Anomaly detection for replacement model in hyperspectral imaging

Francois Vincent (1) , Olivier Besson (2) , Stefania Matteoli (3)

1 DEOS - Département Electronique, Optronique et Signal
2 ISAE-SUPAERO - Institut Supérieur de l'Aéronautique et de l'Espace
3 IEIIT - Istituto di Elettronica e di Ingegneria dell'Informazione e delle Telecomunicazioni

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.

Mots clés: Hyperspectral imagery; Replacement model; GLRT; Anomaly detection

Domaines: Traitement du signal et de l'image [eess.SP]

Francois Vincent, Olivier Besson, Stefania Matteoli. Anomaly detection for replacement model in hyperspectral imaging. Signal Processing, 2021, 185, pp.108079. ⟨10.1016/j.sigpro.2021.108079⟩. ⟨hal-03326706⟩