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The constrained nonnegative matrix factorization algorithm (CNMF) has previously been shown to be a useful method to solve the unmixing problem in hyper spectral remote sensing images, but it also has some key weaknesses which affect its applied range. It’s sensitive to the initial values, and easily falls to the local minimum. To solve the(More)
Hyper spectral unmixing (HU) is important for ground objects identification. Due to the mass data hyper spectral sensors bring, band selection plays an important role in boosting efficiency of HU. This paper proposes a high-efficiency approach of HU that carries out two modified algorithms of band selection followed by nonnegative matrix factorization(More)
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