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Spectra collected by hyperspectral sensors over samples of the same material are not deterministic quantities. Their inherent spectral variability can be accounted for by making use of suitable statistical models. Within this framework, the Gaussian Mixture Model (GMM) is one of the most widely adopted models for modeling hyperspectral data. Unfortunately,(More)
Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Abstract – We propose a local(More)
In recent years, hyperspectral Anomaly Detection (AD) has become a challenging area due to the rich information content provided by hyperspectral sensors about the spectral characteristics of the observed materials. Within this framework, since no prior knowledge about the target is assumed, pixels that have different spectral content from typical(More)
This paper proposes a fully unsupervised anomaly detection strategy in hyperspectral imagery based on mixture learning. Anomaly detection is conducted by adopting a Gaussian Mixture Model (GMM) to describe the statistics of the background in hyperspectral data. One of the key tasks in the application of mixture models is the specification in advance of the(More)
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(More)
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