Genetic Algorithm for Oil Spill Automatic Detection from Envisat Satellite Data

  • Maged Marghany
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7972)


The merchant ship collided with a Malaysian oil tanker on May 25, 2010, and spilled 2,500 tons of crude oil into the Singapore Straits. The main objective of this work is to design automatic detection procedures for oil spill in synthetic aperture radar (SAR) satellite data. In doing so the genetic algorithm tool was designed to investigate the occurrence of oil spill in Malaysian coastal waters using ENVISAT ASAR satellite data. The study shows that crossover process, and the fitness function generated accurate pattern of oil slick in SAR data. This shown by 85% for oil spill, 5% look–alike and 10% for sea roughness using the receiver –operational characteristics (ROC) curve. It can therefore be concludedcrossover process, and the fitness function have the main role in genetic algorithm achievement for oil spill automatic detection in ENVISAT ASAR data.


Oil Spill ENVISAT ASAR data Crossover Process Fitness Function Genetic algorithm 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  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., 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., vol. I, 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. (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)MATHCrossRefGoogle 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, 179–219 (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)MATHCrossRefGoogle Scholar
  29. 29.
    Sivanandam, S.N., Deepa, S.N.: Introduction to Genetic Algorithms. Springer, Heidelberg (2008)MATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Maged Marghany
    • 1
  1. 1.Institute of Geospatial Science and Technology (INSTeG)Universiti Teknologi MalaysiaSkudaiMalaysia

Personalised recommendations