Geoinformatics & Geostatistics: An OverviewISSN: 2327-4581

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Research Article, Geoinfor Geostat An Overview S Vol: 0 Issue: 1

Characterization of the Texture of Digital Images by Variography Approach: An Application to the Classification of SAR Images

Janvier Fotsing1*, Emmanuel Tonye1, Bernard Essimbi Zobo2, Mahaman Bachir Saley3, Fernand Koffi Kouame3 and Jean-Paul Rudant4
1Laboratory of Electronics and Signal Processing, Department Electrical Engineering and Telecommunications, National Advanced School of Engineering, University of Yaound eI, Cameroon
2Electronics Laboratory, Department of Physics, Faculty of Sciences, University of Yaound eI, Cameroon
3Centre universitaire de recherche et d’application en teledetection (CURAT),Laboratoire associé efrancophone (LAF n° 401), UFR des Sciences de la Terre et des Ressources Minières, Université ede Cocody, Côte d’Ivoire
4Laboratoire des Geomateriaux, Institut Francilien des Geosciences, 5Boulevard Descartes, Champs-Sur-Marne, Université ede Marne-La-Vallee,France
Corresponding author : Janvier Fotsing
Laboratory of Electronics and Signal Processing, Department Electrical Engineering and Telecommunications, National Advanced School of Engineering, University of Yaounde I, Cameroon
Email: [email protected]
Received: February 22, 2013 Accepted: May 15, 2013 Published: May 24, 2013
Citation: Fotsing J, Tonye E, Zobo BE, Saley MB, Kouame FK, et al. (2013) Characterization of the Texture of Digital Images by Variography Approach: An Application to the Classification of SAR Images. Geoinfor Geostat: An Overview S1. doi:10.4172/2327-4581.S1-006

Abstract

Characterization of the Texture of Digital Images by Variography Approach: An Application to the Classification of SAR Images

This paper presents a method of texture modelling using geostatistic theory. From variographics abacus and variogram proprieties, fractal and exponential models have been used to characterize Brodatz textures. In this approach, a texture is modelled by a vector called feature vector whose components are parameters characterizing the experimental variogram, notably the “Slope”, the “Range”, the “Landing” and the “Fractal Dimension (FD)”.

Keywords: SAR image; Texture; Variogram; Feature vector; Supervised classification

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