Blind source separation applied to spectral unmixing: Comparing different measures of nongaussianity

Abstract

We report some of our results of a particular blind source separation technique applied to spectral unmixing of remote-sensed hyperspectral images. Different nongaussianity measures are introduced in the learning procedure, and the results are compared to assess their relative efficiencies, with respect to both the output signal-to-interference ratio and the overall computational complexity. This study has been conducted on both simulated and real data sets, and the first results show that skewness is a powerful and unexpensive tool to extract the typical sources that characterize remote-sensed images. © Springer-Verlag Berlin Heidelberg 2007.