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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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https://hal-supelec.archives-ouvertes.fr/hal-00781727
Contributor : Anne-Hélène Picot <>
Submitted on : Friday, June 4, 2021 - 8:51:59 PM
Last modification on : Tuesday, June 15, 2021 - 4:18:44 PM

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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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