  Hyperspectral images contain mixed pixels due to low spatial resolution of
hyperspectral sensors. Mixed pixels are pixels containing more than one
distinct material called endmembers. The presence percentages of endmembers in
mixed pixels are called abundance fractions. Spectral unmixing problem refers
to decomposing these pixels into a set of endmembers and abundance fractions.
Due to nonnegativity constraint on abundance fractions, nonnegative matrix
factorization methods (NMF) have been widely used for solving spectral unmixing
problem. In this paper we have used graph regularized (GNMF) method with
sparseness constraint to unmix hyperspectral data. This method applied on
simulated data using AVIRIS Indian Pines dataset and USGS library and results
are quantified based on AAD and SAD measures. Results in comparison with other
methods show that the proposed method can unmix data more effectively.
