Generalized gamma distribution with MoLC estimation for statistical modeling of SAR images

Although many theoretical and empirical models have been developed to characterize the statistics of SAR images in the literature, they are generally dedicated to the SAR images with certain types of scenes, or cannot provide analytical expression for the probability density function (PDF). In this...

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Bibliographic Details
Main Authors: Heng-Chao Li, Wen Hong, Yi-Rong Wu
Format: Conference Proceeding
Language:English
Subjects:
Amplitude estimation
Image analysis
Laboratories
Layout
Nakagami distribution
Parameter estimation
Probability density function
Radar scattering
Rayleigh scattering
Statistical distributions
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Summary:Although many theoretical and empirical models have been developed to characterize the statistics of SAR images in the literature, they are generally dedicated to the SAR images with certain types of scenes, or cannot provide analytical expression for the probability density function (PDF). In this paper, we propose a new empirical statistical model, called generalized Gamma distribution (GGammaD), for the statistical modeling of SAR images. The GGammaD forms a large variety of alternative distributions, and is flexible to model the SAR images covering different kinds of surfaces in amplitude and intensity formats. Moreover, the method of log-cumulants (MoLC) based on Mellin transform is derived for parameter estimation of GGammaD.Experimental results on two real SAR images are given to demonstrate the validity of our proposed GGammaD.
DOI:10.1109/APSAR.2007.4418665