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Hyperspectral unmixing is the process of estimating constituent endmembers and their fractional abundances present at each pixel in a hyperspectral image. A hyperspectral image is often corrupted by several kinds of noise. This work addresses the hyperspectral unmixing problem in a general scenario that consider the presence of mixed noise. The unmixing model explicitly takes into account both Gaussian noise and sparse noise. The unmixing problem has been formulated to exploit joint-sparsity ofdoi:10.1109/jstars.2016.2521898 fatcat:dsygey4jp5d5ro5fpru55kzaqu