Abstract
In this paper, hyperspectral data is modeled as a combination of a sparse component, a low rank component and noise. The low rank component is a product of the endmembers and the abundances in an image, and the sparse component is composed of outliers and structured noise. Outliers and structured noise in this context are, e.g. band specific noise, vertical or horizontal artifacts or saturated pixels. Sparse and low rank matrix decomposition (SLR) is a method that has recently been developed for estimating those components. Here, an algorithm based on l 1 SLR is developed using sparse blind hyperspectral unmixing and soft thresholding. The number of endmembers and the sparsity parameters are selected using the extended Bayesian information criterion (EBIC). The proposed algorithm is evaluated using a real remote sensing hyperspectral image.
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