  Hyperspectral images contain mixed pixels due to low spatial resolution of
hyperspectral sensors. Spectral unmixing problem refers to decomposing mixed
pixels into a set of endmembers and abundance fractions. Due to nonnegativity
constraint on abundance fractions, nonnegative matrix factorization (NMF)
methods have been widely used for solving spectral unmixing problem. In this
letter we proposed using multilayer NMF (MLNMF) for the purpose of
hyperspectral unmixing. In this approach, spectral signature matrix can be
modeled as a product of sparse matrices. In fact MLNMF decomposes the
observation matrix iteratively in a number of layers. In each layer, we applied
sparseness constraint on spectral signature matrix as well as on abundance
fractions matrix. In this way signatures matrix can be sparsely decomposed
despite the fact that it is not generally a sparse matrix. The proposed
algorithm is applied on synthetic and real datasets. Synthetic data is
generated based on endmembers from USGS spectral library. AVIRIS Cuprite
dataset has been used as a real dataset for evaluation of proposed method.
Results of experiments are quantified based on SAD and AAD measures. Results in
comparison with previously proposed methods show that the multilayer approach
can unmix data more effectively.
