Image fusion of radar and optical remote sensing data for land cover classification

Author

Nsaibi, Maher ; Chaabane, Ferdaous

Author_Institution

Unite de Rech. en Imagerie Satellitaire et ses Applic.-URISA, Ecole Super. des Telecommun. de Tunis, Tunis

fYear

2008

Firstpage

1

Lastpage

4

Abstract

The aim of this paper is to propose a new unsupervised land cover classification method based on probabilistic fusion theory. This method combines two different Besag Markovian auto models: a Markovian Gamma auto model that characterizes the radar texture data and a Gaussian Markov Random Field auto model to characterize the optical spectral data. An optimal Markovian neighborhood order is also applied in order to improve the speckle texture modeling.

Keywords

Gaussian processes; Markov processes; geophysical signal processing; image classification; image fusion; image texture; optical images; probability; radar imaging; remote sensing by radar; speckle; spectral analysis; Besag Markovian auto models; Gaussian Markov random field auto model; Markovian Gamma auto model; image fusion; optical remote sensing data; optical spectral data; optimal Markovian neighborhood order; probabilistic fusion theory; radar data; speckle texture modeling; unsupervised land cover classification method; Adaptive optics; Geometrical optics; Image fusion; Laser radar; Optical filters; Optical sensors; Radar imaging; Radar remote sensing; Remote sensing; Speckle; Besag auto models; Markov neighborhood order; component: Land cover classification;

fLanguage

English

Publisher

ieee

Conference_Titel

Information and Communication Technologies: From Theory to Applications, 2008. ICTTA 2008. 3rd International Conference on

Conference_Location

Damascus

Print_ISBN

978-1-4244-1751-3

Electronic_ISBN

978-1-4244-1752-0

Type

conf

DOI

10.1109/ICTTA.2008.4530043

Filename

4530043