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Robust Estimation of Roughness Parameter in SAR Amplitude Images

机译:SAR幅值图像中粗糙度参数的鲁棒估计

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摘要

The precise knowledge of the statistical properties of synthetic aperture radar (SAR) data plays a central role in image processing and understanding. These properties can be used for discriminating types of land uses and to develop specialized filters for speckle noise reduction, among other applications. In this work we assume the distribution G_A~0 the universal model for multilook amplitude SAR images under the multiplicative model. We study some important properties of this distribution and some classical estimators for its parameters, such as Maximum Likelihood (ML) estimators, but they can be highly influenced by small percentages of 'outliers', i.e., observations that do not fully obey the basic assumptions. Hence, it is important to find Robust Estimators. One of the best known classes of robust techniques is that of M estimators, which are an extension of the ML estimation method. We compare those estimation procedures by means of a Monte Carlo experiment.
机译:合成孔径雷达(SAR)数据的统计特性的精确知识在图像处理和理解中起着核心作用。这些特性可用于区分土地用途的类型,并开发用于减少斑点噪声的专用滤波器,以及其他应用。在这项工作中,我们假设分布模型G_A〜0是乘法模型下多视振幅SAR图像的通用模型。我们研究了此分布的一些重要属性以及一些经典的估计量参数,例如最大似然(ML)估计量,但是它们可能会受到很小百分比的“离群值”的强烈影响,即观察结果不能完全遵循基本假设。因此,找到鲁棒的估计器很重要。鲁棒技术中最著名的一类是M估计器,它是ML估计方法的扩展。我们通过蒙特卡洛实验比较了这些估算程序。

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