Hyperspectral Image Classification Based on Features of Wavelet Vectors

A novel approach for mapping hyperspectral data is presented by extracting the feature of wavelet vectors.The expected decomposed level-L is decided for a given hyperspectral imagery and then a function is constructed to extract the feature of each vector for every pixel.Classified image is obtained by comparing the feature between the pixel vector and the reference spectra.Comparisons with spectral angle mapping(SAM) and cross correlation spectral match(CCSM) are also done.