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(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(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(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(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(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: 2020-02-28

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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参考文献(117)

[1]	LEE J S and POTTIER E. Polarimetric Radar Imaging: From Basics to Applications[M]. New York: CRC Press, 2009: 43–44.
[2]	LARRAÑAGA A and ÁLVAREZ-MOZOS J. On the added value of quad-pol data in a multi-temporal crop classification framework based on RADARSAT-2 imagery[J]. Remote Sensing, 2016, 8(4): 335. doi: 10.3390/rs8040335
[3]	WU Fu, WANG Chao, ZHANG Hong, et al. Rice crop monitoring in South China with RADARSAT-2 quad-polarization SAR data[J]. IEEE Geoscience and Remote Sensing Letters, 2011, 8(2): 196–200. doi: 10.1109/LGRS.2010.2055830
[4]	YAJIMA Y, YAMAGUCHI Y, SATO R, et al. POLSAR image analysis of wetlands using a modified four-component scattering power decomposition[J]. IEEE Transactions on Geoscience and Remote Sensing, 2008, 46(6): 1667–1673. doi: 10.1109/tgrs.2008.916326
[5]	ZHANG Biao, PERRIE W, LI Xiaofeng, et al. Mapping sea surface oil slicks using RADARSAT‐2 quad‐polarization SAR image[J]. Geophysical Research Letters, 2011, 38(10): L10602. doi: 10.1029/2011gl047013
[6]	NUNZIATA F, MIGLIACCIO M, and BROWN C E. Reflection symmetry for polarimetric observation of man-made metallic targets at sea[J]. IEEE Journal of Oceanic Engineering, 2012, 37(3): 384–394. doi: 10.1109/JOE.2012.2198931
[7]	洪文. 基于混合极化架构的极化SAR: 原理与应用(中英文)[J]. 雷达学报, 2016, 5(6): 559–595. doi: 10.12000/JR16074 HONG Wen. Hybrid-polarity architecture based polarimetric SAR: Principles and applications[J]. Journal of Radars, 2016, 5(6): 559–595. doi: 10.12000/JR16074
[8]	SOUYRIS J C, IMBO P, FJORTOFT R, et al. Compact polarimetry based on symmetry properties of geophysical media: The π/4 mode[J]. IEEE Transactions on Geoscience and Remote Sensing, 2005, 43(3): 634–646. doi: 10.1109/TGRS.2004.842486
[9]	STACY N and PREISS M. Compact polarimetric analysis of X-band SAR data[C]. The 6th European Conference on Synthetic Aperture Radar, Dresden, Germany, 2006.
[10]	RANEY R K. Hybrid-polarity SAR architecture[J]. IEEE Transactions on Geoscience and Remote Sensing, 2007, 45(11): 3397–3404. doi: 10.1109/TGRS.2007.895883
[11]	RANEY R K, CAHILL J T S, PATTERSON G W, et al. The m-chi decomposition of hybrid dual-polarimetric radar data with application to lunar craters[J]. Journal of Geophysical Research: Planets, 2012, 117(E12): E00H21. doi: 10.1029/2011je003986
[12]	RANEY R K, SPUDIS P D, BUSSEY B, et al. The lunar mini-RF radars: Hybrid polarimetric architecture and initial results[J]. Proceedings of the IEEE, 2011, 99(5): 808–823. doi: 10.1109/JPROC.2010.2084970
[13]	MISRA T and KUMAR A S K. Scatterometer and RISAT-1: ISRO’S contribution to radar remote sensing[C]. 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Milan, Italy, 2015: 4220–4223. doi: 10.1109/IGARSS.2015.7326757.
[14]	YOKOTA Y, NAKAMURA S, ENDO J, et al. Evaluation of compact polarimetry and along track interferometry as experimental mode of PALSAR-2[C]. 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Milan, Italy, 2015: 4125–4128. doi: 10.1109/IGARSS.2015.7326733.
[15]	SPACEX. RADARSAT constellation mission[EB/OL]. https://www.spacex.com/sites/spacex/files/radarsat_constellation_mission_press_kit.pdf, 2019.
[16]	RANEY R K. DESDynI adopts hybrid polarity SAR architecture[C]. 2009 IEEE Radar Conference, Pasadena, US, 2009: 1–4. doi: 10.1109/RADAR.2009.4977046.
[17]	PUTREVU D, DAS A, VACHHANI J G, et al. Chandrayaan-2 dual-frequency SAR: Further investigation into lunar water and regolith[J]. Advances in Space Research, 2016, 57(2): 627–646. doi: 10.1016/J.ASR.2015.10.029
[18]	张红, 谢镭, 王超, 等. 简缩极化SAR数据信息提取与应用[J]. 中国图象图形学报, 2013, 18(9): 1065–1073. doi: 10.11834/jig.20130902 ZHANG Hong, XIE Lei, WANG Chao, et al. Information extraction and application of compact polarimetric SAR data[J]. Journal of Image and Graphics, 2013, 18(9): 1065–1073. doi: 10.11834/jig.20130902
[19]	NORD M E, AINSWORTH T L, LEE J S, et al. Comparison of compact polarimetric synthetic aperture radar modes[J]. IEEE Transactions on Geoscience and Remote Sensing, 2009, 47(1): 174–188. doi: 10.1109/TGRS.2008.2000925
[20]	YIN Junjun, YANG Jian, and ZHANG Xinzheng. On the ship detection performance with compact polarimetry[C]. 2011 IEEE RadarCon (RADAR), Kansas City, USA, 2011: 675–680. doi: 10.1109/RADAR.2011.5960623.
[21]	DENBINA M and COLLINS M J. Iceberg detection using compact polarimetric synthetic aperture radar[J]. Atmosphere-Ocean, 2012, 50(4): 437–446. doi: 10.1080/07055900.2012.733307
[22]	COLLINS M J, DENBINA M, and ATTEIA G. On the reconstruction of quad-pol SAR data from compact polarimetry data for ocean target detection[J]. IEEE Transactions on Geoscience and Remote Sensing, 2013, 51(1): 591–600. doi: 10.1109/TGRS.2012.2199760
[23]	LI Haiyan, WU Jin, PERRIE W, et al. Wind speed retrieval from hybrid-pol compact polarization synthetic aperture radar images[J]. IEEE Journal of Oceanic Engineering, 2018, 43(3): 713–724. doi: 10.1109/JOE.2017.2722225
[24]	LI Yu, ZHANG Yuanzhi, CHEN Jie, et al. Improved compact polarimetric SAR quad-pol reconstruction algorithm for oil spill detection[J]. IEEE Geoscience and Remote Sensing Letters, 2014, 11(6): 1139–1142. doi: 10.1109/lgrs.2013.2288336
[25]	ESPESETH M M, BREKKE C, and ANFINSEN S N. Hybrid-polarity and reconstruction methods for sea ice with L-and C-band SAR[J]. IEEE Geoscience and Remote Sensing Letters, 2016, 13(3): 467–471. doi: 10.1109/LGRS.2016.2519824
[26]	KUMAR A and PANIGRAHI R K. Entropy based reconstruction technique for analysis of hybrid-polarimetric SAR data[J]. IET Radar, Sonar & Navigation, 2019, 13(4): 620–626. doi: 10.1049/iet-rsn.2018.5338
[27]	YUE Dongxiao, XU Feng, and JIN Yaqiu. Wishart-Bayesian reconstruction of Quad-Pol from Compact-Pol SAR image[J]. IEEE Geoscience and Remote Sensing Letters, 2017, 14(9): 1623–1627. doi: 10.1109/LGRS.2017.2727280
[28]	REIGBER A, NEUMANN M, FERRO-FAMIL L, et al. Multi-baseline coherence optimisation in partial and compact polarimetric modes[C]. 2008 IEEE International Geoscience and Remote Sensing Symposium, Boston, USA, 2008: 597–600. doi: 10.1109/IGARSS.2008.4779063.
[29]	RANEY R K. Comparing compact and quadrature polarimetric SAR performance[J]. IEEE Geoscience and Remote Sensing Letters, 2016, 13(6): 861–864. doi: 10.1109/lgrs.2016.2550863
[30]	RANEY R K. Hybrid dual-polarization synthetic aperture radar[J]. Remote Sensing, 2019, 11(13): 1521. doi: 10.3390/rs11131521
[31]	RANEY R K. Dual-polarized SAR and stokes parameters[J]. IEEE Geoscience and Remote Sensing Letters, 2006, 3(3): 317–319. doi: 10.1109/LGRS.2006.871746
[32]	CHARBONNEAU F J, BRISCO B, RANEY R K, et al. Compact polarimetry: Multi-thematic evaluation[C]. The 4th International Workshop on Science and Applications of SAR Polarimetry and Polarimetric Interferometry (PolInSAR), Frascati, Italy, 2009, 26–30.
[33]	RANEY R K, CAHILL J T S, PATTERSON G W, et al. The m-chi decomposition of hybrid dual-polarimetric radar data[C]. IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Munich: Germany, 2012, 5093–5096. doi: 10.1109/IGARSS.2012.6352465.
[34]	CLOUDE S R, GOODENOUGH D G, and CHEN H. Compact decomposition theory[J]. IEEE Geoscience and Remote Sensing Letters, 2012, 9(1): 28–32. doi: 10.1109/LGRS.2011.2158983
[35]	SABRY R and VACHON P W. A unified framework for general compact and quad polarimetric SAR data and imagery analysis[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(1): 582–602. doi: 10.1109/TGRS.2013.2242479
[36]	GUO R, LIU Y B, WU Y H, et al. Applying H/α decomposition to compact polarimetric SAR[J]. IET Radar, Sonar & Navigation, 2012, 6(2): 61–70. doi: 10.1049/iet-rsn.2011.0007
[37]	ZHANG Hong, XIE Lei, WANG Chao, et al. Investigation of the capability of H-α decomposition of compact polarimetric SAR[J]. IEEE Geoscience and Remote Sensing Letters, 2014, 11(4): 868–872. doi: 10.1109/LGRS.2013.2280456
[38]	谢镭. 多模式极化SAR图像分解与分类方法及应用研究[D]. [博士论文], 中国科学院大学, 2016: 46–59. XIE Lei. Researches on methods and applications of image decomposition and classification for multi-mode polarimetric SAR[D]. [Ph.D. dissertation], University of Chinese Academy of Sciences, 2016: 46–59.
[39]	GUO Rui, HE Wei, ZHANG Shuangxi, et al. Analysis of three-component decomposition to compact polarimetric synthetic aperture radar[J]. IET Radar, Sonar & Navigation, 2014, 8(6): 685–691. doi: 10.1049/iet-rsn.2013.0114
[40]	刘萌, 张红, 王超. 基于简缩极化数据的三分量分解模型[J]. 电波科学学报, 2012, 27(2): 365–371. LIU Meng, ZHANG Hong, and WANG Chao. Three-component scattering model for compact polarimetric SAR data[J]. Chinese Journal of Radio Science, 2012, 27(2): 365–371.
[41]	HAN Kuoye, JIANG Mian, WANG Mingjiang, et al. Compact polarimetric SAR interferometry target decomposition with the freeman-durden method[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2018, 11(8): 2847–2861. doi: 10.1109/JSTARS.2018.2842125
[42]	KUMAR A, DAS A, and PANIGRAHI R K. Hybrid-pol based three-component scattering model for analysis of RISAT data[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017, 10(12): 5155–5162. doi: 10.1109/JSTARS.2017.2768378
[43]	AINSWORTH T L, KELLY J P, and LEE J S. Classification comparisons between dual-pol, compact polarimetric and quad-pol SAR imagery[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2009, 64(5): 464–471. doi: 10.1016/j.isprsjprs.2008.12.008
[44]	KUMAR V, RAO Y S, BHATTACHARYA A, et al. Classification assessment of real versus simulated compact and quad-pol modes of ALOS-2[J]. IEEE Geoscience and Remote Sensing Letters, 2019, 16(9): 1497–1501. doi: 10.1109/LGRS.2019.2899268
[45]	CHARBONNEAU F J, BRISCO B, RANEY R K, et al. Compact polarimetry overview and applications assessment[J]. Canadian Journal of Remote Sensing, 2010, 36(S2): S298–S315. doi: 10.5589/m10-062
[46]	OHKI M and SHIMADA M. Large-area land use and land cover classification with quad, compact, and dual polarization SAR data by PALSAR-2[J]. IEEE Transactions on Geoscience and Remote Sensing, 2018, 56(9): 5550–5557. doi: 10.1109/TGRS.2018.2819694
[47]	BRISCO B, LI K, TEDFORD B, et al. Compact polarimetry assessment for rice and wetland mapping[J]. International Journal of Remote Sensing, 2013, 34(6): 1949–1964. doi: 10.1080/01431161.2012.730156
[48]	XU Lu, ZHANG Hong, and WANG Chao. Comparative analysis of classification results between compact and fully polarimetric SAR images in random forest classifier[C]. 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). Fort Worth, USA, 2017: 3929–3932. doi: 10.1109/IGARSS.2017.8127859.
[49]	XU Lu, ZHANG Hong, WANG Chao, et al. Corn mapping uisng multi-temporal fully and compact SAR data[C]. 2017 SAR in Big Data Era: Models, Methods and Applications (BIGSARDATA), Beijing, China, 2017. doi: 10.1109/BIGSARDATA.2017.8124925.
[50]	MAHDIANPARI M, MOHAMMADIMANESH F, MCNAIRN H, et al. Mid-season crop classification using dual-, compact-, and full-polarization in preparation for the Radarsat Constellation Mission (RCM)[J]. Remote Sensing, 2019, 11(13): 1582. doi: 10.3390/rs11131582
[51]	XIE Lei, ZHANG Hong, WU Fan, et al. Capability of rice mapping using hybrid polarimetric SAR data[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2015, 8(8): 3812–3822. doi: 10.1109/JSTARS.2014.2387214
[52]	XIE Lei, ZHANG Hong, LI Hongzhong, et al. A unified framework for crop classification in southern China using fully polarimetric, dual polarimetric, and compact polarimetric SAR data[J]. International Journal of Remote Sensing, 2015, 36(14): 3798–3818. doi: 10.1080/01431161.2015.1070319
[53]	UPPALA D, KOTHAPALLI R V, POLOJU S, et al. Rice crop discrimination using single date RISAT1 hybrid (RH, RV) polarimetric data[J]. Photogrammetric Engineering & Remote Sensing, 2015, 81(7): 557–563. doi: 10.14358/PERS.81.7.557
[54]	UPPALA D, VENKATA R K, POLOJU S, et al. Discrimination of maize crop with hybrid polarimetric RISAT1 data[J]. International Journal of Remote Sensing, 2016, 37(11): 2641–2652. doi: 10.1080/01431161.2016.1184353
[55]	国贤玉, 李坤, 王志勇, 等. 基于SVM+SFS策略的多时相紧致极化SAR水稻精细分类[J]. 国土资源遥感, 2018, 30(4): 20–27. doi: 10.6046/gtzyyg.2018.04.04 GUO Xianyu, LI Kun, WANG Zhiyong, et al. Fine classification of rice with multi-temporal compact polarimetric SAR based on SVM +SFS strategy[J]. Remote Sensing for Land &Resources, 2018, 30(4): 20–27. doi: 10.6046/gtzyyg.2018.04.04
[56]	CHIRAKKAL S, HALDAR D, and MISRA A. Evaluation of hybrid polarimetric decomposition techniques for winter crop discrimination[J]. Progress in Electromagnetics Research M, 2017, 55: 73–84. doi: 10.2528/PIERM17011603
[57]	BALLESTER-BERMAN J D, and LOPEZ-SANCHEZ J M. Time series of hybrid-polarity parameters over agricultural crops[J]. IEEE Geoscience and Remote Sensing Letters, 2012, 9(1): 139–143. doi: 10.1109/LGRS.2011.2162312
[58]	ZHANG Wangfei, LI Zengyuan, CHEN Erxue, et al. Compact polarimetric response of rape (Brassica napus L.) at C-band: Analysis and growth parameters inversion[J]. Remote Sensing, 2017, 9(6): 591. doi: 10.3390/rs9060591
[59]	DAVE V A, HALDAR D, DAVE R, et al. Cotton crop biophysical parameter study using hybrid/compact polarimetric RISAT-1 SAR data[J]. Progress in Electromagnetics Research M, 2017, 57: 185–196. doi: 10.2528/PIERM16121903
[60]	HOMAYOUNI S, MCNAIRN H, HOSSEINI M, et al. Quad and compact multitemporal C-band PolSAR observations for crop characterization and monitoring[J]. International Journal of Applied Earth Observation and Geoinformation, 2019, 74: 78–87. doi: 10.1016/j.jag.2018.09.009
[61]	GUO Xianyu, LI Kun, SHAO Yun, et al. Inversion of rice biophysical parameters using simulated compact polarimetric SAR C-band data[J]. Sensors, 2018, 18(7): 2271. doi: 10.3390/s18072271
[62]	LIU Changan, CHEN Zhongxin, HAO Pengyu, et al. LAI Retrieval of winter wheat using simulated compact SAR data through GA-PLS modeling[C]. 2018 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Valencia, Spain, 2018: 3840–3843. doi: 10.1109/IGARSS.2018.8518005.
[63]	YANG Zhi, LI Kun, LIU Long, et al. Rice growth monitoring using simulated compact polarimetric C band SAR[J]. Radio Science, 2014, 49(12): 1300–1315. doi: 10.1002/2014RS005498
[64]	YANG Zhi, SHAO Yun, LI Kun, et al. An improved scheme for rice phenology estimation based on time-series multispectral HJ-1A/B and polarimetric RADARSAT-2 data[J]. Remote Sensing of Environment, 2017, 195: 184–201. doi: 10.1016/j.rse.2017.04.016
[65]	LOPEZ-SANCHEZ J M, VICENTE-GUIJALBA F, BALLESTER-BERMAN J D, et al. Polarimetric response of rice fields at C-band: Analysis and phenology retrieval[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(5): 2977–2993. doi: 10.1109/TGRS.2013.2268319
[66]	IZUMI Y, DEMIRCI S, BIN BAHARUDDIN M, et al. Analysis of dual-and full-circular polarimetric SAR modes for rice phenology monitoring: An experimental investigation through ground-based measurements[J]. Applied Sciences, 2017, 7(4): 368. doi: 10.3390/app7040368
[67]	ATTEIA G and COLLINS M J. Ship detection performance assessment for simulated RCM SAR data[C]. 2014 IEEE Geoscience and Remote Sensing Symposium (IGARSS), Quebec City, Canada, 2014: 553–556. doi: 10.1109/IGARSS.2014.6946482.
[68]	SHIRVANY R, CHABERT M, and TOURNERET J Y. Ship and oil-spill detection using the degree of polarization in linear and hybrid/compact dual-pol SAR[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2012, 5(3): 885–892. doi: 10.1109/JSTARS.2012.2182760
[69]	YIN Junjun and YANG Jian. Ship detection by using the M-Chi and M-Delta decompositions[C]. 2014 IEEE Geoscience and Remote Sensing Symposium (IGARSS), Quebec City, Canada, 2014: 2738–2741. doi: 10.1109/IGARSS.2014.6947042.
[70]	曹成会, 张杰, 张晰, 等. C波段紧缩极化合成孔径雷达船只目标检测性能分析[J]. 中国海洋大学学报, 2017, 47(2): 85–93. doi: 10.16441/j.cnki.hdxb.20160347 CAO Chenghui, ZHANG Jie, ZHANG Xi, et al. The analysis of ship target detection performance with C band compact polarimetric SAR[J]. Periodical of Ocean University of China, 2017, 47(2): 85–93. doi: 10.16441/j.cnki.hdxb.20160347
[71]	XU Lu, ZHANG Hong, WANG Chao, et al. Compact polarimetric SAR ship detection with m-δ decomposition using visual attention model[J]. Remote Sensing, 2016, 8(9): 751. doi: 10.3390/rs8090751
[72]	FAN Qiancong, CHEN Feng, CHENG Ming, et al. A modified framework for ship detection from compact polarization SAR image[C]. 2018 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Valencia, Spain, 2018: 3539–3542. doi: 10.1109/IGARSS.2018.8518763.
[73]	FAN Qiancong, CHEN Feng, CHENG Ming, et al. Ship detection using a fully convolutional network with compact polarimetric sar images[J]. Remote Sensing, 2019, 11(18): 2171. doi: 10.3390/rs11182171
[74]	GAO Gui, GAO Sheng, HE Juan, et al. Adaptive ship detection in hybrid-polarimetric SAR images based on the power-entropy decomposition[J]. IEEE Transactions on Geoscience and Remote Sensing, 2018, 56(9): 5394–5407. doi: 10.1109/TGRS.2018.2815592
[75]	GAO Gui, GAO Sheng, HE Juan, et al. Ship detection using compact polarimetric SAR based on the notch filter[J]. IEEE Transactions on Geoscience and Remote Sensing, 2018, 56(9): 5380–5393. doi: 10.1109/TGRS.2018.2815582
[76]	JI Kefeng, LENG Xiangguang, WANG Haibo, et al. Ship detection using weighted SVM and M-CHI decomposition in compact polarimetric SAR imagery[C]. 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Fort Worth, USA, 2017: 890–893. doi: 10.1109/IGARSS.2017.8127095.
[77]	CAO Chenghui, ZHANG Jie, MENG Junmei, et al. Analysis of ship detection performance with full-, compact-and dual-polarimetric SAR[J]. Remote Sensing, 2019, 11(18): 2160. doi: 10.3390/rs11182160
[78]	ZHANG Biao, LI Xiaofeng, PERRIE W, et al. Compact polarimetric synthetic aperture radar for marine oil platform and slick detection[J]. IEEE Transactions on Geoscience and Remote Sensing, 2017, 55(3): 1407–1423. doi: 10.1109/TGRS.2016.2623809
[79]	LI Haiyan, PERRIE W, HE Yijun, et al. Target detection on the ocean with the relative phase of compact polarimetry SAR[J]. IEEE Transactions on Geoscience and Remote Sensing, 2013, 51(6): 3299–3305. doi: 10.1109/TGRS.2012.2224119
[80]	LI Haiyan, PERRIE W, HE Yijun, et al. Analysis of the polarimetric SAR scattering properties of oil-covered waters[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2015, 8(8): 3751–3759. doi: 10.1109/JSTARS.2014.2348173
[81]	KUMAR L J V, KISHORE K J, and RAO K P. Decomposition methods for detection of oil spills based on RISAT-1 SAR images[J]. International Journal of Remote Sensing & Geoscience, 2014, 3(4): 2319–3484.
[82]	MIGLIACCIO M, NUNZIATA F, and BUONO A. SAR polarimetry for sea oil slick observation[J]. International Journal of Remote Sensing, 2015, 36(12): 3243–3273. doi: 10.1080/01431161.2015.1057301
[83]	YIN Junjun, YANG Jian, ZHOU Zhengshu, et al. The extended Bragg scattering model-based method for ship and oil-spill observation using compact polarimetric SAR[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2015, 8(8): 3760–3772. doi: 10.1109/JSTARS.2014.2359141
[84]	NUNZIATA F, MIGLIACCIO M, and LI Xiaofeng. Sea oil slick observation using hybrid-polarity SAR architecture[J]. IEEE Journal of Oceanic Engineering, 2015, 40(2): 426–440. doi: 10.1109/JOE.2014.2329424
[85]	BUONO A, NUNZIATA F, MIGLIACCIO M, et al. Polarimetric analysis of compact-polarimetry SAR architectures for sea oil slick observation[J]. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(10): 5862–5874. doi: 10.1109/TGRS.2016.2574561
[86]	ZHANG Yuanzhi, LI Yu, LIANG X S, et al. Comparison of oil spill classifications using fully and compact polarimetric SAR images[J]. Applied Sciences, 2017, 7(2): 193. doi: 10.3390/app7020193
[87]	谢广奇, 杨帅, 陈启浩, 等. 简缩极化特征值分析的溢油检测[J]. 遥感学报, 2019, 23(2): 303–312. doi: 10.11834/jrs.20197260 XIE Guangqi, YANG Shuai, CHEN Qihao, et al. Oil spill detection based on compact polarimetric eigenvalue decomposition[J]. Journal of Remote Sensing, 2019, 23(2): 303–312. doi: 10.11834/jrs.20197260
[88]	DABBOOR M, SINGHA S, TOPOUZELIS K, et al. Oil spill detection using simulated radarsat constellation mission compact polarimetric SAR data[C]. 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Fort Worth, USA, 2017: 4582–4585. doi: 10.1109/IGARSS.2017.8128021.
[89]	DABBOOR M, SINGHA S, MONTPETIT B, et al. Assessment of simulated compact polarimetry of the RCM medium resolution SAR modes for oil spill detection[C]. 2018 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Valencia, Spain, 2018: 2416–2419. doi: 10.1109/IGARSS.2018.8517756.
[90]	DABBOOR M, SINGHA S, MONTPETIT B, et al. Pre-launch assessment of RADARSAT constellation mission medium resolution modes for sea oil slicks and lookalike discrimination[J]. Canadian Journal of Remote Sensing, 2019, 45(3/4): 530–549. doi: 10.1080/07038992.2019.1659722
[91]	LI Haiyan and PERRIE W. Sea ice characterization and classification using hybrid polarimetry SAR[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2016, 9(11): 4998–5010. doi: 10.1109/JSTARS.2016.2584542
[92]	SINGHA S and RESSEL R. Arctic sea ice characterization using RISAT-1 compact-pol SAR imagery and feature evaluation: A case study over Northeast Greenland[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017, 10(8): 3504–3514. doi: 10.1109/JSTARS.2017.2691258
[93]	SINGHA S. Potential of compact polarimetry for operational sea ice monitoring over arctic and Antarctic region[C]. 2018 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Valencia, Spain, 2018: 7113–7116. doi: 10.1109/IGARSS.2018.8517653.
[94]	ESPESETH M M, BREKKE C, and JOHANSSON A M. Assessment of RISAT-1 and radarsat-2 for sea ice observations from a hybrid-polarity perspective[J]. Remote Sensing, 2017, 9(11): 1088. doi: 10.3390/rs9111088
[95]	NASONOVA S, SCHARIEN R K, GELDSETZER T, et al. Optimal compact polarimetric parameters and texture features for discriminating sea ice types during winter and advanced melt[J]. Canadian Journal of Remote Sensing, 2018, 44(4): 390–411. doi: 10.1080/07038992.2018.1527683
[96]	DABBOOR M, MONTPETIT B, and HOWELL S. Assessment of simulated compact polarimetry of the high resolution radarsat constellation mission SAR mode for multiyear and first year sea ice characterization[C]. 2018 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Valencia, Spain, 2018: 2420–2423. doi: 10.1109/IGARSS.2018.8517737.
[97]	GHANBARI M, CLAUSI D A, XU Linlin, et al. Contextual classification of sea-ice types using compact polarimetric SAR data[J]. IEEE Transactions on Geoscience and Remote Sensing, 2019, 57(10): 7476–7491. doi: 10.1109/TGRS.2019.2913796
[98]	TRUONG-LOI M L, FREEMAN A, DUBOIS-FERNANDEZ P C, et al. Estimation of soil moisture and Faraday rotation from bare surfaces using compact polarimetry[J]. IEEE Transactions on Geoscience and Remote Sensing, 2009, 47(11): 3608–3615. doi: 10.1109/TGRS.2009.2031428
[99]	PONNURANGAM G G, JAGDHUBER T, HAJNSEK I, et al. Soil moisture estimation using hybrid polarimetric SAR data of RISAT-1[J]. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(4): 2033–2049. doi: 10.1109/TGRS.2015.2494860
[100]	SANTI E, PETTINATO S, PALOSCIA S, et al. Estimating soil moisture from C and X band Sar using machine learning algorithms and compact polarimetry[C]. 2018 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Valencia, Spain, 2018: 1426–1429. doi: 10.1109/IGARSS.2018.8518469.
[101]	PONNURANGAM G G and RAO Y S. The application of compact polarimetric decomposition algorithms to L-band PolSAR data in agricultural areas[J]. International Journal of Remote Sensing, 2018, 39(22): 8337–8360. doi: 10.1080/01431161.2018.1488281
[102]	LAVALLE M, SOLIMINI D, POTTIER E, et al. Compact polarimetric SAR interferometry[J]. IET Radar, Sonar & Navigation, 2010, 4(3): 449–456. doi: 10.1049/iet-rsn.2009.0049
[103]	DUBOIS-FERNANDEZ P C, SOUYRIS J C, ANGELLIAUME S, et al. The compact polarimetry alternative for spaceborne SAR at low frequency[J]. IEEE Transactions on Geoscience and Remote Sensing, 2008, 46(10): 3208–3222. doi: 10.1109/TGRS.2008.919143
[104]	谈璐璐, 杨立波, 杨汝良. 合成孔径雷达简缩极化干涉数据的植被高度反演技术研究[J]. 电子与信息学报, 2010, 32(12): 2814–2819. doi: 10.3724/SP.J.1146.2010.00091 TAN Lulu, YANG Libo, and YANG Ruliang. Investigation on vegetation height retrieval technique with compact PolInSAR data[J]. Journal of Electronics &Information Technology, 2010, 32(12): 2814–2819. doi: 10.3724/SP.J.1146.2010.00091
[105]	RAMACHANDRAN N and DIKSHIT O. Experimental validation of compact tomosar for vegetation characterization[C]. 2018 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Valencia, Spain, 2018: 6727–6730. doi: 10.1109/IGARSS.2018.8517824.
[106]	SABRY R and AINSWORTH T L. SAR compact polarimetry for change detection and characterization[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2019, 12(3): 898–909. doi: 10.1109/JSTARS.2019.2896536
[107]	ZHANG Xuefei, ZHANG Hong, and WANG Chao. Water-change detection with Chinese Gaofen-3 simulated compact polarimetric SAR images[C]. 2017 SAR in Big Data Era: Models, Methods and Applications (BIGSARDATA), Beijing, China, 2017. doi: 10.1109/BIGSARDATA.2017.8124940.
[108]	MAHDIANPARI M, SALEHI B, MOHAMMADIMANESH F, et al. An assessment of simulated compact polarimetric SAR data for wetland classification using random forest algorithm[J]. Canadian Journal of Remote Sensing, 2017, 43(5): 468–484. doi: 10.1080/07038992.2017.1381550
[109]	DABBOOR M, BRISCO B, BANKS S, et al. Multitemporal monitoring of wetlands using simulated radarsat constellation mission compact polarimetric SAR data[C]. 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Fort Worth, USA, 2017: 4586–4589. doi: 10.1109/IGARSS.2017.8128022.
[110]	DABBOOR M, BANKS S, WHITE L, et al. Comparison of compact and fully polarimetric SAR for multitemporal wetland monitoring[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2019, 12(5): 1417–1430. doi: 10.1109/JSTARS.2019.2909437
[111]	MOHAMMADIMANESH F, SALEHI B, MAHDIANPARI M, et al. Full and simulated compact polarimetry sar responses to Canadian wetlands: Separability analysis and classification[J]. Remote Sensing, 2019, 11(5): 516. doi: 10.3390/rs11050516
[112]	BANKS S, MILLARD K, BEHNAMIAN A, et al. Contributions of actual and simulated satellite SAR data for substrate type differentiation and shoreline mapping in the Canadian arctic[J]. Remote Sensing, 2017, 9(12): 1206. doi: 10.3390/rs9121206
[113]	WHITE L, MILLARD K, BANKS S, et al. Moving to the RADARSAT constellation mission: Comparing synthesized compact polarimetry and dual polarimetry data with fully polarimetric RADARSAT-2 data for image classification of peatlands[J]. Remote Sensing, 2017, 9(6): 573. doi: 10.3390/rs9060573
[114]	FOBERT M A, SPRAY J G, and SINGHROY V. Assessing the benefits of simulated RADARSAT constellation mission polarimetry images for structural mapping of an impact crater in the Canadian shield[J]. Canadian Journal of Remote Sensing, 2018, 44(4): 321–336. doi: 10.1080/07038992.2018.1517022
[115]	BRISCO B, SHELAT Y, MURNAGHAN K, et al. Evaluation of C-band SAR for identification of flooded vegetation in emergency response products[J]. Canadian Journal of Remote Sensing, 2019, 45(1): 73–87. doi: 10.1080/07038992.2019.1612236
[116]	LIU Yin, LI Linlin, CHEN Qihao, et al. Building damage assessment of compact polarimetric SAR using statistical model texture parameter[C]. 2017 SAR in Big Data Era: Models, Methods and Applications (BIGSARDATA), Beijing, China, 2017. doi: 10.1109/BIGSARDATA.2017.8124923.
[117]	JEON W and KIM Y. Investigation of hybrid polarimetric features for tsunami-induced damage assessment of urban areas[J]. Remote Sensing Letters, 2019, 10(10): 988–997. doi: 10.1080/2150704x.2019.1637957