Paper Info
Title
A spectral anomaly detector in hyperspectral images based on a non-Gaussian mixture model
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.
Year
DOI
Venue
2010
10.1109/WHISPERS.2010.5594901
WHISPERS
Keywords
Field
DocType
bayes methods,feature extraction,geophysical image processing,bayesian learning strategy,background pdf estimation,generalized likelihood ratio test,hyperspectral images,non gaussian mixture model,spectral anomaly detection,bayesian approach,hyperspectral imagery,anomaly detection,model selection,non-gaussian mixture model,hyperspectral imaging,gaussian mixture model,pixel,bayesian methods,mathematical model,materials,remote sensing,statistical model
Anomaly detection,Likelihood-ratio test,Pattern recognition,Computer science,Model selection,Feature extraction,Hyperspectral imaging,Artificial intelligence,Statistical model,Mixture model,Bayesian probability
Conference
ISBN
Citations 
PageRank 
978-1-4244-8907-7
1
0.36
References 
Authors
4
5
Name
Order
Citations
PageRank
Tiziana Veracini1393.61
Stefania Matteoli215218.05
Marco Diani326130.99
Giovanni Corsini429940.26
sergio ugo de ceglie510.36