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Estimating the Polarization Degree of Polarimetric Images using Maximum Likelihood Methods

机译:使用最大似然法估计偏振图像的偏振度

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This paper shows that the joint distribution of polarimetric intensity images is a multivariate gamma distribution in the case of coherent illumination with fully developed speckle. The parameters of this gamma distribution can be estimated according to the maximum likelihood (ML) principle. Different estimators depending on the number of available polarimetric images are studied. These estimators provide different ways of estimating the degree of polarization (DoP) associated to each pixel of the image. A performance comparison with estimators based on methods of moments shows the interest of the ML method for estimating the DoP of polarimetric images.
机译:本文表明,在相干照明且散斑充分发展的情况下,偏振强度图像的联合分布是多元伽马分布。可以根据最大似然(ML)原理估计此伽玛分布的参数。研究了取决于可用偏振图像数量的不同估计量。这些估计器提供了估计与图像的每个像素关联的偏振度(DoP)的不同方法。与基于矩量法的估计器的性能比较显示了ML方法用于估计偏振图像DoP的兴趣。

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