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Classification for Polarimetric SAR Images Based on Subaperture Decomposition

机译:基于子孔径分解的极化SAR图像分类

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In this paper,a novel method,which combines subaperture decomposition with H/a/Wishart classifier,is introduced to classify polarimetric synthetic radar (SAR) images.We use H/a plane to initially classify the full-resolution polarimetric SAR image.The initial classification map defines training sets for classification based on coherency matrices of subapertures and wishart distribution.The classified results are then used to define training sets for the next iteration.The convergence of this kind of iteration is much faster than using full-resolution images.And the interpretation of this method is more easily.Results show that the method provides a significant performance improvement with respect to the approaches based on full-resolution images.
机译:本文介绍了一种结合子孔径分解和H / a / Wishart分类器的新颖方法对极化合成雷达(SAR)图像进行分类。我们使用H / a平面初步对全分辨率极化SAR图像进行分类。初始分类图根据子孔径的关联矩阵和wishart分布定义用于分类的训练集,然后将分类结果用于定义下一次迭代的训练集,这种迭代的收敛速度比使用全分辨率图像快得多。结果表明,与基于全分辨率图像的方法相比,该方法具有明显的性能改进。

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