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Modeling high-resolution synthetic aperture radar images with heavy-tailed distributions

Han Chong-Zhao Sun Zeng-Guo

Modeling high-resolution synthetic aperture radar images with heavy-tailed distributions

Han Chong-Zhao, Sun Zeng-Guo
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  • Received Date:  04 May 2009
  • Accepted Date:  09 June 2009
  • Published Online:  15 February 2010

Modeling high-resolution synthetic aperture radar images with heavy-tailed distributions

  • 1. (1)西安交通大学电子与信息工程学院,西安 710049; (2)西安交通大学机械制造系统工程国家重点实验室,西安 710049

Abstract: Statistical distributions of synthetic aperture radar (SAR) images based on central limit theorem cannot reflect the statistical characteristics of sharp peak and heavy tail of high-resolution SAR images. By using the generalized central limit theorem, the heavy-tailed distributions (heavy-tailed Rayleigh distribution for amplitude image and heavy-tailed exponential distribution for intensity image) are obtained from the symmetric stable distributions of real and imaginary parts of echoes. Taking the heavy-tailed Rayleigh distribution as an example, the algebraic tails of heavy-tailed distributions are explained as well as the statistical properties of sharp peak and heavy tail. In order to model the high-resolution SAR images with the heavy-tailed distributions, based on second-kind statistical, Characteristics the log-cumulant estimator is proposed to efficiently estimate the parameters of the heavy-tailed distributions. Modeling experiments on real SAR images demonstrate that the heavy-tailed distributions based on the generalized central limit theorem can accurately describe the sharp-peaked and heavy-tailed statistical characteristics of high-resolution SAR images.

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