Non Zero Mean Adaptive Cosine Estimator and Application to Hyperspectral Imaging
Résumé
The Adaptive Cosine Estimator (ACE) has become a
popular detection scheme in many applications. Similarly to the
majority of detection schemes, it assumes a zero mean noise. In
some domains, such as hyperspectral imaging, this assumption no
longer holds and this algorithm has to be adapted. In this paper
we revisit the use of ACE in a non zero mean context.We consider
the case where the data under test and the training samples differ
from one scaling factor on the mean and one scaling factor on
the covariance matrix. We derive two-step generalized likelihood
ratio tests for both the additive model and the replacement model
and show that the new detectors differ in the way the mean value
is removed. A real data experiment shows that they outperform
the standard version.
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