Estimation of hyperspectral covariance matrices

Estimation of covariance matrices is a fundamental step in hyperspectral remote sensing where most detection algorithms make use of the covariance matrix in whitening procedures. We present a simple method to improve the estimation of the eigenvalues of a sample covariance matrix. The method achieves two objectives simultaneously: improved estimation of eigenvalues and improved condition number (regularization). Our method is based on the Marcenko-Pastur law, theory of eigenvalue bounds, and energy conservation.