Skip to Main Content
This letter extends the idea of regularization to spectral matched filters. It incorporates a quadratic penalization term in the design of spectral matched filters in order to restrict the possible matched filters (models) to a subset which are more stable and have better performance than the non-regularized adaptive spectral matched filters. The effect of regularization depends on the form of the regularization term and the amount of regularization which is controlled by a parameter so-called the regularization coefficient. In this letter, the sum-of-squares of the filter coefficients is used as the regularization term, and different values for the regularization coefficient are tested. A Bayesian-based derivation of the regularized matched filter is also described which provides a procedure for choosing the regularization coefficient. Experimental results for detecting targets in hyperspectral imagery are presented for regularized and non-regularized spectral matched filters.