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Supervised algorithm for polynomial post-nonlinear unmixing of hyperspectral image unmixing

A new polynomial post-nonlinear mixing model has been proposed for hyperspectral image unmixing. The proposed model assumes that the pixel reflectances are nonlinear functions of pure spectral components. These nonlinear functions are approximated using polynomial functions leading to a polynomial post-nonlinear mixing model. A Bayesian algorithm and optimization methods are proposed to estimate the parameters involved in the model.

The model is described in the paper published in IEEE Trans. Image Processing in 2012:

One of the abundance estimation procedure (based on a gradient-based algorithm) is available as MATLAB code:

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