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  • Research Article
  • Open Access

Statistical Analysis of Hyper-Spectral Data: A Non-Gaussian Approach

EURASIP Journal on Advances in Signal Processing20062007:027673

Received: 5 June 2006

Accepted: 24 October 2006

Published: 17 December 2006


We investigate the statistical modeling of hyper-spectral data. The accurate modeling of experimental data is critical in target detection and classification applications. In fact, having a statistical model that is capable of properly describing data variability leads to the derivation of the best decision strategies together with a reliable assessment of algorithm performance. Most existing classification and target detection algorithms are based on the multivariate Gaussian model which, in many cases, deviates from the true statistical behavior of hyper-spectral data. This motivated us to investigate the capability of non-Gaussian models to represent data variability in each background class. In particular, we refer to models based on elliptically contoured (EC) distributions. We consider multivariate EC-t distribution and two distinct mixture models based on EC distributions. We describe the methodology adopted for the statistical analysis and we propose a technique to automatically estimate the unknown parameters of statistical models. Finally, we discuss the results obtained by analyzing data gathered by the multispectral infrared and visible imaging spectrometer (MIVIS) sensor.


Mixture ModelVisible ImagingTarget DetectionGaussian ModelReliable Assessment


Authors’ Affiliations

Dipartimento di Ingegneria dell'Informazione, Università di Pisa, Pisa, Italy


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© N. Acito et al. 2007

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.