Abstract
This paper explores the performance of sparse unmixing (SU) on non-linear mixtures. We consider SU as an endmember selection method from a spectral library and measure its performance using recently proposed approximately perfect recovery condition for sparse unmixing, comparing with non-negative least squares (NNLS). We also further explore the effect of thresholding on SU and NNLS. Simulations on various kinds of non-linear mixtures and an experiment on real hyperspectral data show that thresholding greatly improves the performance in endmember selection and NNLS could outperform SU when combined with thresholding.
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