Multi-Objective Evolutionary Algorithm for Oil Spill Detection from COSMO-SkeyMed Satellite

  • Maged Marghany
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8584)


This study has demonstrated a design tool for oil spill detection in COSMO-SkyMed satellite data using Multi-Objective Evolutionary Algorithmwhich based on Pareto optimal solutions. The COSMO-SkyMed along the Gulf of Thailand is involved in this study. The study also shows that Multi-Objective Evolutionary Algorithmprovides an accurate pattern of oil slick in COSMO-SkyMed data. This shown by 96% for oil spill, 1% look–alike and 3% for sea roughness using the receiver –operational characteristics (ROC) curve. The MOGA also shows excellent performance in COSMO-SkyMed data. In conclusion, Multi-Objective Evolutionary Algorithmcan be used as an automatic detection tool for oil spill in COSMO-SkyMed satellite data.


Multi-Objective Evolutionary Algorithm COSMO-SkyMed oil spill Pareto optimal solutions Automatic detection 


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  1. 1.
    Adam, J.A.: Specialties: Solar Wings, Oil Spill Avoidance, On-Line Patterns. IEEE Spect. 32, 87–95 (1995)CrossRefGoogle Scholar
  2. 2.
    Aggoune, M.E., Atlas, L.E., Cohn, D.A., El-Sharkawi, M.A., Marks, R.J.: Artificial Neural Networks For Power System Static Security Assessment. IEEE Int. Sym. on Cir. and Syst. Portland, Oregon, pp. 490–494 (1989)Google Scholar
  3. 3.
    Brekke, C., Solberg, A.: Oil Spill Detection by Satellite Remote Sensing. Rem. Sens. of Env. 95, 1–13 (2005)CrossRefGoogle Scholar
  4. 4.
    Fiscella, B., Giancaspro, A., Nirchio, F., Pavese, P., Trivero, P.: Oil Spill Detection Using Marine SAR Images. Int. J. of Rem. Sens. 21, 3561–3566 (2000)CrossRefGoogle Scholar
  5. 5.
    Frate, F.D., Petrocchi, A., Lichtenegger, J., Calabresi, G.: Neural Networks for Oil Spill Detection Using ERS-SAR Data. IEEE Tran. on Geos. and Rem. Sens. 38, 2282–2287 (2000)CrossRefGoogle Scholar
  6. 6.
    Hect-Nielsen, R.: Theory of the Back Propagation Neural Network. In: Proc. of the Int. Joint Conf. on Neu. Net., pp. 593–611. IEEE Press (1989)Google Scholar
  7. 7.
    Marghany, M., Hashim, M.: Comparative algorithms for oil spill detection from multi mode RADARSAT-1 SAR satellite data. In: Murgante, B., Gervasi, O., Iglesias, A., Taniar, D., Apduhan, B.O. (eds.) ICCSA 2011, Part II. LNCS, vol. 6783, pp. 318–329. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  8. 8.
    Marghany, M.: RADARSAT Automatic Algorithms for Detecting Coastal Oil Spill Pollution. Int. J. of App. Ear. Obs. and Geo. 3, 191–196 (2001)CrossRefGoogle Scholar
  9. 9.
    Marghany, M.: RADARSAT for Oil spill Trajectory Model. Env. Mod. and Sof. 19, 473–483 (2004)CrossRefGoogle Scholar
  10. 10.
    Marghany, M., Cracknell, A.P., Hashim, M.: Modification of Fractal Algorithm for Oil Spill Detection from RADARSAT-1 SAR Data. Int. J. of App. Ear. Obs. and Geo. 11, 96–102 (2009)CrossRefGoogle Scholar
  11. 11.
    Marghany, M., Cracknell, A.P., Hashim, M.: Comparison between Radarsat-1 SAR Different Data Modes for Oil Spill Detection by a Fractal Box Counting Algorithm. Int. J. of Dig. Ear. 2, 237–256 (2009)CrossRefGoogle Scholar
  12. 12.
    Marghany, M., Hashim, M., Cracknell, A.P.: Fractal Dimension Algorithm for Detecting Oil Spills Using RADARSAT-1 SAR. In: Gervasi, O., Gavrilova, M.L. (eds.) ICCSA 2007, Part I. LNCS, vol. 4705, pp. 1054–1062. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  13. 13.
    Marghany, M., Hashim, M.: Texture Entropy Algorithm for Automatic Detection of Oil Spill from RADARSAT-1 SAR data. Int. J. of the Phy. Sci. 5, 1475–1480 (2010)Google Scholar
  14. 14.
    Michael, N.: Artificial Intelligence: A guide to Intelligent Systems, 2nd edn. Addison Wesley, Harlow (2005)Google Scholar
  15. 15.
    Migliaccio, M., Gambardella, A., Tranfaglia, M.: SAR Polarimetry to Observe Oil Spills. IEEE Tran. on Geos. and Rem. Sen. 45, 506–511 (2007)CrossRefGoogle Scholar
  16. 16.
    Mohamed, I.S., Salleh, A.M., Tze, L.C.: Detection of Oil Spills in Malaysian Waters from RADARSAT Synthetic Aperture Radar Data and Prediction of Oil Spill Movement. In: Proc. of 19th Asi. Conf. on Rem. Sen., Hong Kong, China, November 23-27, vol. 2, pp. 980–987. Asian Remote Sensing Society, Japan (1999)Google Scholar
  17. 17.
    Provost, F., Fawcett, T.: Robust classification for imprecise environments. Mach. Lear. 42, 203–231 (2001)CrossRefzbMATHGoogle Scholar
  18. 18.
    Samad, R., Mansor, S.B.: Detection of Oil Spill Pollution Using RADARSAT SAR Imagery. In: CD Proc. of 23rd Asi. Conf. on Rem. Sens., Birendra International Convention Centre in Kathmandu, Nepal, November 25-29, Asian Remote Sensing (2002)Google Scholar
  19. 19.
    Skrunes, S., Brekke, C., Eltoft, T.: An Experimental Study on Oil Spill Characterization by Multi-Polarization SAR. In: Proc. European Conference on Synthetic Aperture Radar, Nuremberg, Germany, pp. 139–142 (2012)Google Scholar
  20. 20.
    Topouzelis, K., Karathanassi, V., Pavlakis, P., Rokos, D.: Potentiality of Feed-Forward Neural Networks for Classifying Dark Formations to Oil Spills and Look-alikes. Geo. Int. 24, 179–191 (2009)CrossRefGoogle Scholar
  21. 21.
    Topouzelis, K., Karathanassi, V., Pavlakis, P., Rokos, D.: Detection and Discrimination between Oil Spills and Look-alike Phenomena through Neural Networks. ISPRS J. Photo. Rem. Sens. 62, 264–270 (2007)CrossRefGoogle Scholar
  22. 22.
    Topouzelis, K.N.: Oil Spill Detection by SAR Images: Dark Formation detection, Feature Extraction and Classification Algorithms. Sens. 8, 6642–6659 (2008)CrossRefGoogle Scholar
  23. 23.
    Trivero, P., Fiscella, B., Pavese, P.: Sea Surface Slicks Measured by SAR. Nuo. Cim. 24C, 99–111 (2001)Google Scholar
  24. 24.
    Trivero, P., Fiscella, B., Gomez, F., Pavese, P.: SAR Detection and Characterization of Sea Surface Slicks. Int. J. Rem. Sen. 19, 543–548 (1998)CrossRefGoogle Scholar
  25. 25.
    Velotto, D., Migliaccio, M., Nunziata, F., Lehner, S.: Dual-Polarized TerraSAR-X Data for Oil-Spill Observation. IEEE Trans. Geosci. Remote Sens. 49, 4751–4762 (2011)CrossRefGoogle Scholar
  26. 26.
    Chaiyaratana, N., Zalzala, A.M.S.: Recent developments in evolutionary and genetic algorithms: theory and applications. In: Second International Conference on Genetic Algorithms in Engineering Systems: Innovations and Applications, GALESIA 1997, Glasgow, September 2-4, pp. 270–277 (1997)Google Scholar
  27. 27.
    Kahlouche, S., Achour, K., Benkhelif, M.: Proceedings of the 2002 WSEAS International Conferences, Cadiz, Spain, June 12-16, pp. 1–5 (2002),
  28. 28.
    Gautam, G., Chaudhuri, B.B.: A distributed hierarchical genetic algorithm for efficient optimization and pattern matching. Pattern Recognition Journal 40, 212–228 (2007)CrossRefzbMATHGoogle Scholar
  29. 29.
    Sivanandam, S.N., Deepa, S.N.: Introduction to Genetic Algorithms. Springer, Heidelberg (2008)zbMATHGoogle Scholar
  30. 30.
    Marghany, M.: Genetic Algorithm for Oil Spill Automatic Detection from Envisat Satellite Data. In: Murgante, B., Misra, S., Carlini, M., Torre, C.M., Nguyen, H.-Q., Taniar, D., Apduhan, B.O., Gervasi, O. (eds.) ICCSA 2013, Part II. LNCS, vol. 7972, pp. 587–598. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  31. 31.
    Marghany, M.: Genetic Algorithm for Oil Spill Automatic Detection from Multisar Satellite Data. In: Proceedings of the 34th Asian Conference on Remote Sensing 2013, Bali, Indonesia, October 20-24, pp. SC03-671-SC0-3677 (2013)Google Scholar
  32. 32.
    Zhang, B., Perrie, W., Li, X., Pichel, W.: Mapping sea surface oil slicks using RADARSAT-2 quad-polarization SAR image. Geophys. Res. Lett. 38, L10602 (2011)Google Scholar
  33. 33.
    Zhang, Y., Lin, H., Liu, Q., Hu, J., Li, X., Yeung, K.: Oil-spill monitoring in the coastal waters of Hong Kong and vicinity. Marine Geodesy 35, 93–106 (2012)CrossRefGoogle Scholar
  34. 34.
    Shirvany, R., Chabert, M., Tourneret, J.-Y.: Tourneret: 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, 885–892 (2012)CrossRefGoogle Scholar
  35. 35.
    Trivero, P., Biamino, W., Nirchio, F.: High resolution COSMO - SkyMed SAR images for oil spills automatic detection. In: IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2007, pp. 2–5 (2007)Google Scholar
  36. 36.
    Coello, C.A., Lamont, G.B., Van Veldhuizen, D.A.: Evolutionary algorithms for solving multi-objective problems, 2nd edn. Springer, Berlin (2007)zbMATHGoogle Scholar
  37. 37.
    Yudong, Z., Shuihua, W., Genlin, J., Zhengchao, D.: Genetic Pattern Search and Its Application to Brain Image Classification. Math. Prob. in Eng., 1–8 (2013)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Maged Marghany
    • 1
  1. 1.Institute of Geospatial Science and Technology (INSTeG)Universiti Teknologi MalaysiaSkudai,Johor BahruMalaysia

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