Hyperspectral data modelling by nonGaussian statistical distributions

Author

Acito, N. ; Corsini, G. ; Diani, M.

Author_Institution

Dipt. di Ingegneria dell´´Informazione, Pisa Univ., Italy

Volume

2

fYear

2004

fDate

20-24 Sept. 2004

Firstpage

968

Abstract

In this manuscript we investigate on the statistical modeling of hyperspectral data. Accurately modeling real data is of paramount importance in the design of optimal classification or detection strategies and in evaluating their performances. In the work three nonGaussian models are considered and their capability in characterizing the statistical behavior of real data is discussed with reference to a data set acquired by the multispectral infrared and visible imaging spectrometer (MIVIS) sensor.

Keywords

data analysis; geophysical signal processing; image classification; image sensors; statistical analysis; MIVIS sensor; Multispectral Infrared and Visible Imaging Spectrometer; hyperspectral/real data modelling; nonGaussian statistical distribution; optimal classification/detection strategy; Algorithm design and analysis; Atmospheric modeling; Detection algorithms; Hyperspectral sensors; Infrared image sensors; Infrared spectra; Optical imaging; Sensor phenomena and characterization; Spectroscopy; Statistical distributions;

fLanguage

English

Publisher

ieee

Conference_Titel

Geoscience and Remote Sensing Symposium, 2004. IGARSS '04. Proceedings. 2004 IEEE International

Print_ISBN

0-7803-8742-2

Type

conf

DOI

10.1109/IGARSS.2004.1368570

Filename

1368570