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Research Progress on Synthetic Aperture Radar Parametric Imaging Methods（in English）
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摘要: 传统合成孔径雷达(SAR)成像可视为点目标散射模型约束下数据空间到图像空间的映射。然而，真实目标多为延展目标，与传统线性成像处理中的点目标散射模型存在失配，会导致SAR图像表征失真。常见的现象是使延展目标多呈现为孤立强点，阻碍了基于SAR图像的目标辨识等应用。SAR参数化成像技术是为解决上述模型失配问题而诞生的一种非线性成像方法，特点是兼顾点目标和延展目标的散射模型。具体来说，是通过利用不同类别目标的回波或图像的相位与幅度特征对观测角度的敏感性，辨识目标类型，反演目标散射参数，进而根据目标散射的参数化模型，重建目标图像的技术。在对延展目标成像时，可获得比传统线性成像方法更好的图像质量。该文主要介绍了线型延展目标的参数化成像技术，对应真实场景中的孤立强点和连续边缘，深入讨论了基于回波域、图像域的参数化成像技术和试验结果，展望了未来SAR参数化成像技术的发展趋势。Abstract: Under the constraints of the point scattering model, traditional Synthetic Aperture Radar (SAR) imaging algorithms can be regarded as a mapping from data space to image space. However, most objects in the real scene are extended targets, which are mismatched with the point scattering model in traditional linear imaging algorithms. The abovementioned reasons lead to the distortion of SAR image representation. A common phenomenon is that the extended targets appear as isolated scattered points, which hinder the application of target recognition on the basis of SAR images. SAR parametric nonlinear imaging techniques are established to solve the abovementioned model mismatch problem. Such methods are characterized by the scattering models that consider point targets and extended targets. Specifically, by using the sensitivity of the phase and amplitude characteristics of the echoes or images to the observation angles, SAR parametric imaging methods can first identify the target type and estimate the scattering parameters, and then reconstruct the target image on the basis of the scattering model. SAR parametric imaging methods can obtain better image quality than traditional linear methods for extended targets. This article mainly introduces the parametric imaging methods of linear extended targets, which correspond to the isolated strong points and continuous edges of objects in the real scene, and discusses the parametric imaging methods on the basis of the echo and image domains and experimental results. Last, the future development trends of SAR parametric imaging methods are discussed.
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Key words:
- Synthetic Aperture Radar (SAR) /
- Parametric imaging /
- Extended targets
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图 3 直线型结构不同角度成像结果
Figure 3. Images of a linear structure with different observation angles
图 5 曲线型结构不同角度成像结果
Figure 5. Images of a arc structure with different observation angles
图 8 基于回波域特征的参数化成像方法框图
Figure 8. The block diagram of parametric imaging method based on echo domain features
图 12 点目标SAR图像与不同观测角度下相位的变化结果
Figure 12. The SAR image of a point target and the phase feature
图 13 端点目标SAR图像与不同观测角度下相位的变化结果
Figure 13. The SAR image of an endpoint and the phase feature
图 18 雷达不同观测角度下不同类型点目标的幅度变化图
Figure 18. The amplitude of different types of point targets under different observation angles of radar
图 19 基于宽角度幅度特征的目标辨识方法框图
Figure 19. Block diagram of target identification method based on wide-angle amplitude feature
图 20 基于美国空军实验室铲车数据重建结果
Figure 20. The experimental results of the backhoe from the AFRL
图 7 Block diagram of the parametric imaging method based on the echo domain features in Ref. [16]
图 14 Imaging result of the backhoe from the Air Force Research Laboratory in Ref. [16]
表 1 不同成像方法效果对比分析
Table 1. Analysis of the effects of different imaging methods
类型 成像方法 优点 缺点 线性方法 圆迹SAR BP成像 1.成像分辨率高
2.目标展现度高1.算法计算量大
2.实时性差
3.对雷达航迹需求高
4.数据量大非线性方法 压缩感知SAR成像 1.成像分辨率高
2.成像质量高
3.数据量小1.低信噪比下易出现虚假目标
2.算法计算量大
3.目标特征直观可视效果低多角度SAR图像融合 目标展现度高 1.数据量大
2.图像需要配准回波域参数化成像 1.目标展现度高
2.数据量小
3.成像质量高1.需要至少两个观测角度数据
2.处理多目标困难图像域参数化成像 1.目标展现度高
2.数据量小
3.成像质量高1.需要至少两个观测角度数据
2.图像需要配准
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表 1 Analyses of the different SAR imaging methods
Type Imaging method Advantages Disadvantages Linear Circular trace SAR
BP imaging1. High resolution
2. High fidelity1. Heavy calculation burden
2. Poor real-time performance
3. Requirement of trajectory accuracy
4. Large amount of dataNonlinear Compressed sensing SAR imaging 1. High resolution
2. High imaging quality
3. Small amount of data1. Low SNR prone to false targets
2. Heavy calculation burden
3. Low visual effectMulti-angle SAR image fusion 1. High fidelity 1. Large amount of data
2. Requirement of image registrationParametric imaging method in the echo domain 1. High fidelity
2. Small amount of data
3. High imaging quality1. Requirement of at least two observational angle data
2. Difficulty in dealing with multiple objectsParametric imaging method in the image domain 1. High fidelity
2. Small amount of data
3. High imaging quality1. Requirement of at least two observational angle data
2. Requirement of image registration
下载: 导出CSV
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[1] Air Force Research Laboratory Sensor Data Management System. Backhoe data sample and visual-d challenge problem[EB/OL]. https://www.sdms.afrl.af.mil, 2004. [2] 李春升, 杨威, 王鹏波. 星载SAR成像处理算法综述[J]. 雷达学报, 2013, 2(1): 111–122. doi: 10.3724/SP.J.1300.2013.20071LI Chunsheng, YANG Wei, and WANG Pengbo. A review of spaceborne SAR algorithm for image formation[J]. Journal of Radars, 2013, 2(1): 111–122. doi: 10.3724/SP.J.1300.2013.20071 [3] MCCORKLE J W and ROFHEART M. An order N2log(N) backprojector algorithm for focusing wide-angle wide-bandwidth arbitrary-motion synthetic aperture radar[C]. Proceedings Volume 2747, Radar Sensor Technology, Orlando, USA, 1996: 25–36. [4] CUMMING I G and WONG F H. Digital Processing of Synthetic Aperture Radar Imaging Algorithm and Implementation[M]. Beijing: Electronic Industries Press, 2007: 155–191. [5] BREIT H, SCHATTLER B, and STEINBRECHER U. A high precision workstation-based chirp scaling SAR processor[C]. The IEEE International Geoscience and Remote Sensing Symposium, Remote Sensing - A Scientific Vision for Sustainable Development, Singapore, 1997: 465–467. [6] DAI Eryan, JIN Yaqiu, HAMASAKI T, et al. Three-dimensional stereo reconstruction of buildings using polarimetric SAR images acquired in opposite directions[J]. IEEE Geoscience and Remote Sensing Letters, 2008, 5(2): 236–240. doi: 10.1109/LGRS.2008.915744 [7] XU Feng and JIN Yaqiu. Automatic reconstruction of building objects from multiaspect meter-resolution SAR images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2007, 45(7): 2336–2353. doi: 10.1109/TGRS.2007.896614 [8] SCHMITT A. Multiscale and multidirectional multilooking for SAR image enhancement[J]. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(9): 5117–5134. doi: 10.1109/TGRS.2016.2555624 [9] ISHIMARU A, CHAN T K, and KUGA Y. An imaging technique using confocal circular synthetic aperture radar[J]. IEEE Transactions on Geoscience and Remote Sensing, 1998, 36(5): 1524–1530. doi: 10.1109/36.718856 [10] SOUMEKH M. Reconnaissance with slant plane circular SAR imaging[J]. IEEE Transactions on Image Processing, 1996, 5(8): 1252–1265. doi: 10.1109/83.506760 [11] POHL C and VAN GENDEREN J L. Review article multisensor image fusion in remote sensing: Concepts, methods and applications[J]. International Journal of Remote Sensing, 1998, 19(5): 823–854. doi: 10.1080/014311698215748 [12] BYUN Y, CHOI J, and HAN Y. An area-based image fusion scheme for the integration of SAR and optical satellite imagery[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2013, 6(5): 2212–2220. doi: 10.1109/JSTARS.2013.2272773 [13] 徐建平, 皮亦鸣, 曹宗杰. 基于贝叶斯压缩感知的合成孔径雷达高分辨成像[J]. 电子与信息学报, 2011, 33(12): 2863–2868. doi: 10.3724/SP.J.1146.2010.01377XU Jianping, PI Yiming, and CAO Zongjie. SAR imaging based on Bayesian compressive sensing[J]. Journal of Electronics&Information Technology, 2011, 33(12): 2863–2868. doi: 10.3724/SP.J.1146.2010.01377 [14] JI Shihao, XUE Ya, and CARIN L. Bayesian compressive sensing[J]. IEEE Transactions on Signal Processing, 2008, 56(6): 2346–2356. doi: 10.1109/tsp.2007.914345 [15] YANG Jungang, THOMPSON J, HUANG Xiaotao, et al. Random-frequency SAR imaging based on compressed sensing[J]. IEEE Transactions on Geoscience and Remote Sensing, 2013, 51(2): 983–994. doi: 10.1109/TGRS.2012.2204891 [16] 卫扬铠, 曾涛, 陈新亮, 等. 典型线面目标合成孔径雷达参数化成像[J]. 雷达学报, 2020, 9(1): 143–153. doi: 10.12000/JR19077WEI Yangkai, ZENG Tao, CHEN Xinliang, et al. Parametric SAR imaging for typical lines and surfaces[J]. Journal of Radars, 2020, 9(1): 143–153. doi: 10.12000/JR19077 [17] ZENG Tao, WEI Yangkai, DING Zegang, et al. Parametric image reconstruction for edge recovery from synthetic aperture radar echoes[J]. IEEE Transactions on Geoscience and Remote Sensing, 2021, 59(3): 2155–2173. doi: 10.1109/TGRS.2020.3006884 [18] WEN Yuhan, WANG Yan, DING Zegang, et al. Parametric synthetic aperture radar image recovery for multiple linear structures: An image domain approach[J]. Remote Sensing, 2020, 12(12): 1996. doi: 10.3390/rs12121996 [19] 贺思三. 雷达成像中的非理想散射现象分析[D]. [硕士论文], 国防科学技术大学, 2005: 16–24.HE Sisan. Analysis of non-ideal scattering phenomenon in radar imaging[D]. [Master dissertation], National University of Defense Technology, 2005: 16–24. [20] TAKET N D and BURGE R E. A physical optics version of the geometrical theory of diffraction[J]. IEEE Transactions on Antennas and Propagation, 1991, 39(6): 719–731. doi: 10.1109/8.86868 [21] JACKSON J A, RIGLING B D, and MOSES R L. Canonical scattering feature models for 3D and bistatic SAR[J]. IEEE Transactions on Aerospace and Electronic Systems, 2010, 46(2): 525–541. doi: 10.1109/TAES.2010.5461639 [22] FAN Yujie, CHEN Xinliang, WEI Yangkai, et al. The distributed SAR imaging method for cylinder target[C]. IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan, 2019: 2921–2924. doi: 10.1109/IGARSS.2019.8898372 [23] 范宇杰, 温育涵, 卫扬铠, 等. 连续目标双基地调频连续波SAR回波建模方法[J]. 信号处理, 2018, 34(11): 1345–1354. doi: 10.16798/j.issn.1003-0530.2018.11.010FAN Yujie, WEN Yuhan, WEI Yangkai, et al. Continuous target bistatic FMCW SAR echo modeling method[J]. Journal of Signal Processing, 2018, 34(11): 1345–1354. doi: 10.16798/j.issn.1003-0530.2018.11.010 [24] BHALLA R, MOORE J, and LING Hao. A global scattering center representation of complex targets using the shooting and bouncing ray technique[J]. IEEE Transactions on Antennas and Propagation, 1997, 45(12): 1850–1856. doi: 10.1109/8.650204 [25] POTTER L C and MOSES R L. Attributed scattering centers for SAR ATR[J]. IEEE Transactions on Image Processing, 1997, 6(1): 79–91. doi: 10.1109/83.552098 [26] MA Conghui, WEN Gongjian, DING Boyuan, et al. Three-dimensional electromagnetic model-based scattering center matching method for synthetic aperture radar automatic target recognition by combining spatial and attributed information[J]. Journal of Applied Remote Sensing, 2016, 10(1): 016025. doi: 10.1117/1.JRS.10.016025 [27] DING Baiyuan and WEN Gongjian. Target reconstruction based on 3-D scattering center model for robust SAR ATR[J]. IEEE Transactions on Geoscience and Remote Sensing, 2018, 56(7): 3772–3785. doi: 10.1109/TGRS.2018.2810181 [28] AMERICA C O. CST studio suite 2011: Integrating simulation technology[J]. Microwave Journal, 2010, 53(12): 92–96. [29] GERRY M J, POTTER L C, GUPTA I J, et al. A parametric model for synthetic aperture radar measurements[J]. IEEE Transactions on Antennas and Propagation, 1999, 47(7): 1179–1188. doi: 10.1109/8.785750 [30] GAO Yuexin, XING Mengdao, GUO Liang, et al. Extraction of anisotropic characteristics of scattering centers and feature enhancement in wide-angle SAR imagery based on the iterative re-weighted tikhonov regularization[J]. Remote Sensing, 2018, 10(12): 2066. doi: 10.3390/rs10122066 [31] 高悦欣, 李震宇, 盛佳恋, 等. 一种大转角SAR图像散射中心各向异性提取方法[J]. 电子与信息学报, 2016, 38(8): 1956–1961. doi: 10.11999/JEIT151261GAO Yuexin, LI Zhenyu, SHENG Jialian, et al. Extraction method for anisotropy characteristic of scattering center in wide-angle SAR imagery[J]. Journal of Electronics&Information Technology, 2016, 38(8): 1956–1961. doi: 10.11999/JEIT151261 -
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