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Unsupervised classification based on H/alpha decomposition and Wishart classifier for compact polarimetric SAR

机译:基于H / alpha分解和Wishart分类器的紧凑偏振SAR非监督分类

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In this paper, an unsupervised classification for compact polarimetry SAR (C-PolSAR) image is proposed by combining the H/α decomposition with the Wishart classifier. Firstly, H/α decomposition method is applied to the compact polarimetry (CP) data. By analyzing the different (H, a) values corresponding to the three compact polarimetry mode: the π/4, CL, and CC modes, we find that only in the CC mode, different scattering targets are distinguished well by (H, α) values. The decomposition results are used as the initial classification. Then the maximum likelihood classifier based on the complex Wishart distribution is adopted to classify the image iteratively. After four iterations, the classification results are much improved, and the classification details can be identified clearly. We use the AirSAR of San Francisco L-band data to illustrate the effectiveness of the proposed classification method.
机译:通过结合H /α分解和Wishart分类器,提出了紧凑型极化SAR(C-PolSAR)图像的无监督分类。首先,将H /α分解方法应用于紧凑型极化(CP)数据。通过分析对应于三种紧凑偏振模式(π/ 4,CL和CC模式)的不同(H,a)值,我们发现只有在CC模式下,不同的散射目标才能被(H,α)很好地区分。价值观。分解结果用作初始分类。然后采用基于复杂Wishart分布的最大似然分类器对图像进行迭代分类。经过四次迭代,分类结果得到了很大的改善,并且可以清楚地识别分类细节。我们使用旧金山的AirSAR L波段数据来说明所提出的分类方法的有效性。

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