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- W2010702969 abstract "This paper describes a new adaptive spectral matched filter and a modified RX-based anomaly detector that incorporates the idea of regularization (shrinkage). The regularization has the effect of restricting 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 is controlled by so called regularization coefficient. In this paper the sum-of-squares of the filter coefficients is used as the regularization term and several different values for the regularization coefficient are tested. A Bayesian-based derivation of the regularized matched filter is also provided. Experimental results for detecting and recognizing targets in hyperspectral imagery are presented for regularized and non-regularized spectral matched filters and RX algorithm." @default.
- W2010702969 created "2016-06-24" @default.
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- W2010702969 date "2008-04-03" @default.
- W2010702969 modified "2023-10-18" @default.
- W2010702969 title "Regularization for spectral matched filter and RX anomaly detector" @default.
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- W2010702969 doi "https://doi.org/10.1117/12.773444" @default.
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