Spectral Unmixing of Hyperspectral Imagery using Multilayer NMF
This work addresses the problem of mixed pixels in hyperspectral imaging for remote sensing applications, representing an incremental improvement over existing NMF-based methods.
The paper tackled the spectral unmixing problem in hyperspectral imagery by proposing a multilayer nonnegative matrix factorization (MLNMF) method, which applied sparseness constraints iteratively across layers to decompose mixed pixels into endmembers and abundances, showing more effective unmixing compared to previous methods as quantified by SAD and AAD measures on synthetic and real datasets.
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.