基于全变分正则最大后验估计的高光谱图像亚像元快速定位方法

针对高光谱亚像元定位应用中光谱解混这一病态问题的求解，改进了结合空间分布先验全变分（TV）的最大后验估计（MAP）光谱解混模型，以保证算法的可扩展性和解的唯一性.同时，针对TV先验固有的非线性特性导致的求解过程繁琐的问题，提出了一种快速求解算法，将原始复杂的非线性运算转化成几步较简单的有闭合解的运算，对这些子问题结合运用快速迭代收缩阈值算法（FISTA）和分裂Bregman算法来分别求解.结果表明，提出的新方法保持了与传统梯度下降方法相一致的定位精度，但将迭代速度提高了10倍以上，具有更高的运算效率. 

A Fast Method for Hyperspectral Image Subpixel Mapping Based on Maximum a Posteriori and Total Variation Estimation

To solve the ill-posed pr oblem of spectral unmixing in hyperspectral subpixel mapping applications, the maximum a posteriori estimation (MAP) spectral unmixing model combined with spatial distribution prior total variation (TV) was improved to ensure the scalability of the algorithm and the uniqueness of the solution. At the same time, in order to solve the cumbersome problem caused by the inherent nonlinear characteristics of TV prior, a fast algorithm was proposed to transform the original complex nonlinear operation into several simple operations with closed solutions. To solve the sub-problem respectively, a fast iterative shrinkage threshold algorithm (FISTA) and the split Bregman algorithm were utilized. The results show that the proposed new method can maintain the consistent mapping accuracy of the traditional gradient descent method, and can increase the iteration speed by more than 10 times, providing higher computational efficiency.