One of the challenges in hyperspectral data analysis is the presence of mixedpixels. Mixed pixels are the result of low spatial resolution of hyperspectralsensors. Spectral unmixing methods decompose a mixed pixel into a set ofendmembers and abundance fractions. Due to nonnegativity constraint onabundance fraction values, NMF based methods are well suited to this problem.In this paper multilayer NMF has been used to improve the results of NMFmethods for spectral unmixing of hyperspectral data under the linear mixingframework. Sparseness constraint on both spectral signatures and abundancefractions matrices are used in this paper. Evaluation of the proposed algorithmis done using synthetic and real datasets in terms of spectral angle andabundance angle distances. Results show that the proposed algorithm outperformsother previously proposed methods.
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