Robust hyperspectral signal subspace identification in the presence of signal dependent noise

A new technique for signal subspace identification in hyperspectral images is presented. It estimates the signal subspace by including both the abundant and the rare signal components. The method is derived by assuming a non stationary model for the noise affecting the data. It is particularly suitable for the processing of images acquired by new generation sensors where, due to the improved sensitivity of the electronic components, noise includes a signal dependent term. Results obtained by applying the new algorithm to simulated and real data are presented and discussed.

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