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Non parametric partitioning of SAR images

机译:SAR图像的非参数分割

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We describe and analyse a generalization of a parametric segmentation technique adapted to Gamma distributed SAR images to a simple non parametric noise model. The partition is obtained by minimizing the stochastic complexity of a quantized version on Q levels of the SAR image and lead to a criterion without parameters to be tuned by the user. We analyse the reliability of the proposed approach on synthetic images. The quality of the obtained partition will be studied for different possible strategies. In particular, one will discuss the reliability of the proposed optimization procedure. Finally, we will precisely study the performance of the proposed approach in comparison with the statistical parametric technique adapted to Gamma noise. These studies will be led by analyzing the number of misclassified pixels, the standard Hausdorff distance and the number of estimated regions.
机译:我们描述并分析了适用于Gamma分布SAR图像的参数分割技术的概括,以简化为简单的非参数噪声模型。通过最小化量化图像在SAR图像Q级别上的随机复杂度来获得分区,从而得出无需用户调整参数的标准。我们分析了合成图像上提出的方法的可靠性。将针对不同可能的策略研究获得的分区的质量。特别地,将讨论所提出的优化程序的可靠性。最后,我们将与适用于伽玛噪声的统计参数技术相比,精确研究所提出方法的性能。这些研究将通过分析误分类像素的数量,标准Hausdorff距离和估计区域的数量来进行。

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