Jiantao Cui

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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)
Spectral unmixing has been a useful technique for hyperspectral data exploration since the earliest days of imaging spectroscopy. As nonlinear mixing phenomena are often observed in hyperspectral imagery, linear unmixing methods are often unable to unmix the nonlinear mixtures appropriately. In this paper, we propose a novel blind unmixing algorithm,(More)
Endmember extraction is an important and challenging step to solve the spectral unmixing problem. Most existing endmember extraction algorithms (EEAs) usually find image pixels as endmembers assuming the presence of pure pixels in an image scene or generate virtual endmembers without pure-pixel assumption. When some prevalent materials have pure-pixel(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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