引用本文: 韩文涛, 周璀, 朱建军, 付海强, 解清华, 胡俊, 汪长城, 高晗. 极化SAR分解研究进展与展望[J]. 武汉大学学报 ( 信息科学版).
HAN Wen-tao, ZHOU Cui, ZHU Jian-jun, FU Hai-qiang, JIE Qing-hua, HU Jun, WANG Zhang-cheng, GAO Han. Research Progress and Challenges in the Polarimetric SAR Decompositon[J]. Geomatics and Information Science of Wuhan University.
 Citation: HAN Wen-tao, ZHOU Cui, ZHU Jian-jun, FU Hai-qiang, JIE Qing-hua, HU Jun, WANG Zhang-cheng, GAO Han. Research Progress and Challenges in the Polarimetric SAR Decompositon[J]. Geomatics and Information Science of Wuhan University.

## Research Progress and Challenges in the Polarimetric SAR Decompositon

• 摘要: 极化SAR（Synthetic Aperture Radar， SAR）由于其全天时、全天候的特点使其成为对地观测研究中的一种主要技术手段。极化SAR系统通过收发不同极化状态的雷达波可以获得四种组合方式的信号，提高了对不同地物的描述能力。极化SAR分解技术可以挖掘观测值记录的地物几何物理属性并促进极化SAR应用的发展，因此，该技术受到了广泛的关注。本文基于极化SAR信号与地物的交互过程，系统阐述目前极化SAR分解的主流算法，将其归纳为相干目标分解，非相干目标分解和图像可视化法三大类，并进一步对比了不同方法在C，L，P波段的观测下对不同地物的解译表现。针对已有研究现状，总结了现有极化SAR分解研究面临的两个关键挑战以及有潜力解决这两个问题的思路。论文旨在通过极化SAR分解的分析与总结，加深对极化SAR信号与地物交互过程的理解，促进极化SAR应用的发展。

Abstract: The polarimetric SAR technology (PolSAR) has become essential method in earth observation research due to the advantages of all-time and all-weather. The full PolSAR system can obtain signals in four channels by transmitting and receiving radar waves with horizontal and vertical polarization, which improves the ability to describe different ground targets. The PolSAR decomposition can extract the geometric and physical properties recorded by observations and promote the development of PolSAR applications. It therefore has received extensive attention. In this study, starting from the principle of interaction between PolSAR signals and targets, systematically expounding the mainstream algorithms, which can be summarized into three categories, including coherent decomposition methods represented by Pauli, Krogager, and Cameron decompositions; and incoherent decomposition represented by Freeman-Durden, Cloude-pottier decompositions; and image visualization method represented by polarization characteristic map and polarization projection map. The principles of different methods are summarized, and the interpretation performances of different methods for forests, crops, orthogonal buildings, and oriented buildings under the observation of C, L, and P band SAR signals are compared. The advantage of the coherent decomposition is that it is simple and easy to understand, without involving complex second-order statistical operations. However, its limitation is that it cannot describe the scattering characteristics of distributed targets, which hinders the development of coherent target decomposition. The advantage of model-based decomposition lies in its clear physical meaning, which plays an important role in different applications. However, its main limitation is the coupling between scattering components and the coupling between structure, orientation, and dielectric constant, making it difficult to comprehensively understand the geometric physical properties of targets. Eigenvalue-based decomposition has a rigorous mathematical background and can avoid model coupling. However, this method can only identify three dominant scatterers within each resolution cell. In addition, due to the variation of eigenvectors, it increases the difficulty in understanding their physical meaning. Image visualization methods visualize the interaction process between PolSAR signals and targets, facilitating a fine exploration of scattering information. However, rich polarization information provided increases the difficulty in extracting geometric physical features of targets, limiting its application scope to object classification and similar applications. Furthermore, two key challenges faced by the interpretation of PolSAR image and potential ways to solve these two problems are introduced. The purpose of this paper is to deepen the understanding of the interaction process between PolSAR signals and ground targets and to promote the development of PolSAR applications.

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