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We propose a new spectral unmixing method using a semantic spectral representation, which is produced via non-homogeneous hidden Markov chain (NHMC) models applied to wavelet transforms of the spectra.

We propose a new spectral unmixing method using a semantic spectral representation, which is produced via non-homogeneous hidden Markov chain (NHMC) models applied to wavelet transforms of the spectra. Previous studies have shown that the representation is robust to spectral variability in the same materials because it can automatically detect the diagnostic spectral features in the training data. Therefore, our method can successfully detect materials while automatically extracting diagnostic features, showing high resilience to spectral variability. Simulations indicate that our unmixing method could be effectively used on Hapke mixtures. This is joint work with Yuki Itoh, Siwei Feng and Marco Duarte and was presented at the IEEE International Midwest Symposium on Circuits and Systems (MWSCAS), August 3-6, 2014 in College Station, TX.