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An Eigenvalue-Based Approach for Structure Classification in Polarimetric SAR Images

机译：基于特征值的Polarimetric SAR图像结构分类方法

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摘要

In this letter, we design a novel unsupervised architecture for automatic classification of the dominant polarization in polarimetric SAR images. To this end, we leverage the ideas developed in [1] and suitably exploit them to build a decision logic capable of recognizing the dominant scattering mechanism which characterizes the pixel under test. Specifically, we combine the original data to generate three different sets of reduced-size vectors, which feed dominant eigenvalues classifier based upon the model order selection rules. Then, the outputs of the latter classification schemes are exploited to infer, according to a specific criterion, the dominant polarization. The performance analysis is conducted on the measured data and points out the effectiveness of the newly proposed classification architecture also showing that information about the dominant polarization can be representative of the type of structure which gives raise to the dominant backscattering mechanism.
机译：在这封信中，我们设计了一种新颖的无监督架构，用于自动分类Polarimetric SAR图像中的主导极化。为此，我们利用[1]中开发的思想，并适当地利用它们来构建能够识别特征在测试的像素的主导散射机制的决策逻辑。具体地，我们将原始数据组合生成三组不同的减小尺寸向量，其基于模型顺序选择规则馈送主导特征值分类器。然后，根据特定标准，利用后一种分类方案的输出来推断出主导极化。在测量数据上进行性能分析，并指出了新提出的分类架构的有效性，也表明主导极化的信息可以代表一种结构的类型，其给出了所主体的反向散射机构。

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