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An Efficient and Flexible Statistical Model Based on Generalized Gamma Distribution for Amplitude SAR Images

机译：基于广义伽马分布的振幅SAR图像高效灵活统计模型

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摘要

In the context of synthetic aperture radar (SAR) image processing and applications, the precise modeling of statistical knowledge is a crucial problem. In this paper, an efficient and flexible statistical model, called generalized Gamma Rayleigh $(hbox{G}Gammahbox{R})$ distribution, for amplitude SAR images is proposed by assuming a two-sided generalized Gamma distribution for the real and imaginary parts of the complex SAR backscattered signal. It is shown that the Rayleigh and recently proposed generalized Gaussian Rayleigh distributions can be regarded as special cases of $hbox{G}Gammahbox{R}$ distribution. Considering that the probability density function estimation problem is formulated as a parameter estimation one for the parametric statistical analysis of SAR images, a two-stage estimator based on second-kind cumulants is derived for the parameters of $hbox{G}Gammahbox{R}$ distribution. Furthermore, experimental results on several actual SAR images are given to demonstrate the validity and flexibility of the proposed model.
机译：在合成孔径雷达（SAR）图像处理和应用的背景下，统计知识的精确建模是一个关键问题。在本文中，通过假设实部和虚部的双面广义伽玛分布，提出了一种有效且灵活的统计模型，称为振幅SAR图像的广义伽玛瑞利$（hbox {G} Gammahbox {R}）$分布SAR背向散射信号。结果表明，瑞利分布和最近提出的广义高斯瑞利分布可以看作是$ hbox {G} Gammahbox {R} $分布的特例。考虑到将概率密度函数估计问题作为一种参数估计方法，用于SAR图像的参数统计分析，针对$ hbox {G} Gammahbox {R}的参数推导了基于第二种累积量的两阶段估计器。 $分布。此外，在几个实际SAR图像上给出的实验结果证明了所提模型的有效性和灵活性。

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