  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 NMF (GNMF) method
combined with sparseness constraint to decompose mixed pixels in hyperspectral
imagery. This method preserves the geometrical structure of data while
representing it in low dimensional space. Adaptive regularization parameter
based on temperature schedule in simulated annealing method also has been used
in this paper for the sparseness term. Proposed algorithm is applied on
synthetic and real datasets. Synthetic data is generated based on endmembers
from USGS spectral library. AVIRIS Cuprite dataset is used as real dataset for
evaluation of proposed method. Results are quantified based on spectral angle
distance (SAD) and abundance angle distance (AAD) measures. Results in
comparison with other methods show that the proposed method can unmix data more
effectively. Specifically for the Cuprite dataset, performance of the proposed
method is approximately 10% better than the VCA and Sparse NMF in terms of root
mean square of SAD.
