An ADMM-based algorithm with minimum dispersion constraint for on-line unmixing of hyperspectral images
Résumé
Pushbroom imaging systems are emerging techniques for real-time acquisition of hyperspectral images. These systems are frequently used in industrial applications to control and sort products on-the-fly. In this paper, the on-line hyperspectral image blind unmixing is addressed. We propose a new on-line method based on Alternating Direction Method of Multipliers approach, particularly well-adapted to pushbroom imaging systems. The proposed algorithm presents faster convergence and lower computational complexity compared to the algorithms based on multiplicative update rules. Because of the generally ill-posed nature of the unmixing problem, we impose a minimum endmembers dispersion constraint to regularize the unmixing problem; this constraint can be interpreted as a convex relaxation of the minimum volume constraint and, therefore, presents interesting optimization properties. Experimental results on synthetic and real data sets demonstrate the effectiveness of our method in terms of rapidity and accuracy.
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