With the development of imaging spectrometer

the spectral resolution and space resolution have been enhanced dramatically which makes a challenge to hyperspectral unmixing processing. So a new distributedhybrid parallel model has been proposed to accelerate hyperspectral unmixing processing. In order to reduce the computational complexity of endmember extraction algorithm

the original serial method has been redesigned for parallel computation and a fast implementation of improved method has been proposed based on partitioned determinant operations. At the same time

the Jama and JCuda components have been used to accelerate the computation in distributed cluster environment.Result The proposed distributed hybrid parallel method plays a large role in accelerating hyperspectral unmixing based on maximum simplex volume algorithm. The improved MapReduce model method is near ten times more rapid than the original method for the hyperspectral image which size is 400×400×224.And the more computational load

the more speed up.Conclusion In this paper

the proposed distributedhybrid parallel method can increase the hyperspectral unmixing processing speed dramatically. At the same time

the partitioned determinant solving method can reduce the complexity of MSVA algorithm.The experimental results indicate that the proposed method can achieve great speedup to the algorithms which have characters of parallel executive tasks、lower data transmission between main node and sub nodes and massive calculations.