Modeling the statistics of high resolution SAR images

Abstract : In the context of remotely sensed data analysis, a crucial problem is represented by the need to develop accurate models for the statistics of pixel intensities. In this work, we develop a parametric finite mixture model for modelling the statistics of intensities in high resolution Synthetic Aperture Radar (SAR) images. Along with the models we design an efficient parameter estimation scheme by integrating the Stochastic Expectation Maximization scheme and the Method of log-cumulants with an automatic technique to select, for each mixture component, an optimal parametric model taken from a predefined dictionary of parametric probability density functions (pdf). In particular, the proposed dictionary consists of eight most efficient state-of-the-art SAR-specific pdfs: Nakagami, log-normal, generalized Gaussian Rayleigh, Heavy-tailed Rayleigh, Weibull, K-root, Fisher and generalized Gamma. The experiment results with a set of several real SAR (COSMO-SkyMed) images demonstrate the high accuracy of the designed algorithm, both from the viewpoint of a visual comparison of the histograms, and from the viewpoint of quantitive measures such as correlation coefficient (always above 99,5%) . We stress, in particular, that the method proves to be effective on all the considered images, remaining accurate for multimodal and highly heterogeneous images.
Type de document :
[Research Report] RR-6722, INRIA. 2008, pp.41
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Contributeur : Vladimir Krylov <>
Soumis le : vendredi 30 janvier 2009 - 20:25:30
Dernière modification le : lundi 5 novembre 2018 - 15:52:01
Document(s) archivé(s) le : mercredi 22 septembre 2010 - 11:31:46


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  • HAL Id : inria-00342681, version 2



Vladimir Krylov, Gabriele Moser, Sebastiano B. Serpico, Josiane Zerubia. Modeling the statistics of high resolution SAR images. [Research Report] RR-6722, INRIA. 2008, pp.41. 〈inria-00342681v2〉



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