Accurate statistical models for hyperspectral imaging (HSI) data distribution are useful for many applications. A family of elliptically contoured distribution (ECD) has been investigated to model the unimodal ground cover classes. In this paper we propose to test the elliptical symmetry of real unimodal HSI clutters which will answer the question whether the family of ECD will provide an appropriate model for HSI data. We emphasize that the elliptical symmetry is an inherent feature shared by all ECDs. It is a prerequisite that real HSI clutters must pass these elliptical symmetry tests, so that the family of ECD can be qualified to model these data accurately
International audienceHyperspectral imaging has continued to be exploited in various fields for its ...
National audienceCo-designed and programmable hyperspectral imagers, based on computational imaging,...
International audienceWhen accounting for heterogeneity and non-Gaussianity of real hyperspectral da...
International audienceWhen dealing with impulsive background echoes, Gaussian model is no longer per...
The detection of actual changes in a pair of images is confounded by the inadvertent but pervasive d...
Although the assumption of elliptical symmetry is quite common in multivariate analysis and widespre...
Characterization of the joint (among wavebands) probability density function (pdf) of hyperspectral ...
When accounting for heterogeneity and non-Gaussianity of real hyperspectral data, elliptical distrib...
peer reviewedThe assumption of elliptical symmetry has an important role in many theoretical develop...
AbstractWe present and study a procedure for testing the null hypothesis of multivariate elliptical ...
AbstractThis paper presents a statistic for testing the hypothesis of elliptical symmetry. The stati...
This paper presents a procedure for testing the hypothesis that the underlying distribution of the d...
Spectra collected by hyperspectral sensors over samples of the same material are not deterministic q...
This article first reviews the definition of elliptically symmetric distributions and discusses iden...
We investigate the statistical modeling of hyper-spectral data. The accurate modeling of experimenta...
International audienceHyperspectral imaging has continued to be exploited in various fields for its ...
National audienceCo-designed and programmable hyperspectral imagers, based on computational imaging,...
International audienceWhen accounting for heterogeneity and non-Gaussianity of real hyperspectral da...
International audienceWhen dealing with impulsive background echoes, Gaussian model is no longer per...
The detection of actual changes in a pair of images is confounded by the inadvertent but pervasive d...
Although the assumption of elliptical symmetry is quite common in multivariate analysis and widespre...
Characterization of the joint (among wavebands) probability density function (pdf) of hyperspectral ...
When accounting for heterogeneity and non-Gaussianity of real hyperspectral data, elliptical distrib...
peer reviewedThe assumption of elliptical symmetry has an important role in many theoretical develop...
AbstractWe present and study a procedure for testing the null hypothesis of multivariate elliptical ...
AbstractThis paper presents a statistic for testing the hypothesis of elliptical symmetry. The stati...
This paper presents a procedure for testing the hypothesis that the underlying distribution of the d...
Spectra collected by hyperspectral sensors over samples of the same material are not deterministic q...
This article first reviews the definition of elliptically symmetric distributions and discusses iden...
We investigate the statistical modeling of hyper-spectral data. The accurate modeling of experimenta...
International audienceHyperspectral imaging has continued to be exploited in various fields for its ...
National audienceCo-designed and programmable hyperspectral imagers, based on computational imaging,...
International audienceWhen accounting for heterogeneity and non-Gaussianity of real hyperspectral da...