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GA0 distribution model in edge-preserving parameter estimation of SAR images

机译:SAR图像保边缘参数估计的GA0分布模型。

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Abstract: The multiplicative model has been widely used to explain the statistical properties of SAR images. In it, the model for the image Z is a 2D random field, that is regarded as the result of the product of X, the backscatter that depends on the physical characteristics of the sensed area, and Y, the speckle that depends on the number of looks used to generate the image Z. The most famous distribution for SAR images based on the multiplicative model is the K distribution (Jackeman et al). Recently Frery et al. proposed an alternative distribution, the G$+0$/$-A$/($alpha@,$gamma@,n) distribution which models very well extremely heterogenous areas (cities) as well as moderately heterogeneous areas (forest) and homogeneous areas (crop fields). The ground truth at each pixel can be characterized by the statistical parameters $alpha and $gamma@, while n is constant for all of the pixels. The purpose of estimating these parameters for every pixel is twofold: first, it can be used to perform a segmentation process and, second, it can be used for gray level restoration. In this work we follow a Markov random field approach and propose an energy function derived from the statistical model adopted: G$+0$/$- A$/($alpha@,$gamma@,n). Edge-preservation is taken into account implicitly in the energy function.!9
机译:摘要:乘法模型已被广泛用于解释SAR图像的统计特性。其中,图像Z的模型是2D随机场,被视为X的乘积的结果,X的反向散射取决于检测区域的物理特征,而Y的散斑取决于数量用于生成图像Z的外观。基于乘法模型的SAR图像最著名的分布是K分布(Jackeman等人)。最近,Frery等。提出了另一种分布,即G $ + 0 $ / $-A $ /($ alpha @,$ gamma @,n)分布,该分布很好地模拟了非常异类的区域(城市)以及中等异类的区域(森林)和均质区域(作物田)。每个像素的地面真相可以用统计参数$ alpha和$ gamma @来表征,而n对于所有像素都是恒定的。估计每个像素的这些参数有两个目的:首先,它可用于执行分割过程,其次,可用于灰度恢复。在这项工作中,我们遵循马尔可夫随机场方法,并提出了一种从采用的统计模型得出的能量函数:G $ + 0 $ / $-A $ /($ alpha @,$ gamma @,n)。能量函数中隐含地考虑了边缘保留!9

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