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Anomaly detection for replacement model in hyperspectral imaging

Vincent, Francois and Besson, Olivier and Matteoli, Stefania Anomaly detection for replacement model in hyperspectral imaging. (2021) Signal Processing, 185. 108079. ISSN 0165-1684

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Official URL: https://doi.org/10.1016/j.sigpro.2021.108079

Abstract

In this paper we consider Anomaly Detection in the hyperspectral context, and we extend the popular RX detector, initially designed under the standard additive model, to the replacement model case. Indeed, in this more realistic framework, the target, if present, is supposed to replace a part of the background. We show how to estimate this background power variation to improve the standard RX scheme. The obtained Replacement RX (RRX) is shown to be closed-form and outperforms the standard RX on a real data benchmark experiment.

Item Type:Article
Audience (journal):International peer-reviewed journal
Uncontrolled Keywords:
Institution:Other partners > Consiglio Nazionale delle Ricerche - CNR (ITALY)
Université de Toulouse > Institut Supérieur de l'Aéronautique et de l'Espace - ISAE-SUPAERO (FRANCE)
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Deposited On:11 Jun 2021 17:43

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