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Incoherent Target Scattering Decomposition of Polarimetric SAR Data Based on Vector Model Roll-Invariant Parameters

机译:基于矢量模型滚动不变参数的极化SAR数据非相干目标散射分解

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摘要

Cameron's coherent decomposition is reconsidered in this paper for incoherent target decomposition. Characterizations of the minimum and maximum symmetric scattering components are considered in a unified model and projected into the Pauli basis to derive a new scattering vector model for the representation of coherent target scattering. Some roll-invariant parameters can be derived from the defined scattering vector model, and the classification method that Cameron developed for operational use of his decomposition can be performed using these parameters. The proposed model is compared with Touzi's target scattering vector model (TSVM), and it is revealed that the parameters of the TSVM are not unique and are invariant to the orientation angle of the polarization ellipse of the maximum copolarization backscattering return, which is descriptive of the polarization, whereas the extracted parameters from the proposed scattering vector are invariant to the orientation of the symmetry axis of the maximum symmetric component of the scatterer, which is descriptive of the target. From the implementation using Uninhabited Aerial Vehicle Synthetic Aperture Radar and Experimental Synthetic Aperture Radar images, the advantages and efficiency of the proposed scattering vector model are demonstrated.
机译:对于非相干目标分解,本文重新考虑了Cameron的相干分解。在统一模型中考虑了最小和最大对称散射分量的特征,并将其投影到Pauli基础上,以得出用于表示相干目标散射的新散射矢量模型。可以从定义的散射矢量模型中得出一些滚动不变参数,并且可以使用这些参数来执行Cameron为分解操作而开发的分类方法。将提出的模型与Touzi的目标散射矢量模型(TSVM)进行了比较,发现TSVM的参数不是唯一的,并且与最大共极化反向散射返回的偏振椭圆的取向角无关,这是对极化,而从建议的散射向量中提取的参数对于散射体的最大对称分量的对称轴的方向是不变的,这是目标的描述。通过使用无人飞行器合成孔径雷达和实验合成孔径雷达图像的实现,证明了所提出的散射矢量模型的优势和效率。

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