简缩极化SAR数据处理与应用研究进展

许璐; 张红; 王超; 吴樊; 张波; 汤益先

doi:10.12000/JR19106

简缩极化SAR数据处理与应用研究进展

doi: 10.12000/JR19106

许璐^1,,
张红^1, ,,
王超^1,2,,
吴樊^1,,
张波^1,,
汤益先^1,

1.
中国科学院空天信息创新研究院数字地球重点实验室北京 100094
2.
中国科学院大学北京 100049

基金项目: 国家自然科学基金(41971395, 41930110)

详细信息

作者简介:
许璐：许　璐(1992–)，女，助理研究员，2019年毕业于中国科学院遥感与数字地球研究所，获理学博士学位。现为中国科学院空天信息创新研究院助理研究员。研究方向为极化SAR、时间序列SAR智能处理与应用。E-mail: xulu@radi.ac.cn

张红：张　红(1972–)，女，研究员，博士生导师，2002年毕业于中国科学院遥感应用所，获理学博士学位，现为中国科学院空天信息创新研究院研究员，担任IEEE GRSS北京分会副主席，中国图象图形学学会遥感图像专业委员会委员，主要研究领域为SAR图像智能处理、极化SAR、干涉SAR等。E-mail: zhanghong@radi.ac.cn

王超：王　超(1963–)，男，研究员，博士生导师，曾任德国宇航院高频技术研究所客座研究员，现为中国科学院空天信息创新研究院研究员，中国科学院大学岗位教授，担任中国图象图形学会常务理事、IEEE GRSS高级会员、《遥感技术与应用》副主编、《中国图象图形学报》副主编，曾任IEEE GRSS Beijing Chapter主席，主要从事InSAR高性能处理、SAR图像智能处理与应用研究。E-mail:wangchao@radi.ac.cn

吴樊：吴　樊(1976–)，男，副研究员，2005年于中国科学院遥感应用研究所获博士学位，现为中国科学院空天信息创新研究院副研究员，研究方向为SAR图像处理与信息提取。E-mail: wufan@radi.ac.cn

张波：张　波(1976–)，男，副研究员，硕士生导师，2005年于中国科学院遥感应用研究所获博士学位，现为中国科学院空天信息创新研究院副研究员，研究方向为SAR大数据处理，雷达目标特性，目标检测与识别等。E-mail: zhangbo@radi.ac.cn

汤益先(1978–)，男，副研究员，2006年于中国科学院遥感应用研究所获博士学位，现为中国科学院空天信息创新研究院副研究员，研究方向为高性能InSAR处理与应用。E-mail:tangyx@aircas.ac.cn

通讯作者:
张红 zhanghong@radi.ac.cn

中图分类号: TN957.52
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出版历程
- 收稿日期: 2019-12-02
- 修回日期: 2020-02-02
- 网络出版日期: 2020-02-17
- 刊出日期: 2020-02-28

Progress in the Processing and Application of Compact Polarimetric SAR

XU Lu^1
,,
ZHANG Hong^{1
, ,},
WANG Chao^{1,2
,},
WU Fan^1
,,
ZHANG Bo^1
,,
TANG Yixian^1
,

1.
Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Beijing 100094, China
2.
University of Chinese Academy of Sciences, Beijing 100049, China

Funds: The Natural National Science Foundation of China (41971395, 41930110)

More Information

Corresponding author: ZHANG Hong, zhanghong@radi.ac.cn

摘要

摘要: 极化信息能丰富合成孔径雷达(SAR)数据的信息量，在农业、环境、海洋、森林、军事等领域取得了广泛的应用，但同时也面临分辨率较低、幅宽较小的问题，带来较高的应用成本。简缩极化SAR(CP SAR)作为一种能同时获取较为丰富的地表信息并实现较大幅宽观测的极化SAR模式，在过去十余年中引起了科研人员的广泛关注。随着印度RISAT-1卫星的成功发射，简缩极化SAR在一系列应用研究中取得了新进展。该文简要介绍了简缩极化SAR的经典数据处理方法，总结了近十余年来简缩极化SAR在农业和海洋应用领域的主要研究成果，最后对其发展方向进行了分析与展望。
- 合成孔径雷达 /
- 简缩极化SAR /
- 混合极化 /
- 极化分解 /
- 分类
Abstract: Polarimetric information enriches the content of a Synthetic Aperture Radar (SAR) and has been widely used in agriculture, environment, ocean, forest, military, and other fields. However, it also faces limitations regarding its low resolution and small width, which lead to high application cost. As a novel polarimetric SAR system that can simultaneously obtain relatively rich scatter information and large swath, Compact Polarimetric SAR (CP SAR) has attracted extensive attention from researchers in the past decade. With the successful launch of India’s RISAT-1 satellite, new progresses have been made in the application fields on CP SAR. In this paper, the classical data processing methods of CP SAR are briefly introduced and the main research results of the application of CP SAR in the agriculture and maritime fields over the past 10 years are summarized. Finally, the prospects on its development are given.
- Synthetic Aperture Radar (SAR) /
- Compact Polarimetry SAR(CP SAR) /
- Hybrid polarimetric /
- Polarimetric decomposition /
- Classification

HTML全文

表 1 简缩极化SAR全极化(FP)信息重建方法小结

Table 1. Summary of Fully Polarimetric (FP) information reconstruction methods for CP SAR

文献	方法特点	适用模式	应用领域
文献[8]	假设反射对称性成立，提出S_HV与S_HH和S_VV的关系：$\dfrac{ {\left\langle { { {\left\| { {S_{ {\rm{HV} } } } } \right\|}^2} } \right\rangle } }{ {\left\langle { { {\left\| { {S_{ {\rm{HH} } } } } \right\|}^2} } \right\rangle + \left\langle { { {\left\| { {S_{ {\rm{VV} } } } } \right\|}^2} } \right\rangle } } = \dfrac{ {(1 - \left\| { {\rho _{ {\rm{HH {\text{-} } VV} } } } } \right\|)} }{N}$	不限	不限
文献[19]	考虑到完全随机体散射的情况，提出应迭代地修改N的值： $N = \dfrac{{\left\langle {{{\left\| {{S_{{\rm{HH}}}} - {S_{{\rm{VV}}}}} \right\|}^2}} \right\rangle }}{{\left\langle {{{\left\| {{S_{{\rm{HV}}}}} \right\|}^2}} \right\rangle }}$	不限	不限
文献[20]	假设反射对称性不成立，用改进的四分量分解方法，修改S_HV与S_HH和S_VV的关系$\dfrac{ {\left\langle { { {\left\| { {S_{ {\rm{HV} } } } } \right\|}^2} } \right\rangle } }{ {\left\langle { { {\left\| { {S_{ {\rm{HH} } } } } \right\|}^2} } \right\rangle + \left\langle { { {\left\| { {S_{ {\rm{VV} } } } } \right\|}^2} } \right\rangle } } = \dfrac{ {(1 - \left\| { {\rho _{ {\rm{HH {\text{-} } VV} } } } } \right\|)} }{4}\left( {\dfrac{ { {P_{\rm{v} } } + 2{P_{\rm{h} } } } }{ { {P_{\rm{v} } } } } } \right)$	不限	海面船舶检测
文献[21,22]	针对海面目标检测，提出用N的平均值$\bar N$进行重建，并给出$\bar N$的估计模型$\bar N = {b_1} + {b_2}\exp ( - {\theta ^{{b_3}}})$	不限	海面目标检测
文献[23]	针对海面风速反演，提出新的参数N估计模型：$N = {P_1}{\theta ^4} + {P_2}{\theta ^3} + {P_3}{\theta ^2} + {P_4}\theta + {P_5}$	不限	海面风速反演
文献[24]	针对海面溢油检测，提出新的参数N估计模型 $N = a \times {R^b};R = \dfrac{{\left\langle {{{\left\| {{S_{{\rm{HV}}}}} \right\|}^2}} \right\rangle }}{{\left\langle {{{\left\| {{S_{{\rm{HH}}}}} \right\|}^2}} \right\rangle + \left\langle {{{\left\| {{S_{{\rm{VV}}}}} \right\|}^2}} \right\rangle }}$	不限	海面溢油检测
文献[25]	基于Stokes参数，提出两个直接估计交叉极化项的方法$\left\langle {{{\left\| {{S_{{\rm{HV}}}}} \right\|}^2}} \right\rangle = \dfrac{{\left( {1 - {\rm{DoP}}} \right){g_0}}}{2}$; $\left\langle {{{\left\| {{S_{{\rm{HV}}}}} \right\|}^2}} \right\rangle = \dfrac{{{\lambda _2}{g_0}}}{{2{\lambda _1}}}$	HP模式	海冰监测
文献[26]	根据Freeman-Durden分解的体散射模型提出直接估计交叉极化项的方法${P_{\rm{v}}} = H \times \left( {{\lambda _1} + {\lambda _2}} \right) = 8 \times \left\langle {{{\left\| {{S_{{\rm{HV}}}}} \right\|}^2}} \right\rangle $	HP模式	不限
文献[27]	提出基于Wishart-Bayesian正则化的重建方法，不依赖参数N的估计	不限	不限

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表 2 简缩极化SAR极化分解方法小结

Table 2. Summary of the polarimetric decomposition methods for CP SAR

类型	优点	缺点
基于Stokes参数	简单易行，便于理解	存在体散射过估计
H/α分解	便于与全极化SAR进行直接对比	只有DCP模式的α角能指示不同散射机制，且与全极化之间存在近似余角的关系；存在散射熵过估计
基于模型的分解	便于与全极化SAR进行直接对比	分解结果受模型假设条件影响；存在体散射过估计；需迭代求解模型结果，计算较为复杂

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表 3 基于简缩极化SAR特征的农作物生物物理学参数反演研究小结

Table 3. Summary of crop biophysical parameter inversion based on CP SAR features

文献	数据	相关植被参数	相关简缩极化特征
文献[57]	12景ESAR L波段仿真HP模式数据	小麦湿生物量	δ
		玉米湿生物量	δ, μ_C
		冬油菜高度	m
文献[58]	5景RADARSAT-2仿真HP模式数据	油菜生物量	g₃
		油菜株高	μ_C
		油菜LAI	μ_C
文献[59]	2景RISAT-1真实HP模式数据	棉花株高	m-χ/m-δ分解体散射分量
		棉花株龄	σ_RH,σ_RV,m-χ/m-δ分解体散射分量
		棉花生物量
文献[60]	6景RADARSAT-2仿真HP模式数据	玉米生物量	m-δ分解VSR
文献[61]	5景RADARSAT-2仿真HP模式数据	水稻株高	g₀,σ_RL,σ_RV,m-χ分解
		水稻冠层含水量	g₀,σ_RV,σ_RH,σ_RR,σ_RL
		水稻穗生物量	σ_RH,σ_RR,m-χ/m-δ分解
		水稻LAI	g₀,g₁
文献[62]	1景RADARSAT-2仿真HP模式数据	冬小麦LAI	H,P_L,m-δ分解偶次散射分量、反熵A(即m)

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表 4 简缩极化SAR船舶检测算法小结

Table 4. Summary of the ship detection methods of CP SAR

文献	方法	相关特征	性能
文献[71]	视觉注意机制的特征增强、lognormal-CFAR检测	δ, m-δ分解体散射分量	优于SPAN-CFAR或RH-CFAR
文献[72]	CFAR预检测、滤波、SVM分类器	δ, χ, RHRV通道相关系数、m-χ 分解体散射分量	优于SPAN-CFAR、全极化PWF
文献[73]	U-Net网络	RH, RV通道强度	优于HH单极化、HH/HV双极化；优于CFAR和Faster RCNN网络
文献[74]	广义GAMMA-CFAR检测	HESA, LESA	优于SPAN-CFAR、全极化SPAN-CFAR, H-CFAR
文献[75]	Notch滤波、广义GAMMA- CFAR检测	S_RH,S_RV	优于HH/HV双极化
文献[76]	ReliefF特征筛选、加权SVM检测、基于m-χ分解的虚警滤除	H/α分解、m-χ分解表面散射分量	优于SVM检测器、与全极化相当
文献[77]	–	Phase Factor	优于不同分布下的CFAR检测器

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