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Regularized Spectral Matched Filter for Target Recognition in Hyperspectral Imagery

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1 Author(s)
Nasrabadi, N.M. ; U.S. Army Res. Lab., Adelphi

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.

Published in:

Signal Processing Letters, IEEE  (Volume:15 )