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 burden.In this case,the parameter substitution is introduced to remove the non-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.