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

Joana Frontera-Pons (1) , Mélanie Mahot (1) , Jean-Philippe Ovarlez (1, 2) , Frédéric Pascal (1) , Sze Kim Pang (3) , Jocelyn Chanussot (4)

1 SONDRA - Sondra, CentraleSupélec, Université Paris-Saclay (COmUE)
2 ONERA - The French Aerospace Lab [Palaiseau]
3 DSO National Laboratories
4 GIPSA-SIGMAPHY - GIPSA - Signal Images Physique

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.

Domains: Signal and Image processing; Signal and Image Processing

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⟩