SPARSE DISTRIBUTED HYPERSPECTRAL UNMIXING

Blind hyperspectral unmixing is the task of jointly estimating the spectral signatures of material in a hyperspectral images and their abundances at each pixel. The size of hyperspectral images are usually very large, which may raise difficulties for classical optimization algorithms, due to limited memory of the hardware used. One solution to this problem is to consider distributed algorithms. In this paper, we develop a distributed sparse hyperspectral unmixing algorithm using the alternating direction method of multipliers (ADMM) algorithm and l(1) sparse regularization. Each sub-problem does not need to have access to the whole hyperspectral image. The algorithm is evaluated using a very large real hyperspectral image.