合成孔径雷达三维成像——从层析、阵列到微波视觉

丁赤飚 仇晓兰 徐丰 梁兴东 焦泽坤 张福博

丁赤飚, 仇晓兰, 徐丰, 等. 合成孔径雷达三维成像——从层析、阵列到微波视觉[J]. 雷达学报, 2019, 8(6): 693–709. doi: 10.12000/JR19090
引用本文: 丁赤飚, 仇晓兰, 徐丰, 等. 合成孔径雷达三维成像——从层析、阵列到微波视觉[J]. 雷达学报, 2019, 8(6): 693–709. doi: 10.12000/JR19090
DING Chibiao, QIU Xiaolan, XU Feng, et al. Synthetic aperture radar three-dimensional imaging ——from TomoSAR and array InSAR to microwave vision[J]. Journal of Radars, 2019, 8(6): 693–709. doi: 10.12000/JR19090
Citation: DING Chibiao, QIU Xiaolan, XU Feng, et al. Synthetic aperture radar three-dimensional imaging ——from TomoSAR and array InSAR to microwave vision[J]. Journal of Radars, 2019, 8(6): 693–709. doi: 10.12000/JR19090

合成孔径雷达三维成像——从层析、阵列到微波视觉

doi: 10.12000/JR19090
基金项目: 国家自然科学基金重大项目“合成孔径雷达微波视觉三维成像理论与应用基础研究”(61991420, 61991421)
详细信息
    作者简介:

    丁赤飚(1969–),男,研究员,博士生导师,现任中国科学院空天信息创新研究院副院长,主要从事合成孔径雷达、遥感信息处理和应用系统等领域的研究工作,先后主持多项国家863重点项目和国家级遥感卫星地面系统工程建设项目,曾获国家科技进步一等奖、二等奖各一项。E-mail: cbding@mail.ie.ac.cn

    仇晓兰(1982–),女,中国科学院空天信息创新研究院研究员,博士生导师,主要研究领域为SAR成像处理、SAR图像理解,IEEE高级会员、IEEE地球科学与遥感快报副主编、雷达学报青年编委。E-mail: xlqiu@mail.ie.ac.cn

    徐丰:徐 丰(1982–),男,复旦大学博士学位,教授,复旦大学电磁波信息科学教育部重点实验室副主任,研究方向为SAR图像解译、电磁散射建模、人工智能。IEEE地球科学与遥感快报副主编、IEEE地球科学与遥感学会上海分会主席。E-mail: fengxu@fudan.edu.cn

    梁兴东(1973–),男,陕西人;北京理工大学博士;中国科学院空天信息创新研究院研究员;研究方向为高分辨率合成孔径雷达系统、干涉合成孔径雷达、成像处理及应用、实时数字信号处理等。E-mail: xdliang@mail.ie.ac.cn

    焦泽坤(1991–),男,博士,现任职于中国科学院空天信息创新研究院,助理研究员,研究方向为SAR 3维成像技术。E-mail: zkjiao@mail.ie.ac.cn

    张福博(1988–),男,副研究员,2015年获得工学博士学位,2016年入选中国科学院电子学研究所优秀人才计划,主要研究方向为微波3维成像技术,解决了超分辨和高相参信号处理难题,发表学术论文十余篇,获得2018年度国家技术发明奖。E-mail: zhangfubo8866@126.com

    通讯作者:

    丁赤飚 cbding@mail.ie.ac.cn

    仇晓兰 xlqiu@mail.ie.ac.cn

  • 责任主编:张群 Corresponding Editor: ZHANG Qun
  • 中图分类号: TN957.52

Synthetic Aperture Radar Three-dimensional Imaging——From TomoSAR and Array InSAR to Microwave Vision (in English)

Funds: The Major Program of National Natural Science Foundation of China “Research on SAR Microwave Vision Three-Dimensional Imaging Theory and Application Fundation”(61991420, 61991421)
More Information
    Author Bio:

    DING Chibiao received a B.S. and Ph. D. degree in electronics engineering from Beihang University, Beijing, China, in 1997. Since 1997, he has been with the Institute of Electronics, Chinese Academy of Sciences, Beijing, where he is currently a Research Fellow and the Vice Director. His research interests include advanced synthetic aperture radar systems, signal processing technology, and information systems. E-mail: cbding@mail.ie.ac.cn

    QIU Xiaolan received a B.S. degree in electronic engineering and information science from the University of Science and Technology of China, Hefei, China, in 2004, and a Doctoral degree in signal and information processing from the Graduate University of Chinese Academy of Sciences, Beijing, in 2009. Since 2009, she has been with the Institute ofElectronics, Chinese Academy of Sciences, Beijing. Her research interests include synthetic aperture radar (SAR) imaging and geo-correction, SAR simulation, and SAR image interpretation. She currently serves as an Associate Editor for the IEEE GEOSCIENCE AND REMOTE SENSING LETTERS. E-mail: xlqiu@mail.ie.ac.cn

    XU Feng (S’06–M’08–SM’14) received a B.E. degree (Hons.) in information engineering from Southeast University, Nanjing, China, in 2003, and a Ph.D. degree (Hons.) in electronic engineering from Fudan University, Shanghai, China, in 2008. From 2008 to 2010, he was a Postdoctoral Fellow with the NOAA Center for Satellite Application and Research (STAR), Camp Springs, MD. From 2010 to 2013, he was with Intelligent Automation Inc., Rockville, while partly working for the NASA Goddard Space Flight Center, Greenbelt, as a Research Scientist. In 2013, he joined Fudan University, where he is currently a professor with the School of Information Science and Technology. E-mail: fengxu@fudan.edu.cn

    LIANG Xingdong received a Ph.D. degree from the Beijing Institute of Technology, Beijing, China, in 2001. Since 2002, he has been with the Institute of Electronics, Chinese Academy of Science, Beijing, where he is currently a Professor of the Science and Technology on Microwave Imaging Laboratory. His research interests include real-time radar signal processing, coherent polarimetric and interferometric SAR systems. E-mail: xdliang@mail.ie.ac.cn

    JIAO Zekun received a B.S. degree in electronic engineering and information science from the University of Science and Technology of China, Hefei, China, in 2014, and a Doctoral degree in signal and information processing from the University of Chinese Academy of Sciences, Beijing, in 2019. Since 2019, he has been with the Institute ofElectronics, Chinese Academy of Sciences, Beijing. His research interests include synthetic aperture radar (SAR) 3D imaging. E-mail: zkjiao@mail.ie.ac.cn

    ZHANG Fubo received a Ph.D. degree from the Institute of Electronics, Chinese Academy of Science, Beijing, China, in 2015. Since 2015, he has been with the Institute of Electronics, Chinese Academy of Science. His research interests include synthetic aperture radar tomography. E-mail: zhangfubo8866@126.com

    Corresponding author: DING Chibiao, cbding@mail.ie.ac.cnQIU Xiaolan, xlqiu@mail.ie.ac.cn
  • 摘要: 合成孔径雷达3维成像技术可以消除目标和地形在2维图像上产生的严重混叠,显著提升目标识别和3维建模能力,已经成为当前SAR发展的重要趋势。合成孔径雷达3维成像技术经过了数十年的发展,已提出多种技术体制。该文系统性回顾了SAR 3维成像技术领域的发展过程,深入分析了现有SAR 3维成像技术的特点;指出了SAR回波及图像中蕴含的未被现有技术利用的3维信息,提出“合成孔径雷达微波视觉3维成像”的新概念和新思路,将SAR成像方法与微波散射机制和图像视觉语义有机融合,形成SAR微波视觉3维成像理论与方法,实现高效能、低成本的SAR 3维成像。该文重点阐述了SAR微波视觉3维成像的概念、目标和关键科学问题,并给出了初步的技术途径,为SAR 3维成像提供了新的技术思路。

     

  • 图  1  TomoSAR 3维成像原理图

    Figure  1.  Diagrammatic sketch of TomoSAR 3D imaging

    图  2  TomoSAR 3维成像几何原理图

    Figure  2.  TomoSAR imaging geometry

    图  3  2010年,德宇航首个城区TomoSAR 3维成像[12]

    Figure  3.  TomoSAR 3D imaging result of urban areas by DLR in 2010[12]

    图  4  极化层析SAR与HoloSAR 3维成像

    Figure  4.  Three-dimensional imaging results of PolTomoSAR and HoloSAR

    图  5  德国联邦铁路公司总部层析成像结果[32]

    Figure  5.  TomoSAR imaging results of DB Headquarters in Munich[32]

    图  6  TerraSAR-X卫星轨道控制精度示意图[33]

    Figure  6.  Diagrammatic sketch of TSX orbit control performance[33]

    图  7  下视阵列3维成像技术示意图

    Figure  7.  Diagrammatic sketch of downward looking array 3D imaging

    图  8  全球阵列SAR 3维成像系统

    Figure  8.  Global array 3D imaging systems

    图  9  阵列干涉SAR 3维成像示意图

    Figure  9.  Diagrammatic sketch of Array InSAR 3D imaging

    图  10  阵列干涉SAR 3维超分辨成像算法原理示意图

    Figure  10.  Diagrammatic sketch of Array InSAR super-resolution imaging algorithm

    图  11  传统成像算法与阵列超分辨算法原理及效果对比

    Figure  11.  Comparison of principles and performances between traditional methods and Array InSAR imaging method

    图  12  空时频多维信号波形编码方案原理示意图

    Figure  12.  Diagrammatic sketch of multidimensional waveform coding

    图  13  多维信号波形正交编码成像结果对比图

    Figure  13.  Comparison between traditional and multidimensional orthogonal waveform imaging results

    图  14  刚柔组合的柔性基线测量和补偿方法原理示意图

    Figure  14.  Diagrammatic sketch of flexible baseline measurement and compensation algorithm

    图  15  中科院电子所阵列干涉SAR 3维成像系统

    Figure  15.  Array InSAR 3D imaging system by IECAS

    图  16  独栋建筑阵列干涉SAR 3维成像结果

    Figure  16.  Array InSAR 3D imaging results of a single building

    图  17  阵列干涉SAR小区场景3维重建结果

    Figure  17.  3D imaging results by array InSAR of the observed scene

    图  18  电磁散射求逆问题示意图

    Figure  18.  Diagrammatic sketch of inverse problem of electromagnetic scattering

    图  19  SAR成像与光学成像的区别

    Figure  19.  Differences between SAR and optical images

    图  20  基于微波视觉的3维成像概念

    Figure  20.  SAR microwave vision 3D imaging

    图  21  SAR图像中某叠掩像素的信号表达式

    Figure  21.  Signal model of the overlapping pixel

    图  1  Diagram of the TomoSAR 3D imaging

    图  2  TomoSAR imaging geometry

    图  3  TomoSAR 3D imaging result of urban areas by DLR[12]

    图  4  Three-dimensional PolTomoSAR and HoloSAR imaging results

    图  5  TomoSAR imaging results of the DB Headquarters in Munich[32]

    图  6  Diagram of the TSX orbit control performance[33]

    图  7  Diagram of the downward-looking array 3D imaging

    图  8  Global array 3D imaging systems

    图  9  Diagram of the array InSAR 3D imaging

    图  10  Diagram of the array InSAR super-resolution imaging algorithm

    图  11  Comparison of principles and performances of traditional methods and the array InSAR imaging method

    图  12  Diagram of the multidimensional waveform coding

    图  13  Comparison between traditional and multidimensional orthogonal waveform imaging results

    图  14  Sketch of the flexible baseline measurement and compensation algorithm

    图  15  Array InSAR 3D imaging system by IECAS

    图  16  array InSAR 3D imaging results of a single building

    图  17  3D imaging results by the array InSAR

    图  18  Sketch of the inverse problem of electromagnetic scattering

    图  19  Differences between the SAR and optical images

    图  20  SAR microwave vision 3D imaging

    图  21  Signal model of the overlapping pixel

    表  1  SAR 微波视觉3维成像与传统成像技术的对比

    Table  1.   Comparison between SAR microwave vision 3D imaging and traditional 3D imaging techniques

    维度 分辨机理 分辨方法 信息来源 雷达体制
    1 距离维 时间分辨 脉冲压缩 频率扩展 传统雷达
    2 方位维 角度分辨 合成孔径 空间扩展 2维雷达成像(SAR)
    3 高度维 角度分辨 合成孔径 空间扩展 层析/阵列干涉SAR 3维成像
    散射机制
    视觉语义
    角度分辨
    SAR微波视觉
    3维成像方法
    散射机制
    视觉内容
    空间扩展
    微波视觉
    3维SAR
    下载: 导出CSV

    表  1  Comparison between SAR microwave vision 3D and traditional 3D imaging techniques

    Dimension Resolution mechanism Processing method Source of information Radar system
    1st Range Time resolution Range compression Frequency expansion Traditional radar
    2nd Azimuth Angular resolution Synthetic aperture Space expansion SAR
    3rd Elevation Angular resolution Synthetic aperture Space expansion TomoSAR and array InSAR
    Scattering mechanism visual semantics angular resolution SAR microwave vision 3D imaging Scattering mechanism Visual information space expansion Microwave vision 3D SAR
    下载: 导出CSV
  • [1] GRAHAM L C. Synthetic interferometer radar for topographic mapping[J]. Proceedings of the IEEE, 1974, 62(6): 763–768. doi: 10.1109/PROC.1974.9516
    [2] LAPRADE G L. An analytical and experimental study of stereo for radar[J]. Photogrammetric Engineering, 1963, 29(2): 294–300.
    [3] LEBERL F W, RAGGAM J, and KOBRICK M. On stereo viewing of SAR images[J]. IEEE Transactions on Geoscience and Remote Sensing, 1985, GE-23(2): 110–117. doi: 10.1109/TGRS.1985.289407
    [4] KNAELL K. Three-dimensional SAR from curvilinear apertures[C]. Proceedings of the 1996 IEEE national Radar Conference, Ann Arbor, USA, 1996.
    [5] SCHMITT M and ZHU Xiaoxiang. Demonstration of single-pass millimeterwave SAR tomography for forest volumes[J]. IEEE Geoscience and Remote Sensing Letters, 2016, 13(2): 202–206. doi: 10.1109/LGRS.2015.2506150
    [6] ZHU Xiaoxiang and BAMLER R. Tomographic SAR inversion by L1-norm regularization—the compressive sensing approach[J]. IEEE Transactions on Geoscience and Remote Sensing, 2010, 48(10): 3839–3846. doi: 10.1109/TGRS.2010.2048117
    [7] ZHU Xiaoxiang and BAMLER R. Very high resolution spaceborne SAR tomography in urban environment[J]. IEEE Transactions on Geoscience and Remote Sensing, 2010, 48(12): 4296–4308. doi: 10.1109/TGRS.2010.2050487
    [8] PASQUALI P, PRATI C, ROCCA F, et al. A 3-D sar experiment with EMSL data[C]. Proceedings of International Geoscience and Remote Sensing Symposium, IGARSS’95, Quantitative Remote Sensing for Science and Applications, Firenze, Italy, 1995: 784–786.
    [9] REIGBER A and MOREIRA A. First demonstration of airborne SAR tomography using multibaseline L-band data[J]. IEEE Transactions on Geoscience and Remote Sensing, 2000, 38(5): 2142–2152. doi: 10.1109/36.868873
    [10] FORNARO G, LOMBARDINI F, and SERAFINO F. Three-dimensional multipass SAR focusing: Experiments with long-term spaceborne data[J]. IEEE Transactions on Geoscience and Remote Sensing, 2005, 43(4): 702–714. doi: 10.1109/TGRS.2005.843567
    [11] FORNARO G, LOMBARDINI F, PAUCIULLO A, et al. Tomographic processing of interferometric SAR data: Developments, applications, and future research perspectives[J]. IEEE Signal Processing Magazine, 2014, 31(4): 41–50. doi: 10.1109/MSP.2014.2312073
    [12] REALE D, FORNARO G, PAUCIULLO A, et al. Tomographic imaging and monitoring of buildings with very high resolution SAR data[J]. IEEE Geoscience and Remote Sensing Letters, 2011, 8(4): 661–665. doi: 10.1109/LGRS.2010.2098845
    [13] ZHU Xiaoxiang and BAMLER R. Demonstration of super-resolution for tomographic SAR imaging in urban environment[J]. IEEE Transactions on Geoscience and Remote Sensing, 2012, 50(8): 3150–3157. doi: 10.1109/TGRS.2011.2177843
    [14] ZHU Xiaoxiang, MONTAZERI S, GISINGER C, et al. Geodetic SAR tomography[J]. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(1): 18–35. doi: 10.1109/TGRS.2015.2448686
    [15] WANG Yuanyuan, ZHU Xiaoxiang, and BAMLER R. An efficient tomographic inversion approach for urban mapping using meter resolution SAR image stacks[J]. IEEE Geoscience and Remote Sensing Letters, 2014, 11(7): 1250–1254. doi: 10.1109/LGRS.2013.2290833
    [16] ZHU Xiaoxiang and BAMLER R. Super-resolution power and robustness of compressive sensing for spectral estimation with application to spaceborne tomographic SAR[J]. IEEE Transactions on Geoscience and Remote Sensing, 2011, 50(1): 247–258.
    [17] ZHU Xiaoxiang and BAMLER R. Superresolving SAR tomography for multidimensional imaging of urban areas: Compressive sensing-based TomoSAR inversion[J]. IEEE Signal Processing Magazine, 2014, 31(4): 51–58. doi: 10.1109/MSP.2014.2312098
    [18] ZHU Xiaoxiang, GE Nan, and SHAHZAD M. Joint sparsity in SAR tomography for urban mapping[J]. IEEE Journal of Selected Topics in Signal Processing, 2015, 9(8): 1498–1509. doi: 10.1109/JSTSP.2015.2469646
    [19] TEBALDINI S and ROCCA F. Multibaseline polarimetric SAR tomography of a boreal forest at P-and L-bands[J]. IEEE Transactions on Geoscience and Remote Sensing, 2012, 50(1): 232–246. doi: 10.1109/TGRS.2011.2159614
    [20] PONCE O, PRATS-IRAOLA P, SCHEIBER R, et al. First airborne demonstration of holographic SAR tomography with fully Polarimetric multicircular acquisitions at L-Band[J]. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(10): 6170–6196. doi: 10.1109/TGRS.2016.2582959
    [21] BI Hui, ZHANG Bingchen, and HONG Wen. Lq regularization-based unobserved baselines’ data estimation method for tomographic synthetic aperture radar inversion[J]. Journal of Applied Remote Sensing, 2016, 10(3): 035014. doi: 10.1117/1.JRS.10.035014
    [22] BI Hui, LIU Jianguo, ZHANG Bingchen, et al. Baseline distribution optimization and missing data completion in wavelet-based CS-TomoSAR[J]. Science China Information Sciences, 2018, 61(4): 042302. doi: 10.1007/s11432-016-9068-y
    [23] WEI Lianhuan, BALZ T, and LIAO Mingsheng. Tomographic analysis of high-rise buildings using TerraSAR-X spotlight data with compressive sensing approach[C]. Proceedings of 2014 IEEE Geoscience and Remote Sensing Symposium, Quebec City, Canada, 2014.
    [24] WANG Jinfeng and PI Yiming. SAR tomography imaging via higher-order spectrum analysis[J]. Journal of Systems Engineering and Electronics, 2009, 20(4): 748–754.
    [25] DONOHO D L. Compressed sensing[J]. IEEE Transactions on Information Theory, 2006, 52(4): 1289–1306. doi: 10.1109/TIT.2006.871582
    [26] BARANIUK R G. Compressive sensing[J]. IEEE Signal Processing Magazine, 2007, 24(4): 181–121.
    [27] BARANIUK R and STEEGHS P. Compressive radar imaging[C]. Proceedings of 2007 IEEE Radar Conference, Boston, USA, 2007: 128-133.
    [28] BUDILLON A, EVANGELISTA A, and SCHIRINZI G. SAR tomography from sparse samples[C]. Proceedings of 2009 IEEE International Geoscience and Remote Sensing Symposium, Cape Town, South Africa, 2009: IV-865–IV-868.
    [29] BUDILLON A, EVANGELISTA A, and SCHIRINZI G. Three-dimensional SAR focusing from multipass signals using compressive sampling[J]. IEEE Transactions on Geoscience and Remote Sensing, 2011, 49(1): 488–499. doi: 10.1109/TGRS.2010.2054099
    [30] REN X Z and SUN F. Tomography SAR imaging strategy based on block-sparse model[J]. Progress in Electromagnetics Research M, 2016, 47: 191–200. doi: 10.2528/PIERM16010904
    [31] WEI Lianhuan, BALZ T, ZHANG Lu, et al. A novel fast approach for SAR tomography: Two-step iterative shrinkage/thresholding[J]. IEEE Geoscience and Remote Sensing Letters, 2015, 12(6): 1377–1381. doi: 10.1109/LGRS.2015.2402124
    [32] SHI Yilei, ZHU Xiaoxiang, and BAMLER R. Nonlocal compressive sensing-based SAR tomography[J]. IEEE Transactions on Geoscience and Remote Sensing, 2019, 57(5): 3015–3024. doi: 10.1109/TGRS.2018.2879382
    [33] YOON Y T, EINEDER M, YAGUE-MARTINEZ N, et al. TerraSAR-X precise trajectory estimation and quality assessment[J]. IEEE Transactions on Geoscience and Remote Sensing, 2009, 47(6): 1859–1868. doi: 10.1109/TGRS.2008.2006983
    [34] DE FLORIO S and D’AMICO S. Optimal Autonomous Orbit Control of a Remote Sensing Spacecraft[M]. AAIA. Spaceflight Mechanics 2009. Univelt, San Diego: AAIA, 2009: 949-968.
    [35] PITZ W and MILLER D. The TerraSAR-X satellite[J]. IEEE Transactions on Geoscience and Remote Sensing, 2010, 48(2): 615–622. doi: 10.1109/TGRS.2009.2037432
    [36] WERMUTH M, HAUSCHILD A, MONTENBRUCK O, et al. TerraSAR-X rapid and precise orbit determination[C]. Proceedings of the 21st International Symposium on Space Flight Dynamics, Toulouse, Frankreich, 2009.
    [37] CALTAGIRONE F. Status, results and perspectives of the Italian Earth Observation SAR COSMO-SkyMed[C]. Proceedings of European Radar Conference, Rome, Italy, 2009: 330–334.
    [38] GIERULL C H. On a concept for an airborne downward-looking imaging radar[J]. International Journal of Electronics and Communications, 1999, 53(6): 295–304.
    [39] GIRET R, JEULAND H, and ENERT P. A study of a 3D-SAR concept for a millimeter wave imaging radar onboard an UAV[C]. The First European Radar Conference, Amsterdam, The Netherlands, 2004: 201–204.
    [40] NOUVEL J, JEULAND H, BONIN G, et al. A Ka band imaging radar: DRIVE on board ONERA motorglider[C]. Proceedings of IEEE International Symposium on Geoscience and Remote Sensing, Denver, USA, 2006: 134–136.
    [41] NOUVEL J F. ONERA DRIVE project[C]. Proceedings of International Radar Conference "Surveillance for a Safer World", Bordeaux, France, 2009: 1–4.
    [42] WEIß M, PETERS O, and ENDER J. First flight trials with ARTINO[C]. Proceedings of the 7th European Conference on Synthetic Aperture Radar, Friedrichshafen, Germany, 2008: 1-4.
    [43] KLARE J, BRENNER A, and ENDER J. Impact of platform attitude disturbances on the 3D imaging quality of the UAV ARTINO[C]. Proceedings of the 7th European Conference on Synthetic Aperture Radar, Friedrichshafen, Germany, 2008.
    [44] WEIß M and GILLES M. Initial ARTINO radar experiments[C]. Proceedings of the 8th European Conference on Synthetic Aperture Radar, Aachen, Germany, 2010: 1–4.
    [45] PENG Xueming, WANG Yanping, HONG Wen, et al. Airborne downward looking sparse linear array 3-D SAR heterogeneous parallel simulation[J]. Remote Sensing, 2013, 5(10): 5304–5329. doi: 10.3390/rs5105304
    [46] 彭学明, 王彦平, 谭维贤, 等. 基于跨航向稀疏阵列的机载下视 MIMO 3D-SAR三维成像算法[J]. 电子与信息学报, 2012, 34(4): 943–949. doi: 10.3724/SP.J.1146.2011.00720

    PENG Xueming, WANG Yanping, TAN Weixian, et al. Airborne downward-looking MIMO 3D-SAR imaging algorithm based on cross-track thinned array[J]. Journal of Electronics&Information Technology, 2012, 34(4): 943–949. doi: 10.3724/SP.J.1146.2011.00720
    [47] ZHANG Fubo, LIANG Xingdong, WU Yirong, et al. 3D surface reconstruction of layover areas in continuous terrain for multi-baseline SAR interferometry using a curve model[J]. International Journal of Remote Sensing, 2015, 36(8): 2093–2112. doi: 10.1080/01431161.2015.1030042
    [48] LI Hang, LIANG Xingdong, ZHANG Fubo, et al. A novel 3-D reconstruction approach based on group sparsity of array InSAR[J]. Scientia Sinica Informationis, 2018, 48(8): 1051–1064. doi: 10.1360/N112017-00023
    [49] WANG Jie, LIANG Xingdong, and CHEN Longyong. MIMO SAR system using digital implemented OFDM waveforms[C]. Proceedings of IEEE International Geoscience and Remote Sensing Symposium, Munich, Germany, 2012: 7428–7431.
    [50] WANG Jie, CHEN Longyong, LIANG Xingdong, et al. Implementation of the OFDM chirp waveform on MIMO SAR systems[J]. IEEE Transactions on Geoscience and Remote Sensing, 2015, 53(9): 5218–5228. doi: 10.1109/TGRS.2015.2419271
    [51] WANG Jie, LIANG Xingdong, DING Chibiao, et al. An improved OFDM chirp waveform used for MIMO SAR system[J]. Science China Information Sciences, 2014, 57(6): 1–9.
    [52] 王杰, 丁赤飚, 梁兴东, 等. 机载同时同频MIMO-SAR系统研究概述[J]. 雷达学报, 2018, 7(2): 220–234. doi: 10.12000/JR17046

    WANG Jie, DING Chibiao, LIANG Xingdong, et al. Research outline of airborne MIMO-SAR system with same time-frequency coverage[J]. Journal of Radars, 2018, 7(2): 220–234. doi: 10.12000/JR17046
    [53] 向茂生, 丁赤飚. 一种基于刚性和柔性基线组合的多基线测量方法[P]. 中国, CN201210512927.4, 2014.
    [54] SPORTOUCHE H, TUPIN F, and DENISE L. Extraction and three-dimensional reconstruction of isolated buildings in urban scenes from high-resolution optical and SAR Spaceborne images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2011, 49(10): 3932–3946. doi: 10.1109/TGRS.2011.2132727
    [55] 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
    [56] ZHANG Yueting, DING Chibiao, QIU Xiaolan, et al. The characteristics of the multipath scattering and the application for geometry extraction in high-resolution SAR images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2015, 53(8): 4687–4699. doi: 10.1109/TGRS.2015.2406793
    [57] 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
    [58] 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
    [59] JACKSON J A and MOSES R L. Synthetic aperture radar 3D feature extraction for arbitrary flight paths[J]. IEEE Transactions on Aerospace and Electronic Systems, 2012, 48(3): 2065–2084. doi: 10.1109/TAES.2012.6237579
    [60] LI Yongchen, XU Feng, and JIN Yaqiu. A complex target reconstruction characterized by canonical scattering objects[C]. Proceedings of IEEE International Geoscience and Remote Sensing Symposium, Beijing, China, 2016: 1278–1280.
    [61] XU Feng, JIN Yaqiu, and MOREIRA A. A preliminary study on SAR advanced information retrieval and scene reconstruction[J]. IEEE Geoscience and Remote Sensing Letters, 2016, 13(10): 1443–1447. doi: 10.1109/LGRS.2016.2590878
    [62] LIU Xiaobai, ZHAO Yibiao, and ZHU Songchun. Single-view 3D scene reconstruction and parsing by attribute grammar[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018, 40(3): 710–725. doi: 10.1109/TPAMI.2017.2689007
    [63] 徐丰, 金亚秋. 从物理智能到微波视觉[J]. 科技导报, 2018, 36(10): 30–44.

    XU Feng and JIN Yaqiu. From the emergence of intelligent science to the research of microwave vision[J]. Science&Technology Review, 2018, 36(10): 30–44.
    [64] 陈健堃, 彭凌霄, 仇晓兰, 等. 基于深度神经网络的SAR建筑目标3维重建方法[J]. 中国科学: 信息科学, 2019, 49(11): 1–20.

    CHEN Jiankun, PENG Lingxiao, QIU Xiaolan, et al. A 3D building reconstruction method for SAR based on deep neural network[J]. Scientia Sinica Informationis, 2019, 49(11): 1–20.
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  • 收稿日期:  2019-09-30
  • 修回日期:  2019-11-04
  • 网络出版日期:  2019-11-25

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