University of Toulouse, IRIT/INP-ENSEEIHT/TeSA, Toulouse, France.
IEEE Trans Image Process. 2012 Jun;21(6):3017-25. doi: 10.1109/TIP.2012.2187668. Epub 2012 Feb 13.
This paper presents a nonlinear mixing model for hyperspectral image unmixing. The proposed model assumes that the pixel reflectances are nonlinear functions of pure spectral components contaminated by an additive white Gaussian noise. These nonlinear functions are approximated using polynomial functions leading to a polynomial postnonlinear mixing model. A Bayesian algorithm and optimization methods are proposed to estimate the parameters involved in the model. The performance of the unmixing strategies is evaluated by simulations conducted on synthetic and real data.
本文提出了一种用于高光谱图像解混的非线性混合模型。该模型假设像素反射率是纯光谱分量的非线性函数，受到加性白高斯噪声的污染。这些非线性函数使用多项式函数进行逼近，从而得到多项式后非线性混合模型。提出了一种贝叶斯算法和优化方法来估计模型中涉及的参数。通过在合成和真实数据上进行的仿真评估了解混策略的性能。
