Wavelet dimension reduction of AIRS infrared (IR) hyperspectral data

Recently developed hyperspectral sensors provide much richer information than comparable multispectral sensors. However traditional methods that have been designed for multispectral data are not easily adaptable to hyperspectral data. One way to approach this problem is to perform dimension reduction as pre-processing, i.e. to apply a transformation that brings data from a high order dimension to a low order dimension. Wavelet spectral analysis of hyperspectral images has been recently proposed as a method for dimension reduction and, when tested on the classification of AVIRIS data, has shown promising results over the traditional principal component analysis (PCA) technique. We propose to extend and apply the wavelet analysis reduction method to the Atmospheric Infrared Sounder (AIRS) instrument data, designed to measure the Earth´s atmospheric water vapor and temperature profiles on a global scale. With more than 2,000 channels, the AIRS infrared data represent a good candidate for dimension reduction, and especially wavelet reduction, due to its computational efficiency and the large data sizes involved.