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Parametric and nonparametric tests for speckled imagery

机译：斑点图像的参数和非参数测试

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摘要

Synthetic aperture radar (SAR) has a pivotal role as a remote imaging method. Obtained by means of coherent illumination, SAR images are contaminated with speckle noise. The statistical modeling of such contamination is well described according to the multiplicative model and its implied (fancyscript{G}^0) distribution. The understanding of SAR imagery and scene element identification is an important objective in the field. In particular, reliable image contrast tools are sought. Aiming the proposition of new tools for evaluating SAR image contrast, we investigated new methods based on stochastic divergence. We propose several divergence measures specifically tailored for (fancyscript{G}^0) distributed data. We also introduce a nonparametric approach based on the Kolmogorov–Smirnov distance for (fancyscript{G}^0) data. We devised and assessed tests based on such measures, and their performances were quantified according to their test sizes and powers. Using Monte Carlo simulation, we present a robustness analysis of test statistics and of maximum likelihood estimators for several degrees of innovative contamination. It was identified that the proposed tests based on triangular and arithmetic-geometric measures outperformed the Kolmogorov–Smirnov methodology.
机译：合成孔径雷达（SAR）作为远程成像方法具有举足轻重的作用。通过相干照明获得的SAR图像被斑点噪声污染。根据乘法模型及其隐含（fancyscript {G} ^ 0）分布，很好地描述了这种污染的统计模型。对SAR图像和场景元素识别的理解是该领域的重要目标。特别地，寻求可靠的图像对比工具。为了提出评估SAR图像对比度的新工具，我们研究了基于随机散度的新方法。我们提出了几种针对（fancyscript {G} ^ 0）分布式数据量身定制的差异度量。我们还针对（fancyscript {G} ^ 0）数据引入了基于Kolmogorov–Smirnov距离的非参数方法。我们基于此类度量设计和评估了测试，并且根据其测试规模和功能对其性能进行了量化。使用蒙特卡洛模拟，我们针对几种创新污染程度提供了测试统计量和最大似然估计器的鲁棒性分析。可以确定，基于三角形和算术几何度量的拟议测试优于Kolmogorov-Smirnov方法。

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