Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  studies a hierarchical Bayesian model for nonlinear hyperspectral image unmixing. The proposed model assumes that the pixel reflectances are polynomial functions of linear mixtures of pure spectral components contaminated by an additive white Gaussian noise. The parameters involved in this model satisfy constraints that are naturally expressed within a Bayesian framework. A Gibbs sampler allows one to sample the unknown abundances and nonlinearity parameters according to the joint posterior of interest. The performance of the resulting unmixing strategy is evaluated thanks to simulations conducted on synthetic and real data.
['Yoann Altmann', 'Abderrahim Halimi', 'Nicolas Dobigeon', 'Jean-Yves Tourneret']
I am writing a research paper on a idea Supervised nonlinear spectral unmixing using a polynomial post nonlinear model for hyperspectral imagery
[{"from": "human", "value": "I am writing a research paper on a idea Supervised nonlinear spectral unmixing using a polynomial post nonlinear model for hyperspectral imagery"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  studies a hierarchical Bayesian model for nonlinear hyperspectral image unmixing. The proposed model assumes that the pixel reflectances are polynomial functions of linear mixtures of pure spectral components contaminated by an additive white Gaussian noise. The parameters involved in this model satisfy constraints that are naturally expressed within a Bayesian framework. A Gibbs sampler allows one to sample the unknown abundances and nonlinearity parameters according to the joint posterior of interest. The performance of the resulting unmixing strategy is evaluated thanks to simulations conducted on synthetic and real data."}]