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Full polarimetric SAR classification based on Yamaguchi decomposition model and scattering parameters

机译:基于Yamaguchi分解模型和散射参数的全极化SAR分类

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The full polarimetric information of the target from polarized Synthetic Aperture Radar (POLSAR) enables us to implement recognition and classification of remote sensing images more effectively. Based on the analysis of typical polarized target decomposition and classification, the issue proposes a new scheme for iterative classification of polarimetric SAR image, which blends the outcomes of Yamaguchi decomposition and H/α decomposition. This technique extracts four decomposition coefficients of four scattering mechanism components through Yamaguchi decomposition, the scattering entropy and angle through H/α decomposition first; then the initial classification of the POLSAR images is done by the combination of the 6 parameters mentioned above. The final result is obtained by iterative classification due to coherence scattering matrix following wishart distribution. The effectiveness of this method plus less computation required is demonstrated by the experimental results of polarimetric SAR data.
机译:来自极化合成孔径雷达(POLSAR)的目标的完整极化信息使我们能够更有效地实现遥感图像的识别和分类。在分析典型极化目标分解和分类的基础上,提出了一种新的极化SAR图像迭代分类方案,该方案融合了Yaguguchi分解和H /α分解的结果。该技术通过山口分解提取四个散射机制分量的四个分解系数,首先通过H /α分解提取散射熵和角度。然后通过上述6个参数的组合完成POLSAR图像的初始分类。最终结果是通过迭代分类得到的,这归因于遵循ishartart分布的相干散射矩阵。极化SAR数据的实验结果证明了该方法的有效性以及所需的较少计算量。

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