Multi-GPU Implementation of the Minimum Volume Simplex Analysis Algorithm for Hyperspectral Unmixing

Spectral unmixing is an important task in remotely sensed hyperspectral data exploitation. The linear mixture model has beenwidely used to unmixhyperspectral images by identifying a set of pure spectral signatures, called endmembers, and estimating their respective abundances in each pixel of the scene. Several algorithms have been proposed in the recent literature to automati-cally identify endmembers, even if the original hyperspectral scene does not contain any pure signatures. A popular strategy for end-member identification in highly mixed hyperspectral scenes has been theminimum volume simplex analysis (MVSA), known to be a computationally very expensive algorithm. This algorithm calcu-lates the minimum volume enclosing simplex, as opposed to other algorithms that perform maximum simplex volume analysis (MSVA). The high computational complexity of MVSA, together with its very high memory requirements, has limited its adoption i