We consider the application of non-homogeneous hidden Markov chain (NHMC) models to the problem of hyperspectral signature classification. It has been previously shown that the NHMC model enables the detection of several semantic structural features of hyperspectral signatures.

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 consider the problem of designing features for the identification of reflectance spectra that capture the semantic information used by experts in ad-hoc labeling rules, such as the shape and position of absorption bands in spectra. We propose the use of statistical models on wavelet coefficients that allow us to quantify the presence of spectrum discontinuities at multiple scales.