中文摘要：

光谱解混是高光谱图像处理的重要技术之一。传统线性光谱混合模型（LSMM）解混方法采用迭代求解方式，由于其中含有非负和归一化约束条件，复杂度较高。为此，首先通过参量替换去除非负和归一化约束条件，使得光谱解混的过程成为以最小均方误差为适应度函数的极值寻优问题；进而，应用田口（Taguchi）优化算法进行迭代寻优，并利用高光谱数据的光谱维度很高这一特点和统计学原理对算法的初始化方法进行了研究。人工合成的数据实验和真实高光谱数据实验一致表明，本文提出的方法较传统方法解混精度和解混效率均获提高。

英文摘要：

Spectral unmixing is one of the important techniques for hyperspectral image processing. Traditional spectral unmixing method based on linear spectral mixing modeling （LSMM） with non-negative and sum-to-one constraints is solved in terms of iteration manner, suffering a heavy computational bur den. In this case, the parameter substitution is introduced to remove the no,negative and sum-to-one constraints. So the process of spectral unmixing is resorted to an optimization problem for finding the extreme value of minimum mean square error based fitness function. Then the Taguchi optimization algorithm is used to solve the optimization problem iteratively. At the same time, using the features of high spectral dimension for hyperspectral data and the principles of statistics, the initialization method of the algorithm is researched. Experiments implemented on synthesized data and truth hyperspectral data show that the proposed method gives higher unmixing efficiency and unmixing accuracy than the traditional LSMM method.