若干层析SAR成像方法在解叠掩性能上的对比分析

任烨仙 徐丰

任烨仙, 徐丰. 若干层析SAR成像方法在解叠掩性能上的对比分析[J]. 雷达学报, 2022, 11(1): 71–82. doi: 10.12000/JR21139
引用本文: 任烨仙, 徐丰. 若干层析SAR成像方法在解叠掩性能上的对比分析[J]. 雷达学报, 2022, 11(1): 71–82. doi: 10.12000/JR21139
REN Yexian and XU Feng. Comparative experiments on separation performance of overlapping scatterers with several tomography imaging methods[J]. Journal of Radars, 2022, 11(1): 71–82. doi: 10.12000/JR21139
Citation: REN Yexian and XU Feng. Comparative experiments on separation performance of overlapping scatterers with several tomography imaging methods[J]. Journal of Radars, 2022, 11(1): 71–82. doi: 10.12000/JR21139

若干层析SAR成像方法在解叠掩性能上的对比分析

doi: 10.12000/JR21139
基金项目: 国家自然科学基金(61991422)
详细信息
    作者简介:

    任烨仙(1994–),男,江苏无锡人,博士生,复旦大学信息科学与工程学院,研究方向为层析SAR影像三维成像、极化SAR影像处理

    徐 丰(1982–),男,浙江东阳人,复旦大学博士学位,教授,研究方向为SAR图像解译、电磁散射建模、智能信息技术

    通讯作者:

    徐丰 fengxu@fudan.edu.cn

  • 责任主编:张冰尘 Corresponding Editor: ZHANG Bingchen
  • 中图分类号: TN957.52

Comparative Experiments on Separation Performance of Overlapping Scatterers with Several Tomography Imaging Methods

Funds: The National Natural Science Foundation of China (61991422)
More Information
  • 摘要: 层析技术因具有解译城区SAR影像上复杂叠掩场景的能力而备受关注。层析成像包含两个部分:估计散射体在高程向的分布和确定散射体在混叠像元内的真实数目。该文以中科院空天院峨眉数据的机载阵列干涉系统参数为基础,选取了若干代表性的方法,包括OMP, SLIM和MUSIC等层析谱估计方法以及BIC和GLRT等模型定阶方法,进行了模拟叠掩目标的层析反演实验,使用了克拉默-拉奥界和重建成功率来评估实验结果。实验表明:在机载阵列数很有限的条件下,(1)使用2阶统计量反演的高程估计量的方差比单个观测矢量反演结果的方差更小;(2)叠掩散射体间的幅度比、相位差和散射间距会影响层析算法解叠掩的成功率;(3)叠掩散射体间的相位差会使层析算法的高程估计发生偏差。

     

  • 图  1  层析成像几何示意图

    Figure  1.  Imaging geometry of tomographic SAR

    图  2  峨眉数据的克拉默-拉奥界

    Figure  2.  The Cramér-Rao Lower Bound (CRLB) of the Emei data

    图  3  由OMP算法估计散射体位置分布及其对应的高斯混合分布拟合曲线

    Figure  3.  The probability density function of scattering position estimated by OMP and its corresponding Gaussian mixture distribution fitting curve

    图  4  不同算法的高程估计精度(M=11, SNR=5 dB, ρs=23.7805 m)

    Figure  4.  Elevation estimation accuracy of different algorithm (M=11, SNR=5 dB, ρs=23.7805 m)

    图  5  OMP-BIC, OMP-GLRT, SLIM, MUSIC的重建成功率与叠掩散射体的幅度比${a_1}/{a_2}$的函数关系

    Figure  5.  Recovery probability as a function of amplitude ratio ${a_1}/{a_2}$ obtained by using the OMP-BIC, OMP-GLRT, SLIM and MUSIC

    图  6  OMP-BIC, OMP-GLRT, SLIM, MUSIC的重建成功率与叠掩散射体的相位差$\Delta \phi $的函数关系

    Figure  6.  Recovery probability as a function of phase difference $\Delta \phi $ obtained by using the OMP-BIC, OMP-GLRT, SLIM and MUSIC

    图  7  OMP-BIC, OMP-GLRT, SLIM, MUSIC的重建成功率与叠掩散射体的规范化高程间距$\Delta s/{\rho _s}$的函数关系

    Figure  7.  Recovery probability as a function of elevation distance $\Delta s/{\rho _s}$ obtained by using the OMP-BIC, OMP-GLRT, SLIM and MUSIC

    图  8  叠掩散射体间的相位差对MUSIC算法谱估计结果造成的扰动效应(图 8是图 7中$1.2{\rho _s}$附近发生重建成功率异常下降的解释)

    Figure  8.  The disturbance of height estimation caused by phase difference $\Delta \phi $ between overlapped scatterers (Fig. 8 is an explanation of the abnormal decrease in the recovery rate in Fig. 7)

    图  9  MUSIC算法的高程估计精度(当限定${a_1}/{a_2} = 0.5$时,能量小的散射体位置估计出现明显偏差)

    Figure  9.  Elevation estimation accuracy of MUSIC algorithm (When the amplitude ratio is fixed at 0.5, the position estimation of the scatterer with small energy is obviously deviated)

    图  10  峨眉数据的场景(红色的箭头标记了切面上的三处叠掩)

    Figure  10.  The scene of Emei data (The walls of tall buildings and the roofs of low buildings are overlapped)

    图  11  与图 10中红虚线对应的距离-高程切面图(未经过地理编码)

    Figure  11.  Range-Elevation section corresponding to the red dotted line in Fig. 10 (without geocoding)

    图  12  层析成像结果(经过地理编码)

    Figure  12.  Tomography results (after geocoding)

    表  1  峨眉数据的参数

    Table  1.   Parameters of Emei data

    参数数值
    波长 $\lambda $0.031 m
    斜距 r1629 m
    入射角 $ \theta $$ {47}^{\circ } $
    基线 b0.2$ \times $11 m
    瑞利分辨率 ${\rho _s}$23.78 m
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出版历程
  • 收稿日期:  2021-09-26
  • 修回日期:  2022-01-09
  • 网络出版日期:  2022-01-28
  • 刊出日期:  2022-02-28

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