Blind Source Separation Applied to Spectral Unmixing: Comparing Different Measures of Nongaussianity

CESAR F. CAIAFA; EMANUELE SALERNO; ARACELI N. PROTO

Knowledge-Based Intelligent Information and Engineering Systems

SPRINGER

Lugar: Berlin; Año: 2007; p. 1 - 8

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.