Anomaly Detection (AD) in remotely sensed airborne hyperspectral images has been proven valuable in many applications. Within the AD approach that defines the spectral anomalies with respect to a statistical model for the background, reliable background PDF estimation is essential to a successful outcome. This paper proposes a new Bayesian strategy for learning a non-Gaussian mixture model for the background PDF based on elliptically contoured distributions. The resulting estimated background PDF is then used to detect spectral anomalies, characterized by a low probability of occurrence with respect to the global background, through the Generalized Likelihood Ratio Test (GLRT). Real hyperspectral imagery is used for experimental evaluation of the proposed strategy.

A spectral anomaly detector in hyperspectral images based on a non-Gaussian mixture model

MATTEOLI, STEFANIA;DIANI, MARCO;CORSINI, GIOVANNI;
2010

Abstract

Anomaly Detection (AD) in remotely sensed airborne hyperspectral images has been proven valuable in many applications. Within the AD approach that defines the spectral anomalies with respect to a statistical model for the background, reliable background PDF estimation is essential to a successful outcome. This paper proposes a new Bayesian strategy for learning a non-Gaussian mixture model for the background PDF based on elliptically contoured distributions. The resulting estimated background PDF is then used to detect spectral anomalies, characterized by a low probability of occurrence with respect to the global background, through the Generalized Likelihood Ratio Test (GLRT). Real hyperspectral imagery is used for experimental evaluation of the proposed strategy.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11568/139900
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