A fast, edge-preserving, distance-regularized model with bilateral filtering for oil spill segmentation of SAR images

Abstract

Marine oil spills are among the most significant sources of marine pollution. Synthetic aperture radar (SAR) has been used to improve oil spill observations because of its advantages in oil spill detection and identification. However, speckle noise, weak boundaries, and intensity inhomogeneity often exist in the oil spill regions of SAR imagery, which will seriously affect the accurate identification of oil spills. To enhance marine oil spill segmentation of SAR images, a fast, edge-preserving framework based on the distance-regularized level set evolution (DRLSE) model was proposed. Specifically, a bilateral filter penalty term is designed and incorporated into the DRLSE energy function (BF-DRLSE) to preserve the edges of oil spills, and an adaptive initial box boundary was selected for the DRLSE model to reduce the operation time complexity. Two sets of RadarSat-2 SAR data were used to test the proposed method. The experimental results indicate that the bilateral filtering scheme incorporated into the energy function during level set evolution improved the stability of level set evolution. Compared with other methods, the proposed improved BF-DRLSE algorithm displayed a higher overall segmentation accuracy (97.83%). In addition, using an appropriate initial box boundary for the DRLSE method accelerated the global search process, improved the accuracy of oil spill segmentation, and reduced computational time. Therefore, the results suggest that the proposed framework is effective and applicable for marine oil spill segmentation.

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References

  1. Adler D C, Ko T H, Fujimoto J G. 2004. Speckle reduction in optical coherence tomography images by use of a spatially adaptive wavelet filter. Optics Letters, 29(24): 2 878–2 880, https://doi.org/10.1364/OL.29.002878.

    Article  Google Scholar 

  2. Alves T M, Kokinou E, Zodiatis G, Radhakrishnan H, Panagiotakis C, Lardner R. 2016. Multidisciplinary oil spill modeling to protect coastal communities and the environment of the Eastern Mediterranean Sea. Scientific Reports, 6: 36882, https://doi.org/10.1038/srep36882.

    Article  Google Scholar 

  3. Argenti F, Lapini A, Bianchi T, Alparone L. 2013. A tutorial on speckle reduction in synthetic aperture radar images. IEEE Geoscience and Remote Sensing Magazine, 1(3): 6–35, https://doi.org/10.1109/MGRS.2013.2277512.

    Article  Google Scholar 

  4. Cheng Y C, Li X F, Xu Q, Garcia-Pineda O, Andersen O B, Pichel W G. 2011. SAR observation and model tracking of an oil spill event in coastal waters. Marine Pollution Bulletin, 62(2): 350–363, https://doi.org/10.1016/j.marpolbul.2010.10.005.

    Article  Google Scholar 

  5. Chiau W Y. 2005. Changes in the marine pollution management system in response to the Amorgos oil spill in Taiwan. Marine Pollution Bulletin, 51(8–12): 1 041–1 047, https://doi.org/10.1016/j.marpolbul.2005.02.048.

    Article  Google Scholar 

  6. Cisneros-Montemayor A M, Sumaila U R. 2010. A global estimate of benefits from ecosystem-based marine recreation: potential impacts and implications for management. Journal of Bioeconomics, 12(3): 245–268, https://doi.org/10.1007/s10818-010-9092-7.

    Article  Google Scholar 

  7. de Andrade M L S C, Skeika E, Aires S B K. 2017. Segmentation of the prostate gland in images using prior knowledge and level set method. In: 2017 Workshop of Computer Vision (WVC). IEEE, Natal, Brazil. p.31–36.

    Google Scholar 

  8. Espeseth M M, Brekke C, Jones C E, Holt B, Freeman A. 2020. The impact of system noise in polarimetric SAR imagery on oil spill observations. IEEE Transactions on Geoscience and Remote Sensing, 58(6): 4 194–4 214, https://doi.org/10.1109/TGRS.2019.2961684.

    Article  Google Scholar 

  9. Fawwaz I, Zarlis M, Suherman, Rahmat R F. 2018. The edge detection enhancement on satellite image using bilateral filter. In: The 10th International Conference Numerical Analysis in Engineering. IOP, Banda Aceh, Indonesia. p.012052, https://doi.org/10.1088/1757-899X/308/1/012052.

    Google Scholar 

  10. Ganta R R, Zaheeruddin S, Baddiri N, Rao R R. 2012. Segmentation of oil spill images with illumination-reflectance based adaptive level set model. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 5(5): 1 394–1 402, https://doi.org/10.1109/JSTARS.2012.2201249.

    Article  Google Scholar 

  11. Gautama B G, Longépé N, Fablet R, Mercier G. 2016. Assimilative 2-D Lagrangian transport model for the estimation of oil leakage parameters from SAR images: application to the Montara oil spill. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9(11): 4 962–4 969, https://doi.org/10.1109/JSTARS.2016.2606110.

    Article  Google Scholar 

  12. Jackson C R, Apel J R. 2004. Synthetic Aperture Radar: Marine User’s Manual. 457p. http://www.sarusersmanual.com/. Accessed on 2020-02-24.

  13. Jubai A, Jing B, Yang J. 2006. Combining fuzzy theory and a genetic algorithm for satellite image edge detection. International Journal of Remote Sensing, 27(14): 3 013–3 024, https://doi.org/10.1080/01431160600554371.

    Article  Google Scholar 

  14. Karantzalos K, Argialas D. 2008. Automatic detection and tracking of oil spills in SAR imagery with level set segmentation. International Journal of Remote Sensing, 29(21): 6 281–6 296, https://doi.org/10.1080/01431160802175488.

    Article  Google Scholar 

  15. Li C M, Xu C Y, Gui C F, Fox M D. 2010. Distance regularized level set evolution and its application to image segmentation. IEEE Transactions on Image Processing, 19(12): 3 243–3 254, https://doi.org/10.1109/TIP.2010.2069690.

    Article  Google Scholar 

  16. Li Y, Zhang Y Z, Chen J, Zhang H S. 2014. Improved compact polarimetric SAR quad-pol reconstruction algorithm for oil spill detection. IEEE Geoscience and Remote Sensing Letters, 11(6): 1 139–1 142, https://doi.org/10.1109/lgrs.2013.2288336.

    Article  Google Scholar 

  17. Liu J Y, Zhang Z N, Yang H M. 2015. A variational level set remote sensing SAR image segmentation approach for oil spill detecting based on fuzzy cluster. Applied Mechanics and Materials, 719-720: 1 049–1 055, https://doi.org/10.4028/www.scientific.net/amm.719-720.1049.

    Article  Google Scholar 

  18. Osher S, Sethian J A. 1988. Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations. Journal of Computational Physics, 79(1): 12–49, https://doi.org/10.1016/0021-9991(88)90002-2.

    Article  Google Scholar 

  19. Rischard J F. 2001. High noon: we need new approaches to global problem-solving, fast. Journal of International Economic Law, 4(3): 507–525, https://doi.org/10.1093/jiel/4.3.507.

    Article  Google Scholar 

  20. Routray S, Ray A K, Mishra C. 2018. Image denoising by preserving geometric components based on weighted bilateral filter and curvelet transform. Optik, 159: 333–343, https://doi.org/10.1016/j.ijleo.2018.01.096.

    Article  Google Scholar 

  21. Sethian J A. 1999. Level Set Methods and Fast Marching Methods: Evolving Interfaces in Computational Geometry, Fluid Mechanics, Computer Vision, and Materials Science, 2nd edn. Cambridge University Press, Cambridge, UK. 378p.

    Google Scholar 

  22. Shao Z, Zhai H Y, Liu X Y. 2013. Segmentation of oil spill images based on SFCM and level set methods. Journal of Changchun University of Science and Technology (Natural Science Edition), 36(3–4): 134–137, https://doi.org/10.3969/j.issn.1672-9870.2013.03.040. (in Chinese with English abstract)

    Google Scholar 

  23. Solberg A H S. 2012. Remote sensing of ocean oil-spill pollution. Proceedings of the IEEE, 100(10): 2 931–2 945, https://doi.org/10.1109/JPROC.2012.2196250.

    Article  Google Scholar 

  24. Song D M, Wang B, Chen W M, Wang N, Yu S Y, Ding Y, Liu B, Zhen Z J, Xu M M, Zhang T. 2018. An efficient marine oil spillage identification scheme based on an improved active contour model using fully polarimetric SAR imagery. IEEE Access, 6: 67 959–67 981, https://doi.org/10.1109/access.2018.2876173.

    Article  Google Scholar 

  25. Song H H, Huang B, Zhang K H. 2013. A globally statistical active contour model for segmentation of oil slick in SAR imagery. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 6(6): 2 402–2 409, https://doi.org/10.1109/JSTARS.2013.2255119.

    Article  Google Scholar 

  26. Sussman M, Fatemi E. 1999. An efficient, interface-preserving level set redistancing algorithm and its application to interfacial incompressible fluid flow. SIAM Journal on Scientific Computing, 20(4): 1 165–1 191, https://doi.org/10.1137/S1064827596298245.

    Article  Google Scholar 

  27. Sussman M, Smereka P, Osher S. 1994. A level set approach for computing solutions to incompressible two-phase flow. Journal of Computational Physics, 114(1): 146–159, https://doi.org/10.1006/jcph.1994.1155.

    Article  Google Scholar 

  28. Tang L L, Jiang P, Dai C D, Jackson T J. 1996. Evaluation of smoothing filters suppressing speckle noise on SAR images. Remote Sensing of Environment China, 11(3): 206–211. (in Chinese with English abstract)

    Google Scholar 

  29. Tomasi C, Manduchi R. 1998. Bilateral filtering for gray and color images. In: Sixth International Conference on Computer Vision. IEEE, Bombay, India. p.839–846.

    Google Scholar 

  30. Topouzelis K, Psyllos A. 2012. Oil spill feature selection and classification using decision tree forest on SAR image data. ISPRS Journal of Photogrammetry and Remote Sensing, 68: 135–143, https://doi.org/10.1016/j.isprsjprs.2012.01.005.

    Article  Google Scholar 

  31. Wang W D, Sheng H, Liu S W, Chen Y L, Wan J H, Mao J J. 2019. An edge-preserving active contour model with bilateral filter based on hyperspectral image spectral information for oil spill segmentation. In: 2019 10th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS). IEEE, Amsterdam, Netherlands. p.1–5, https://doi.org/10.1109/WHISPERS.2019.8921042.

    Google Scholar 

  32. Wu Y F, He C J, Yang L, Su M T. 2017. A backscattering-suppression-based variational level-set method for segmentation of SAR oil slick images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10(12): 5 485–5 494, https://doi.org/10.1109/JSTARS.2017.2740979.

    Article  Google Scholar 

  33. Xu B, Cui Y, Zuo B, Yang J, Song J S. 2016. Polarimetric SAR image filtering based on patch ordering and simultaneous sparse coding. IEEE Transactions on Geoscience and Remote Sensing, 54(7): 4 079–4 093, https://doi.org/10.1109/TGRS.2016.2536648.

    Article  Google Scholar 

  34. Yang X, Gao X B, Li J, Han B. 2014. A shape-initialized and intensity-adaptive level set method for auroral oval segmentation. Information Sciences, 277: 794–807, https://doi.org/10.1016/j.ins.2014.03.014.

    Article  Google Scholar 

  35. Yang X, Gao X B, Tao D C, Li X L, Li J. 2015. An efficient MRF embedded level set method for image segmentation. IEEE Transactions on Image Processing, 24(1): 9–21, https://doi.org/10.1109/TIP.2014.2372615.

    Article  Google Scholar 

  36. Zhang G S, Perrie W, Zhang B, Khurshid S, Warner K. 2018. Semi-empirical ocean surface model for compact-polarimetry mode SAR of RADARSAT Constellation Mission. Remote Sensing of Environment, 217: 52–60, https://doi.org/10.1016/j.rse.2018.08.006.

    Article  Google Scholar 

  37. Zhang K H, Zhang L, Song H H, Zhang D. 2013. Reinitialization-free level set evolution via reaction diffusion. IEEE Transactions on Image Processing, 22(1): 258–271, https://doi.org/10.1109/TIP.2012.2214046.

    Article  Google Scholar 

  38. Zhang T, Lü X L, Qian J, Hong J, Li Y. 2016. Bilateral linear SURE-based SAR interferogram filter. Remote Sensing Letters, 7(12): 1 190–1 198, https://doi.org/10.1080/2150704x.2016.1225169.

    Article  Google Scholar 

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Acknowledgment

The authors thank the reviewers and editors for their professional and diligent works.

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Corresponding author

Correspondence to Hui Sheng.

Additional information

Supported by the National Key R&D Program (No. 2017YFC1405600), the National Natural Science Foundation of China (Nos. 41776182, 42076182), and the Natural Science Foundation of Shandong Province (No. ZR2016DM16)

Data Availability Statement

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Wang, W., Sheng, H., Chen, Y. et al. A fast, edge-preserving, distance-regularized model with bilateral filtering for oil spill segmentation of SAR images. J. Ocean. Limnol. (2021). https://doi.org/10.1007/s00343-020-0105-7

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Keyword

  • level sets
  • bilateral filter
  • marine oil spill segmentation
  • synthetic aperture radar (SAR) imagery