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Oil spill detection based on compact polarimetric eigenvalue decomposition

  • role： First author第一作者
  • Affiliation：

    Faculty of Information Engineering, China University of Geosciences, Wuhan 430074, China

  • Email：xie_gq@163.com
  • Introduction：谢广奇，1992年生，男，硕士研究生，研究方向为极化SAR海洋目标提取。E-mail：xie_gq@163.com
Guangqi XIE,  
  • Affiliation：

    Faculty of Information Engineering, China University of Geosciences, Wuhan 430074, China

Shuai YANG,  
  • Affiliation：

    Faculty of Information Engineering, China University of Geosciences, Wuhan 430074, China

Qihao CHEN,  
  • role： Corresponding author通信作者
  • Affiliation：

    Faculty of Information Engineering, China University of Geosciences, Wuhan 430074, China

  • Email：liuxg318@163.com
  • Introduction：刘修国，1969年生，男，教授，研究方向为遥感影像信息提取与3S集成。E-mail：liuxg318@163.com
Xiuguo LIU*

Resümee

Oil spill is one of the most common causes of marine pollution. Synthetic aperture radar (SAR) can detect marine oil spills due to its all-weather and all-day imaging capability. However, conventional algorithms using single-channel SAR usually confuses oil spills with oil spill lookalikes, making oil spill detection a challenging task. In recent years, polarimetric SAR has been studied to improve oil spill detection performance, particularly with respect to distinguishing oil spills and lookalikes. Compact polarimetric (CP) SAR is one of the most popular trends of polarimetric SAR platforms. Compared with full polarimetric SAR, CP SAR contains abundant polarization information of scattering targets. CP SAR also has large imaging range and its manufacturing cost is considerably low. Therefore, CP SAR is a promising tool for oil spill detection. In this study, we utilized CP HP-mode data and derived three parameters to distinguish oil spills from lookalikes. The three novel parameters are CP entropy (Hc), CP fraction (PFc), and CP pedestal height (PHc), which are conducted from CP eigenvalue decomposition. The scattering type of sea surface is dominated by low entropy scattering. For instance, Bragg scattering from small rough surfaces can be regarded as weakly depolarized. In this vein, CP eigenvalue $\hat I {\gg 1}$ is much higher than $\hat I {\gg 2}$ , which indicates the dominance of Bragg scattering. On the contrary, oil spill weakens the Bragg scattering of the ocean, and the difference between the two eigenvalues is not as high as before. Based on former consideration, Hc is high, PFc is low, and PHc is high, which means that the average scattering is in a depolarized state. The target is considered “random”, and polarization status information is lost. Hence, the aforementioned parameters can be used to detect oil spills. The threshold is determined by Otsu’s method and statistical analysis. The threshold for PFc and PHc was automatically determined by Otsu’s method, and other parameters were statistically analyzed to determine the threshold. In this study, Radarsat-2 and SIR-C/X-SAR data of C-band were used to detect oil spills. The results show that Hc, PFc, and PHc can effectively distinguish oil spills and lookalikes. The performance of all the parameters based on the CP eigenvalue decomposition is excellent in effectively excluding lookalikes, highlighting oil spill areas, and enhancing differences between oil spill and sea clutter. Moreover, the parameters are excellent in effectively maintaining details. CP SAR is promising in polarimetric SAR system because of its ability to detect a wide range of marine oil spills. The parameters derived in this study, which were based on the CP eigenvalue decomposition, can obtain satisfactory results in distinguishing oil spills from lookalikes. The quantitative experiments also confirm that the Bhattacharyya distance between oil spills and sea surface surpasses that of lookalikes, which proves that our method achieves fine oil spill detection.

Schlüsselwort

oil spill detection;compact polarimetric SAR;eigenvalue decomposition;compact polarimetric parameter;Bragg scattering

References

  1. 1.
    Ainsworth T L, Cloude S R and Lee J S. 2002. Eigenvector analysis of polarimetric SAR data//IEEE International Geoscience and Remote Sensing Symposium. Toronto, Ontario, Canada: IEEE: 626–628
  2. 2.
    Brekke C and Solberg A H S. 2005. Oil spill detection by satellite remote sensing. Remote Sensing of Environment, 95(1): 1–13
  3. 3.
    Charbonneau F J, Brisco B, Raney R K, McNairn H, Liu C, Vachon P W, Shang J, DeAbreu R, Champagne C, Merzouki A and Geldsetzer T. 2010. Compact polarimetry overview and applications assessment. Canadian Journal of Remote Sensing, 36(S2): S298–S315
  4. 4.
    Cloude S R, Goodenough D G and Chen H. 2012. Compact decomposition theory. IEEE Geoscience and Remote Sensing Letters, 9(1): 28–32
  5. 5.
    Cloude S R and Pottier E. 1996. A review of target decomposition theorems in radar polarimetry. IEEE Transactions on Geoscience and Remote Sensing, 34(2): 498–518
  6. 6.
    Li Y, Lin H, Zhang Y, et al. 2015. Comparisons of circular transmit and linear receive compact polarimetric SAR features for oil slicks discrimination. Journal of Sensors
  7. 7.
    Lu Y C, Hu C M, Sun S J, Zhang M W, Zhou Y, Shi J and Wen Y S. 2016. Overview of optical remote sensing of marine oil spills and hydrocarbon seepage. Journal of Remote Sensing, 20(5): 1259–1269 (
  8. 8.
    Ma T B and Wang S Y. 2009. Oil detection algorithm based on edge detection. Journal of Remote Sensing, 13(6): 1082–1091 (
  9. 9.
    Migliaccio M, Ferrara G, Gambardella A, Nunziata F and Sorrentino A. 2007. A physically consistent speckle model for marine SLC SAR images. IEEE Journal of Oceanic Engineering, 32(4): 839–847
  10. 10.
    Migliaccio M, Nunziata F, Brown C E, Holt B, Li X F, Pichel W and Shimada M. 2012. Polarimetric synthetic aperture radar utilized to track oil spills. EOS Transactions American Geophysical Union, 93(16): 161–162
  11. 11.
    Migliaccio M, Nunziata F and Buono A. 2015. SAR polarimetry for sea oil slick observation. International Journal of Remote Sensing, 36(12): 3243–3273
  12. 12.
    Migliaccio M, Nunziata F and Gambardella A. 2009. On the co-polarized phase difference for oil spill observation. International Journal of Remote Sensing, 30(6): 1587–1602
  13. 13.
    Minchew B, Jones C E, Holt B. 2012. Polarimetric analysis of backscatter from the deepwater horizon oil spill using l-band synthetic aperture radar. IEEE Transactions on Geoscience and Remote Sensing, 50(10): 3812–3830
  14. 14.
    Nunziata F, Migliaccio M and Gambardella A. 2011. Pedestal height for sea oil slick observation. IET Radar, Sonar and Navigation, 5(2): 103–110
  15. 15.
    Nunziata F, Migliaccio M and Li X F. 2015. Sea oil slick observation using hybrid-polarity SAR architecture. IEEE Journal of Oceanic Engineering, 40(2): 426–440
  16. 16.
    Raney R K. 2007. Hybrid-polarity SAR architecture. IEEE Transactions on Geoscience and Remote Sensing, 45(11): 3397–3404
  17. 17.
    Salberg A B, Rudjord Ø and Solberg A H S. 2012. Model based oil spill detection using polarimetric SAR//Proceedings of 2012 IEEE International Geoscience and Remote Sensing Symposium. Munich, Germany: IEEE.
  18. 18.
    Salberg A B, Rudjord Ø and Solberg A H S. 2014. Oil spill detection in hybrid-polarimetric SAR images. IEEE Transactions on Geoscience and Remote Sensing, 52(10): 6521–6533
  19. 19.
    Shirvany R, Chabert M and Tourneret J Y. 2012. Ship and oil-spill detection using the degree of polarization in linear and hybrid/compact dual-pol SAR. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 5(3): 885–892
  20. 20.
    Skrunes S, Brekke C and Eltoft T. 2014. Characterization of marine surface slicks by radarsat-2 multipolarization features. IEEE Transactions on Geoscience and Remote Sensing, 52(9): 5302–5319
  21. 21.
    Solberg A H S. 2012. Remote sensing of ocean oil-spill pollution. Proceedings of the IEEE, 100(10): 2931–2945
  22. 22.
    Wang W G, Lu F, Wu P and Wang J. 2010. Oil spill detection from polarimetric SAR image. Proceedings of IEEE 10th International Conference on Signal Processing. Beijing, China: IEEE: 832–835
  23. 23.
    Wu Y Q, Ji Y, Shen Y and Zhang Y F. 2012. Marine spill oil SAR image segmentation based on Tsallis entropy and improved Chan Vese model. Journal of Remote Sensing, 16(4): 678–690 (
  24. 24.
    Yang F, Yang J, Yin J J and Song J S. 2015. Spill detection based on polarimetric SAR decomposition models. Journal of Tsinghua University (Science and Technology), 55(8): 854–859 (
  25. 25.
    Yin J J, Yang J, Zhou Z S and Song J S. 2015. The extended Bragg scattering model-based method for ship and oil-spill observation using compact polarimetric SAR. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(8): 3760–3772
  26. 26.
    Zhang B, Li X F, Perrie W, and Garcia-Pineda O. 2017. Compact polarimetric synthetic aperture radar for marine oil platform and slick detection. IEEE Transactions on Geoscience and Remote Sensing, 55(3): 1407–1423
  27. 27.
    Zhang B, Perrie W, Li X F and Pichel W G. 2011. Mapping sea surface oil slicks using RADARSAT-2 quad-polarization SAR image. Geophysical Research Letters, 38(10): L10602
  28. 28.
    Zheng H L, Zhang Y M and Wang Y H. 2015. Oil spill detection based on polarimetric feature SERD. Transactions of Oceanology and Limnology(4): 173–180 (

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