  Hyperspectral remote sensing is a prominent research topic in data
processing. Most of the spectral unmixing algorithms are developed by adopting
the linear mixing models. Nonnegative matrix factorization (NMF) and its
developments are used widely for estimation of signatures and fractional
abundances in the SU problem. Sparsity constraints was added to NMF, and was
regularized by $ L_ {q} $ norm. In this paper, at first hyperspectral images
are clustered by fuzzy c- means method, and then a new algorithm based on
sparsity constrained distributed optimization is used for spectral unmixing. In
the proposed algorithm, a network including clusters is employed. Each pixel in
the hyperspectral images considered as a node in this network. The proposed
algorithm is optimized with diffusion LMS strategy, and then the update
equations for fractional abundance and signature matrices are obtained.
Simulation results based on defined performance metrics illustrate advantage of
the proposed algorithm in spectral unmixing of hyperspectral data compared with
other methods.
