Creating and sharing knowledge for telecommunications

Blind Hyperspectral Unmixing

Nascimento, J. ; Bioucas-Dias, J.

Blind Hyperspectral Unmixing, Proc Conf. on Telecommunications - ConfTele, Peniche, Portugal, Vol. I, pp. 617 - 624, May, 2007.

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Abstract
This paper introduces a new hyperspectral unmixing
method called Dependent Component Analysis (DECA). This method decomposes a hyperspectral image into a collection of reflectance (or radiance) spectra of the materials present in the scene (endmember signatures) and the corresponding abundance fractions at each pixel.
DECA models the abundance fractions as mixtures of Dirichlet densities, thus enforcing the constraints on abundance fractions imposed by the acquisition process, namely non-negativity and constant sum. The mixing matrix is inferred by a generalized expectation-maximization (GEM) type algorithm. This method
overcomes the limitations of unmixing methods based on Independent Component Analysis (ICA) and on geometrical based approaches. DECA performance is illustrated using simulated and real data.