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

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

LECTURE NOTES IN COMPUTER SCIENCE

SPRINGER

Lugar: Berlin; Año: 2007 vol. 4694 p. 1 - 8

0302-9743

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.