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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。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.
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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SHV与SHH和SVV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SHV与SHH和SVV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 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 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, μC 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 μC 油菜LAI μC 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 σRH,σRV,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,σRL,σRV,m-χ分解 水稻冠层含水量 g0,σRV,σRH,σRR,σRL 水稻穗生物量 σRH,σRR,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,PL,m-δ分解偶次散射分量、反熵A(即m) 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 SRH,SRV 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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