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Conference Papers Year : 2012

A class of Robust Estimates for Detection in Hyperspectral Images using Elliptical Distributions Backgroung

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.
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Dates and versions

hal-00781727 , version 1 (04-06-2021)

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Joana Frontera-Pons, Mélanie Mahot, Jean-Philippe Ovarlez, Frédéric Pascal, Sze Kim Pang, et al.. A class of Robust Estimates for Detection in Hyperspectral Images using Elliptical Distributions Backgroung. IGARSS 2012 - IEEE International Geoscience and Remote Sensing Symposium, Jul 2012, Munich, Germany. pp.4166 - 4169, ⟨10.1109/IGARSS.2012.6350938⟩. ⟨hal-00781727⟩
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