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An approach to parameter optimization for the low-rank matrix recovery methodin hyperspectral imaging is discussed. We formulate an optimization problemwith respect to the initial parameters of the low-rank matrix recovery method.The performance for different parameter settings is compared in terms ofcomputational times and memory. The results are evaluated by computing the peaksignal-to-noise ratio as a quantitative measure. The potential improvement ofthe performance of the noise reduction method is discussed when optimizing thechoice of the initial values. The optimization method is tested on standard andopenly available hyperspectral data sets including Indian Pines, Pavia Centre,and Pavia University.

Parameter optimization for low-rank matrix recovery in hyperspectral imaging