高级搜索

合成孔径雷达海面溢油探测研究进展

李煜 陈杰 张渊智

引用本文: 李煜, 陈杰, 张渊智. 合成孔径雷达海面溢油探测研究进展[J]. 电子与信息学报, 2019, 41(3): 751-762. doi: 10.11999/JEIT180468 shu
Citation:  Yu LI, Jie CHEN, Yuanzhi ZHANG. Progress in Research on Marine Oil Spills Detection Using Synthetic Aperture Radar[J]. Journal of Electronics and Information Technology, 2019, 41(3): 751-762. doi: 10.11999/JEIT180468 shu

合成孔径雷达海面溢油探测研究进展

    作者简介: 李煜: 男,1986年生,讲师,研究方向为遥感图像处理和模式识别;
    陈杰: 男,1973年生,教授,研究方向为合成孔径雷达系统建模和信号处理;
    张渊智: 男,1964年生,研究员,研究方向为微波和光学遥感
    通讯作者: 张渊智,zhangyz@nao.cas.cn
  • 基金项目: 国家重点研发计划(2016YFB0501501),国家自然科学基金(41706201)

摘要: 海洋溢油污染不仅严重威胁海洋生态安全、破坏海岸带环境,而且直接和间接地影响着广大人民群众的生活和健康以及区域社会经济的发展。合成孔径雷达因其具有全天候和高灵敏度的观测能力而成为海面油膜探测的主要手段之一。该文从SAR海面油膜探测的基本原理出发,介绍了单极化、全极化和紧缩极化SAR海面油膜探测技术的国内外最新研究进展,对该技术手段在实际应用中遇到的主要困难和挑战做了深入分析,最后总结展望了该技术未来发展的广阔前景。

English

    1. [1]

      刘鹏. SAR海面溢油检测与识别方法研究[D]. [博士论文], 中国海洋大学, 2012.
      LIU Peng. Research on ocean oil spill detection and recognition[D]. [Ph.D. dissertation], Ocean University of China, 2012: 1–2.

    2. [2]

      朱姝霖. 海上溢油事故的影响及处理分析[J]. 航海, 2011(4): 54–56.
      ZHU Shulin. The influence and treatment analysis of the marine oil spill accident[J]. Navigation, 2011(4): 54–56.

    3. [3]

      Deepwater Horizon oil spill[OL]. https://en.wikipedia.org/wiki/Deepwater_Horizon_oil_spill.

    4. [4]

      罗孝学, 许庭春. 海上溢油事故及其防范[J]. 中国水运: 理论版, 2006, 4(7): 18–19.
      LUO Xiaoxue and XU Tingchun. Marine oil spill accident at sea and its prevention[J]. China Water Transport, 2006, 4(7): 18–19.

    5. [5]

      劳辉. 最近29年我国沿海船舶、码头溢油50吨以上事故统计[J]. 交通环保, 2003, 24(6): 47.
      LAO Hui. Statistics on accidents of over 50 tons of oil spills on ships and wharfs along the coast in recent 29 years[J]. Environmental Protection in Transportation, 2003, 24(6): 47.

    6. [6]

      NOAA. NOAA office of response and restoration, open water oil identification job aid for aerial observation. [OL]. http://response.restoration.noaa.gov/jobaid/orderform, 2016.

    7. [7]

      SUKAWATTANAVIJIT C, CHEN Jie, and ZHANG Hongsheng. GA-SVM algorithm for improving land-cover classification using SAR and optical remote sensing data[J]. IEEE Geoscience & Remote Sensing Letters, 2017, 14(3): 284–288. doi: 10.1109/LGRS.2016.2628406

    8. [8]

      LIU Lin and ZHANG Yuanzhi. Urban heat island analysis using the landsat TM data and ASTER data: A case study in Hong Kong[J]. Remote Sensing, 2011, 3(7): 1535–1552. doi: 10.3390/rs3071535

    9. [9]

      ZHANG Yuanzhi, PULLIAINEN J, KOPONEN S, et al. Application of an empirical neural network to surface water quality estimation in the Gulf of Finland using combined optical data and microwave data[J]. Remote Sensing of Environment, 2002, 81(2): 327–336. doi: 10.1016/S0034-4257(02)00009-3

    10. [10]

      Jones C , Holt B. Experimental L-Band Airborne SAR for Oil Spill Response at Sea and in Coastal Waters[J]. Sensors, 2018, 18(2): 641–106. doi: 10.3390/s18020641

    11. [11]

      GAUTHIER M, WEIR L, OU Z, et al. Integrated satellite tracking of pollution: A new operational program[C]. IEEE International Geoscience & Remote Sensing Symposium, Barcelona, Spain, 2007: 967–970.

    12. [12]

      刘康炜, 杨文玉. 海上溢油监测技术研究进展[J]. 安全、健康和环境, 2012, 12(7): 1–3. doi: 10.3969/j.issn.1672-7932.2012.07.002

    13. [13]

      CHEN Jie, IQBAL M, YANG Wei, et al. Mitigation of azimuth ambiguities in spaceborne stripmap SAR images using selective restoration[J]. IEEE Transactions on Geoscience & Remote Sensing, 2014, 52(7): 4038–4045. doi: 10.1109/TGRS.2013.2279109

    14. [14]

      BAMLER R. Principles of synthetic aperture radar[J]. Surveys in Geophysics, 2001, 21(2-3): 147–157. doi: 10.1023/A:1006790026612

    15. [15]

      ARISTOTLE. Problematica Physica[M].Leiden, Koninklijke Brill NV, 2015.

    16. [16]

      MARANGONI C. Sul principio della viscosita superficiale dei liquidi stabili[J]. Nuovo Cimento, 1872, 5-6(1): 239–273. doi: 10.1007/BF02718643

    17. [17]

      SOLBERG A H S. Remote sensing of ocean oil-spill pollution[J]. Proceedings of the IEEE, 2012, 100(10): 2931–2945. doi: 10.1109/JPROC.2012.2196250

    18. [18]

      SOLBERG AHS, STORVIK G, SOLBERG R, et al. Automatic detection of oil spills in ERS SAR images[J]. IEEE Transactions on Geoscience and Remote Sensing, 1999, 37(4): 1916–1924. doi: 10.1109/36.774704

    19. [19]

      MIGLIACCIO M, FERRARA G, GAMBARDELLA A, et al. A new stochastic model for oil spill observation by means of single-look SAR data[J]. Environmental Research, Engineering and Management, 2007, 1(39): 24–29. doi: 10.1109/BALTIC.2006.7266181

    20. [20]

      SHU Yuanming, LI J, and YOUSIF H. Dark-spot detection from SAR intensity imagery with spatial density thresholding for oil-spill monitoring[J]. Remote Sensing of Environment, 2010, 114(9): 2026–2035. doi: 10.1016/j.rse.2010.04.009

    21. [21]

      BARNI M, BETTI M, and MECOCCI A. A fuzzy approach to oil spill detection on SAR images[J]. IEEE International Geoscience and Remote Sensing Symposium, 1995, 1(I): 157–159. doi: 10.1109/IGARSS.1995.519676

    22. [22]

      MERCIER G, DERRODE S, PIECZYNSKI W, et al. Multiscale oil slick segmentation with Markov Chain Model[C]. IEEE International Geoscience and Remote Sensing Symposium, Toulouse, France, 2003: 3501–3503.

    23. [23]

      HUANG Bo, LI Hongga, and HUANG X. A level set method for oil slick segmentation in SAR images[J]. International Journal of Remote Sensing, 2005, 26(6): 1145–1156. doi: 10.1080/01431160512331326747

    24. [24]

      ZHANG Yuanzhi, LIN Hui, LIU Qiang, et al. Oil-spill monitoring in the coastal waters of Hong Kong and vicinity[J]. Marine Geodesy, 2012, 35(1): 93–106. doi: 10.1080/01490419.2011.637872

    25. [25]

      SOLBERG A, DOKKEN S T and SOLBERG R, Automatic detection of oil spills in ENVISAT, Radarsat and ERS SAR images[C]. 2003 IEEE International Geoscience and Remote Sensing Symposium. Proceedings, Toulouse, 2003, 4: 2747–2749.

    26. [26]

      FISCELLA B, GIANCASPRO A, NIRCHIO F, et al. Oil spill detection using marine SAR images[J]. International Journal of Remote Sensing, 2000, 21(18): 3561–3566. doi: 10.1080/014311600750037589

    27. [27]

      DEL FRATE F, PETROCCHI A, LICHTENEGGER J, et al. Neural networks for oil spill detection using ERS-SAR data[J]. IEEE transactions on geoscience and remote sensing, 2000, 38(5): 2282–2287. doi: 10.1109/IGARSS.1999.773451

    28. [28]

      TOPOUZELIS K, KARATHANASSI V, PAVLAKIS P, et al. Detection and discrimination between oil spills and look-alike phenomena through neural networks[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2007, 62(4): 264–270. doi: 10.1016/j.isprsjprs.2007.05.003

    29. [29]

      NIRCHIO F, SORGENTE M, GIANCASPRO A, et al. Automatic detection of oil spills from SAR images[J]. International Journal of Remote Sensing, 2005, 26(6): 1157–1174. doi: 10.1080/01431160512331326558

    30. [30]

      KERAMITSOGLOUA I, CARTALISA C, and KIRANOUDIS C. Automatic identification of oil spills on satellite images[J]. Environmental Modeling & Software, 2006, 21(5): 640–652. doi: 10.1016/j.envsoft.2004.11.010

    31. [31]

      GAMBARDELLA A, GIACINTO G, and MIGLIACCIO M. On the mathematical formulation of the SAR oil-spill observation problem[C], IEEE International Geoscience and Remote Sensing Symposium, Boston, USA, 2008: 1382–1385.

    32. [32]

      MARGHANY M, CRACKNELL A, and HASHIM M. Modification of fractal algorithm for oil spill detection from RADARSAT-1 SAR data[J]. International Journal of Applied Earth Observation and Geoinformation, 2009, 11(2): 96–102. doi: 10.1016/j.jag.2008.09.002

    33. [33]

      GARCIA-PINEDA O, MACDONALD I R, LI Xiaofeng, et al. Oil spill mapping and measurement in the gulf of mexico with textural classifier neural network algorithm (TCNNA)[J]. Selected Topics in Applied Earth Observations and Remote Sensing, 2013, 6(6): 2517–2525. doi: 10.1109/JSTARS.2013.2244061

    34. [34]

      MARGHANY M. Genetic algorithm for oil spill automatic detection from Envisat satellite data[C]. Computational Science and Its Applications - ICCSA, Ho Chi Minh City, Vietnam, 2013: 587–598.

    35. [35]

      BROWN C E, and FINGAS M F. Synthetic Aperture Radar sensors: viable for marine oil spill response[C]. Arctic and Marine Oil spill Program, Canada, 2003: 299–310.

    36. [36]

      MIGLIACCIO M, GAMBARDELLA A, and TRANFAGLIA M. SAR polarimetry to observe oil spills[J]. IEEE Transactions on Geoscience & Remote Sensing, 2007, 45(2): 506–511. doi: 10.1109/TGRS.2006.888097

    37. [37]

      MIGLIACCIO M, and TRANFAGLIA M. Study on the use of SAR polarimetric data to observe oil spills[C]. Europe Oceans 2005, Brest, France, 2005: 196–200.

    38. [38]

      MIGLIACCIO M, FERRARA G, GAMBARDELLA A, et al. A new stochastic model for oil spill observation by means of single-look SAR data[J]. Environmental Engineering and Management Journal, 2007, 1(39): 24–29. doi: 10.1109/BALTIC.2006.7266181

    39. [39]

      NUNZIATA F, MIGLIACCIO M, and GAMBARDELLA A. Pedestal height for sea oil slick observation[J]. Radar, Sonar & Navigation, 2011, 5(2): 103–110. doi: 10.1049/iet-rsn.2010.0092

    40. [40]

      MIGLIACCIO M, NUNZIATA F, and GAMBARDELLA A. On the copolarized phase difference for oil spill observation[J]. International Journal of Remote Sensing, 2009, 30(6): 1587–1602. doi: 10.1080/01431160802520741

    41. [41]

      SKRUNES S, BREKKE C, and ELTOFT T. An experimental study on oil spill characterization by multi-polarization SAR[C]. 9th European Conference on Synthetic Aperture Radar, Nuremberg, Germany, 2012: 139–142.

    42. [42]

      MELSHELMER C, ALPERS W, and GADE M. Investigation of multifrequency/multipolarization radar signatures of rain cells, derived from SIR-C/X-SAR data[C]. Geoscience and Remote Sensing Symposium, Lincoln, USA 1996: 1370–1372.

    43. [43]

      MINCHEW B, JONES C E, and HOLT B. Polarimetric analysis of backscatter from the deepwater Horizon oil spill using L-Band synthetic aperture radar[J]. IEEE Transactions on Geoscience and Remote Sensing, 2012, 50(10): 3812–3830. doi: 10.1109/TGRS.2012.2185804

    44. [44]

      NUNZIATA F, SOBIESKI P, and MIGLIACCIO M. The Two-Scale BPM scattering model for sea biogenic slicks contrast[J]. IEEE Transactions on Geoscience and Remote Sensing, 2009, 47(7): 1949–1956. doi: 10.1109/TGRS.2009.2013135

    45. [45]

      田维. 海面油膜雷达遥感检测机理与方法研究[D]. [博士论文], 中国科学院遥感应用研究所, 2009.

    46. [46]

      LI Yu, ZHANG Yuanzhi, CHEN Jie, et al. Model-based sea surface scattering analysis for the DWH oil spill accident case[C]. Geoscience and Remote Sensing Symposium, Beijing, China, 2016: 7711–7714.

    47. [47]

      HANJSEK I, POTTIER E, and CLOUDE S R. Inversion of surface parameters from polarimetric SAR[J]. Geoscience and Remote Sensing, IEEE Transactions on, 2003, 41(4): 727–744. doi: 10.1109/TGRS.2003.810702

    48. [48]

      WANG Wenguang, LU Fei, WU Peng, et al. Oil spill detection from polarimetric SAR image[J]. Proc. Int. Conf. Signal Process, 2010: 832–835. doi: 10.1109/ICOSP.2010.5655943

    49. [49]

      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): 415–421. doi: 10.1029/2011GL047013

    50. [50]

      SKRUNES S, BREKKE C, JONES C E, et al. A multisensor comparison of experimental oil spills in polarimetric SAR for high wind conditions[J]. IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing, 2016, 9(11): 4948–4961. doi: 10.1109/JSTARS.2016.2565063

    51. [51]

      CHEN Jie and QUEGAN S. Calibration of spaceborne CTLR compact polarimetric low-frequency SAR using mixed radar calibrators[J]. IEEE Transactions on Geoscience & Remote Sensing, 2011, 49(7): 2712–2723. doi: 10.1109/TGRS.2011.2109065

    52. [52]

      RANEY R K. Hybrid-Polarity SAR architecture[J]. IEEE Transactions on Geoscience & Remote Sensing, 2007, 45(11): 3397–3404. doi: 10.1109/TGRS.2007.895883

    53. [53]

      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 & Remote Sensing, 2009, 64(5): 464–471. doi: 10.1016/j.isprsjprs.2008.12.008

    54. [54]

      LAVALLE M, POTTIER E, SOLIMINI D, et al. Compact polarimetric SAR Interferometry: PALSAR observations and associated reconstruction algorithms[C]. Workshop on Science and Applications of SAR Polarimetry and Polarimetric, Frascati, Italy, 2009: 26–30.

    55. [55]

      SOUYRIS JC, STACY N, AINSWORTH T, et al. SAR Compact Polarimetry (CP) for earth observation and planetology: Concept and challenges[C]. Proceedings of International Workshop on Science & Applications of Sar Polarimetry & Polarimetric Interferometry, Noordwijk, Netherlands, 2007: 22–26.

    56. [56]

      NORD M E, AINSWORTH T L, et al. Comparison of compact polarimetric synthetic aperture radar modes[J]. IEEE Transactions on Geoscience & Remote Sensing, 2009, 47(1): 174–188. doi: 10.1109/TGRS.2008.20009

    57. [57]

      YIN Junjun, YANG Jian, and ZHANG Xinzheng. On the ship detection performance with compact polarimetry[C]. IEEE Radar Conference, Kansas City, USA, 2011: 675–680.

    58. [58]

      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 & Remote Sensing, 2012, 51(1): 591–600. doi: 10.1109/TGRS.2012.2199760

    59. [59]

      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 & Remote Sensing, 2017, 55(3): 1407–1423. doi: 10.1109/TGRS.2016.2623809

    60. [60]

      LI Yu, ZHANG Yuanzhi, CHEN Jie, et al. Improved compact polarimetric SAR Quad-Pol reconstruction algorithm for oil spill detection[J]. IEEE Geoscience & Remote Sensing Letters, 2014, 11(6): 1139–1142. doi: 10.1109/LGRS.2013.2288336

    61. [61]

      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]. Selected Topics in Applied Earth Observations and Remote Sensing, 2012, 5(3): 885–892. doi: 10.1109/JSTARS.2012.2182760

    62. [62]

      CLOUDE S R, GOODENOUGH D G, and CHEN H. Compact decomposition theory[J]. Geoscience and Remote Sensing Letters, 2011, 9(1): 28–32. doi: 10.1109/LGRS.2011.2158983

    63. [63]

      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 & Remote Sensing, 2013, 51(6): 3299–3305. doi: 10.1109/TGRS.2012.2224119

    64. [64]

      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 & Remote Sensing, 2015, 8(8): 3751–3759. doi: 10.1109/JSTARS.2014.2348173

    65. [65]

      TRUONG-LOI M, DUBOIS-FERNANDEZ P, FREEMAN A and POTTIER E, The conformity coefficient or how to explore the scattering behaviour from compact polarimetry mode[C]. 2009 IEEE Radar Conference, Pasadena, CA, 2009: 1-6.

    66. [66]

      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 & Remote Sensing, 2015, 8(8): 3760–3772. doi: 10.1109/JSTARS.2014.2359141

    67. [67]

      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

    68. [68]

      LI Yu, LIN Hui, ZHANG Yuanzhi, et al. Comparisons of circular transmit and linear receive compact polarimetric SAR features for oil slicks discrimination[J]. Journal of Sensors, 2015, 2015(99): 1–14. doi: 10.1155/2015/631561

    69. [69]

      ZHANG Yuanzhi, LI Yu, LIANG X, 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

    70. [70]

      KUMAR L J V, KISHORE J K, and RAO P K. Decomposition methods for detection of oil spills based on Risat-1 SAR images[J]. Remote Sens. Geosci, 2014, 3(4): 1–10.

    71. [71]

      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 & Remote Sensing, 2016, 54(10): 5862–5874. doi: 10.1109/TGRS.2016.2574561

    72. [72]

      ALPERS W, HOLT B, and ZENG K. Oil spill detection by imaging radars: Challenges and pitfalls[J]. In Remote Sensing of Environment, 2017, 201(2017): 133–147. doi: 10.1016/j.rse.2017.09.002

    73. [73]

      CHEN Jie and QUEGAN S. Improved estimators of faraday rotation in spaceborne polarimetric SAR data[J]. IEEE Geoscience & Remote Sensing Letters, 2010, 7(4): 846–850. doi: 10.1109/LGRS.2010.2047002

    74. [74]

      RANEY R, Hybrid-Polarity SAR Architecture[C]. 2006 IEEE International Symposium on Geoscience and Remote Sensing, Denver, CO, 2006: 3846-3848. doi:

    75. [75]

      CHEN Guandong, LI Yu, SUN Guangmin, et al. Application of deep networks to oil spill detection using polarimetric Synthetic Aperture Radar Images[J]. Applied Sciences, 2017, 7(10): 968. doi: 10.3390/app7100968

    1. [1]

      闵林, 王宁, 毋琳, 李宁, 赵建辉. 基于多源雷达遥感技术的黄河径流反演研究. 电子与信息学报, 2020, 42(7): 1590-1598.

    2. [2]

      王一宾, 裴根生, 程玉胜. 基于标记密度分类间隔面的组类属属性学习. 电子与信息学报, 2020, 42(5): 1179-1187.

    3. [3]

      张斌, 吴浩明. 一种面向连接的快速多维包分类算法. 电子与信息学报, 2020, 42(6): 1526-1533.

    4. [4]

      张天骐, 胡延平, 冯嘉欣, 张晓艳. 基于零空间矩阵匹配的极化码参数盲识别算法. 电子与信息学报, 2020, 41(0): 1-7.

    5. [5]

      李根, 马彦恒, 侯建强, 徐公国. 基于子孔径Keystone变换的曲线轨迹大斜视SAR回波模拟. 电子与信息学报, 2020, 41(0): 1-8.

    6. [6]

      吴袁超, 吕容川, 李一楠, 李浩, 卢海梁, 罗丰, 李青侠, 窦昊锋. 基于截断奇异值的镜像综合孔径亮温重建方法. 电子与信息学报, 2020, 42(0): 1-6.

    7. [7]

      李一楠, 张林让, 卢海梁, 李鹏飞, 吕容川, 李浩, 付庸杰, 邱尔雅, 唐世阳. 基于地基综合孔径微波辐射计的空中目标无源探测技术研究. 电子与信息学报, 2020, 41(0): 1-8.

    8. [8]

      陈根华, 陈伯孝. 复杂多径信号下基于空域变换的米波雷达稳健测高算法. 电子与信息学报, 2020, 42(5): 1297-1302.

    9. [9]

      刘新, 阎焜, 杨光耀, 叶盛波, 张群英, 方广有. UWB-MIMO穿墙雷达三维成像与运动补偿算法研究. 电子与信息学报, 2020, 41(0): 1-8.

    10. [10]

      项厚宏, 陈伯孝, 杨婷, 杨明磊. 基于多帧相位增强的米波雷达低仰角目标DOA估计方法. 电子与信息学报, 2020, 42(7): 1581-1589.

    11. [11]

      周宝亮. 分布式相参雷达LFM宽带去斜参数估计方法. 电子与信息学报, 2020, 42(7): 1566-1572.

    12. [12]

      刘汝卿, 蒋衍, 姜成昊, 李锋, 朱精果. 应用于激光雷达信号处理系统的放大电路接口设计. 电子与信息学报, 2020, 42(7): 1636-1642.

    13. [13]

      孙闽红, 丁辰伟, 张树奇, 鲁加战, 邵鹏飞. 基于统计相关差异的多基地雷达拖引欺骗干扰识别. 电子与信息学报, 2020, 42(0): 1-7.

    14. [14]

      吕晓德, 孙正豪, 刘忠胜, 张汉良, 刘平羽. 基于二阶统计量盲源分离算法的无源雷达同频干扰抑制研究. 电子与信息学报, 2020, 42(5): 1288-1296.

    15. [15]

      张坤, 水鹏朗, 王光辉. 相参雷达K分布海杂波背景下非相干积累恒虚警检测方法. 电子与信息学报, 2020, 42(7): 1627-1635.

    16. [16]

      黄俊生, 苏洪涛. 二维相控阵-MIMO雷达联合发射子阵划分和波束形成设计方法. 电子与信息学报, 2020, 42(7): 1557-1565.

    17. [17]

      全英汇, 高霞, 沙明辉, 陈侠达, 李亚超, 邢孟道, 岳超良. 基于期望最大化算法的捷变频联合正交频分复用雷达高速多目标参数估计. 电子与信息学报, 2020, 42(7): 1611-1618.

  • 图 1  墨西哥湾“深水地平线”溢油事故[3]

    图 2  SAR成像几何示意图

    图 4  包含溢油区域的汕尾附近海域SAR后向散射VV通道图像(图像来自欧空局)

    图 3  雷达信号海面散射示意图

    表 1  常用单极化SAR油膜特征

    强度特征形态学特征纹理特征*环境特征
    油膜后向散射强度(${\mu _{{\rm{obj}}}}$) 面积(A) 同质性(Homogeneity) 距海岸距离
    油膜后向散射方差(${\sigma _{{\rm{obj}}}}$) 周长(P ) 对比度(Contrast) 距最近黑斑距离
    油膜周围后向散射(${\mu _{{\rm{sce}}}}$) 复杂度(C ) 差异度(Dissimilarity) 周围黑斑数量
    灰度比(${\mu _{{\rm{obj}}}}/{\mu _{{\rm{sce}}}}$) 不对称性 熵(Entropy) 周围船只数量
    方差比(${\sigma _{{\rm{obj}}}}/{\sigma _{{\rm{sce}}}}$) 欧拉数 均值(Mean)
    ISRI(${\mu _{{\rm{obj}}}}/{\sigma _{{\rm{obj}}}}$) 形状指数 方差(Variance)
    ISRO(${\mu _{{\rm{obj}}}}/{\sigma _{{\rm{sce}}}}$) 轴线长度 相关性(Correlation)
    油膜最小灰度值(MSV) 紧致度
    最大对比度(${\sigma _{{\rm{sce}}}}$-MSV)
    边缘梯度
    注:纹理特征通过灰度共生矩阵(Gray-Level Co-occurrence Matrix, GLCM)得到
    下载: 导出CSV
  • 加载中
图(4)表(1)
计量
  • PDF下载量:  51
  • 文章访问数:  757
  • HTML全文浏览量:  217
文章相关
  • 通讯作者:  张渊智, zhangyz@nao.cas.cn
  • 收稿日期:  2018-05-06
  • 录用日期:  2018-11-15
  • 网络出版日期:  2018-12-17
  • 刊出日期:  2019-03-01
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

/

返回文章