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A Model-Free Four Component Scattering Power Decomposition for Polarimetric SAR Data
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing ( IF 5.5 ) Pub Date : 2021-03-29 , DOI: 10.1109/jstars.2021.3069299
Subhadip Dey , Avik Bhattacharya , Alejandro C. Frery , Carlos Lopez-Martinez , Yalamanchili S. Rao

Target decomposition methods from polarimetric Synthetic Aperture Radar (PolSAR) data provides target scattering information. In this regard, several conventional model-based methods use scattering power components to analyze polarimetric SAR data. However, the typical hierarchical process to enumerate power components uses various branching conditions, leading to several limitations. These techniques assume ad hoc scattering models within a radar resolution cell. Therefore, the use of several models makes the computation of scattering powers ambiguous. Some common issues of model-based decompositions are related to the compensation of the orientation angle about the radar line of sight and the occurrence of negative power components. We propose a model-free four-component scattering power decomposition that alleviates these issues. In the proposed approach, we use the nonconventional 3-D Barakat degree of polarization to obtain the polarization state of scattered electromagnetic wave. The degree of polarization is used to obtain the even-bounce, odd-bounce, and diffused scattering power components. Along with this, a measure of target scattering asymmetry is also proposed, which is then suitably utilized to obtain the helicity power. All the power components are roll-invariant, nonnegative, and unambiguous. In addition to this, we propose an unsupervised clustering technique that preserves the dominance of the scattering power components for different targets. This clustering technique assists in understanding the importance of diverse scattering mechanisms based on target characteristics. The technique adequately captures the clusters’ variations from one target to another according to their physical and geometrical properties. In this study, we utilized L -, C -, and X -band full-polarimetric SAR data. We used these three datasets to show the effectiveness of decomposition powers and the natural interpretability of clustering results. The code is available at: https://github.com/Subho07/MF4CF

中文翻译:

极化SAR数据的无模型四分量散射功率分解。

来自极化合成孔径雷达(PolSAR)数据的目标分解方法可提供目标散射信息。在这方面,几种传统的基于模型的方法使用散射功率分量来分析极化SAR数据。但是,枚举功率分量的典型分层过程使用各种分支条件,从而导致一些限制。这些技术假设特设雷达分辨单元内的散射模型。因此,使用几种模型会使散射功率的计算变得模棱两可。基于模型的分解的一些常见问题与雷达视线周围的定向角的补偿和负功率分量的出现有关。我们提出了一种无模型的四分量散射功率分解方法,可以缓解这些问题。在提出的方法中,我们使用非常规的3-D Barakat极化度来获得散射电磁波的极化状态。极化度用于获得偶数反弹,奇数反弹和扩散散射功率分量。与此同时,还提出了目标散射不对称性的度量,然后将其适当地用于获得螺旋度。所有功率分量都是滚动不变的,非负的和明确的。除此之外,我们提出了一种无监督的聚类技术,该技术保留了不同目标的散射功率分量的优势。这种聚类技术有助于了解基于目标特征的各种散射机制的重要性。该技术可以根据群集的物理和几何特性,充分捕获群集从一个目标到另一个目标的变化。在这项研究中,我们利用了 这种聚类技术有助于了解基于目标特征的各种散射机制的重要性。该技术可以根据群集的物理和几何特性,充分捕获群集从一个目标到另一个目标的变化。在这项研究中,我们利用了 这种聚类技术有助于了解基于目标特征的各种散射机制的重要性。该技术可以根据群集的物理和几何特性,充分捕获群集从一个目标到另一个目标的变化。在这项研究中,我们利用了大号 -, C -, 和 X 带全极化SAR数据。我们使用这三个数据集来显示分解能力的有效性和聚类结果的自然可解释性。该代码位于:https://github.com/Subho07/MF4CF
更新日期:2021-04-23
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