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Unsupervised Classification Method for Polarimetric Synthetic Aperture Radar Imagery Based on Yamaguchi Four-Component Decomposition Model

机译:基于山口四分量分解模型的极化合成孔径雷达图像无监督分类方法

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For improving the accuracy of unsupervised classification based on scattering models, the four-component Yamaguchi model is introduced, which is an improved version of the best-known three-component Freeman model. Therewith, the four-component model is combined with the Wishart distance model. The new proposed algorithm of clustering is rolled out thereafter and the procedure of this new method is listed. In experiments, seven areas of various homogeneities are singled out from the Flevoland sample image in AIRSAR dataset. Qualitative and quantitative experiments are performed for a comparative study. It can be easily seen that the resolution and details are remarkably upgraded by the new proposed method. The accuracy of classification in homogeneous areas has also increased significantly by adopting the new iterative algorithm.
机译:为了提高基于散射模型的无监督分类的准确性,引入了四分量山口模型,它是最著名的三分量Freeman模型的改进版本。因此,将四分量模型与Wishart距离模型结合在一起。此后推出了新的聚类算法,并列出了该新方法的过程。在实验中,从AIRSAR数据集中的Flevoland样本图像中选出了七个同质性不同的区域。进行定性和定量实验以进行比较研究。可以很容易地看出,新提出的方法显着提高了分辨率和细节。通过采用新的迭代算法,均匀区域中分类的准确性也大大提高了。

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