Improving hyperspectral classification based on wavelet decomposition 1Ophir Almog

Information extraction from hyperspectral imagery is highly affected by difficulties in accounting for flux density variation and bidirectional reflectance effects. Calculation of flux density requires digital description of the surface structure at the pixel level, which is frequently not available at the accuracy required (if exists). The result of these shortcomings in achieving accurate radiometric image calibration is reduced separability of surface types: limiting the performance of spectral classification schemes. In this study an alternative approach is presented: application of features of the spectral signature which mainly represent the shape of the spectral curve. This is achieved by applying features calculated based on Wavelet decomposition.