Shrinkage estimators for covariance matrices in spectral remote sensing

This talk describes investigations of shrinkage parameter estimates for covariance matrices used in spectral processing of remote sensing imagery, such as for target, anomaly, or change detection. These estimates are derived in the context of cross-validated fiting of Gaussian likelihood models to the non-target background distribution. Here, the utility of these estimates is evaluated for Gaussian and non-Gaussian distributions. An alternative criterion, based on matched-filter detection of "generic" targets, is derived and compared to the estimated likelihood criterion as a way to choose the shrinkage parameter.