Paper Info
Title
A class of robust estimates for detection in hyperspectral images using elliptical distributions background.
Abstract
When dealing with impulsive background echoes, Gaussian model is no longer pertinent. We study in this paper the class of elliptically contoured (EC) distributions. They provide a multivariate location-scatter family of distributions that primarily serve as long tailed alternatives to the multivariate normal model. They are proven to represent a more accurate characterization of HSI data than models based on the multivariate Gaussian assumption. For data in Rk, robust proposals for the sample covariance estimate are the M-estimators. We have also analyzed the performance of an adaptive non-Gaussian detector built with these improved estimators. Constant False Alarm Rate (CFAR) is pursued to allow the detector independence of nuisance parameters and false alarm regulation.
Year
DOI
Venue
2012
10.1109/IGARSS.2012.6350938
IGARSS
Keywords
Field
DocType
Gaussian processes,estimation theory,geophysical image processing,CFAR,EC distribution,Gaussian model,HSI data,M-estimators,adaptive nonGaussian detector,constant false alarm rate,elliptical distribution background,elliptically contoured distribution,hyperspectral images,multivariate Gaussian assumption,multivariate location-scatter family,multivariate normal model,nuisance parameters,robust estimation,M-estimators,elliptical distributions,hypespectral imaging,target detection
False alarm,Pattern recognition,Multivariate statistics,Computer science,Multivariate normal distribution,Gaussian,Artificial intelligence,Gaussian process,Constant false alarm rate,Estimation theory,Estimator
Conference
ISSN
Citations 
PageRank 
2153-6996
5
0.52
References 
Authors
7
6
Name
Order
Citations
PageRank
Joana Frontera-Pons1254.07
Melanie Mahot2272.21
Jean Philippe Ovarlez319025.11
Frédéric Pascal417523.99
Sze Kim Pang5646.65
Jocelyn Chanussot64145272.11