Spectral unmixing of hyperspectral images with the independent component analysis and wavelet packets

Abstract

The study addresses the problem of spectral unmixing hyperspectral images, technique allowing the spectra and abundance of each pure material present in each pixel of a scene to be extracted. We first remark that the linear model commonly used in spectral unmixing is exactly the same as the model used in the independant component analysis (ICA), a blind source separation technique studied in the signal processing community; ICA allows each source to be extracted from the observation of some linear combinations-of these ones, based on the assumption of their statistical independence. We show the interest of analyzing the spectra issued from a wavelet packets transformation in order to deal with the assumption of independence, which is usually not verified for natural spectra. A pyramidal algorithm is implemented, allowing the problem of the great number of observations to be addressed.