University of Toulouse, Toulouse Cedex, France.
IEEE Trans Image Process. 2014 Jun;23(6):2663-2675. doi: 10.1109/TIP.2014.2314022. Epub 2014 Mar 26.
This paper presents a nonlinear mixing model for hyperspectral image unmixing. The proposed model assumes that the pixel reflectances are post-nonlinear functions of unknown pure spectral components contaminated by an additive white Gaussian noise. These nonlinear functions are approximated using second-order polynomials leading to a polynomial post-nonlinear mixing model. A Bayesian algorithm is proposed to estimate the parameters involved in the model yielding an unsupervised nonlinear unmixing algorithm. Due to the large number of parameters to be estimated, an efficient Hamiltonian Monte Carlo algorithm is investigated. The classical leapfrog steps of this algorithm are modified to handle the parameter constraints. The performance of the unmixing strategy, including convergence and parameter tuning, is first evaluated on synthetic data. Simulations conducted with real data finally show the accuracy of the proposed unmixing strategy for the analysis of hyperspectral images.
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未知纯光谱分量的后非线性函数，这些纯光谱分量受到加性白高斯噪声的污染。这些非线性函数使用二阶多项式进行近似，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来估计模型中涉及的参数，从而得到一种无监督的非线性解混算法。由于需要估计的参数数量众多，因此研究了一种高效的哈密顿蒙特卡罗算法。该算法的经典蛙跳步骤被修改以处理参数约束。首先在合成数据上评估解混策略的性能，包括收敛性和参数调整。最后，使用真实数据进行的仿真表明了所提出的解混策略用于分析高光谱图像的准确性。
