lithologic classes on hyper-spectral satellite images. We investigated the efficiency of the S-amplitude spectra in enhancing the spectral information of each pixel of a known class. We compared the overall accuracy of classified themes using Support Vector Classification (SVC) scheme, with and without the enhanced spectral information. We found that SVC aided by spectral enhancement from 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.

Keywords: Remote Sensing, S-Transform, Discrimination, Support Vector Classification, Hyper-spectral, Classification.