Blind Separation of Noisy Multivariate Data Using Second-Order Statistics: Remote-Sensing Applications

@article{Herring2009BlindSO,
  title={Blind Separation of Noisy Multivariate Data Using Second-Order Statistics: Remote-Sensing Applications},
  author={Keith T. Herring and Amy V. Mueller and David H. Staelin},
  journal={IEEE Transactions on Geoscience and Remote Sensing},
  year={2009},
  volume={47},
  pages={3406-3415}
}
In this paper a second-order method for blind source separation of noisy instantaneous linear mixtures is presented for the case where the signal order k is unknown. Its performance advantages are illustrated by simulations and by application to Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) multichannel visible/infrared data. The model assumes that m mixtures x of dimension n are observed, where x = Ap + Gw, and the underlying signal vector p has k < n/3 independent unit-variance… CONTINUE READING

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