Spectral unmixing of hyperspectral images with the independent component analysis and wavelet packets
Marc Lennon, Grégoire Mercier, M.C. Mouchot, Laurence Hubert‐Moy · 2002
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.