Advertisement

Improving Reliability of Oil Spill Detection Systems Using Boosting for High-Level Feature Selection

  • Geraldo L. B. Ramalho
  • Fátima N. S. de Medeiros
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4633)

Abstract

A major problem in surveillance systems is the occurrence of false alarms which lead people to take wrong actions. Thus, if the false alarm is frequent and occurs mainly due to system misclassification, this system will turn into an unreliable one and briefly out of use.

This paper proposes a classification method to oil spill detection using SAR images. The proposed methodology uses boosting method to minimize misclassification and also reach better generalization in order to reduce false alarms. Different feature sets were applied to single neural network classifiers and its performance were compared to a modified boosting method which provides a high-level feature selection. The experiments show substantial improvement in discriminating SAR images containing oil spots from the look-alike ones.

Keywords

oil slick SAR image neural network feature selection boosting 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Martinez, A., Moreno, V.: An oil spill monitoring system based on SAR images. Spill Science & Technology Bulletin 3(1/2), 65–71 (1996)CrossRefGoogle Scholar
  2. 2.
    Ferrado, G., Bernadini, A., David, M., Meyer-Roux, S., Muellenhoff, O., Perkovic, M., Tarchi, D., Toupozelis, K.: Towards an operational use of space imagery for oil pollution monitoring in the mediterranean basin: A demonstration in the adriatic sea. Marine Pollution Bulletin (2007), doi:10.1016/j.marpolbul.2006.11.022Google Scholar
  3. 3.
    Brekke, C., Solberg, A.H.S.: Oil spill detection by satellite remote sensing. Remote Sensing Environment 95, 1–13 (2005)CrossRefGoogle Scholar
  4. 4.
    Marghany, M.: Radarsat automatic algorithms for detecting coastal oil spill pollution. International Journal of Applied Earth Observation and Geoinformation 3(2), 191–196 (2001)CrossRefGoogle Scholar
  5. 5.
    de Lopes, A., Ramalho, D.F., de Medeiros, F.N.S., Costa, R.C.S., Araújo, R.T.S.: Combining features to improve oil spill classification in SAR images. In: Yeung, D.-Y., Kwok, J.T., Fred, A., Roli, F., de Ridder, D. (eds.) Structural, Syntactic, and Statistical Pattern Recognition. LNCS, vol. 4109, pp. 928–936. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  6. 6.
    Webb, A.R.: Statistical Pattern Recognition, 2nd edn. Wiley, England (2002)MATHGoogle Scholar
  7. 7.
    Topouzelis, K., Karathanassi, V., Pavlakis, P., Rokos, D.: Oil spill detection using RBF neural networks and SAR data. In: Proceedings on ISPRS, pp. 724–729 (2005)Google Scholar
  8. 8.
    Haykin, S.: Redes Neurais, princípios e prática, 2nd edn. Bookman, Porto Alegre (2001)Google Scholar
  9. 9.
    Sun, Y., Liu, Z., Todorovic, S., Li, J.: SAR automatic target recognition using adaboost. In: Proc. SPIE on Technologies and Systems for Defense and Security, vol. 5808, pp. 282–293 (2005)Google Scholar
  10. 10.
    Dzeroski, S., Zenko, B.: Is Combining Classifiers Better than Selection the Best One? Machine Learning 54(3), 255–274 (2004)MATHCrossRefGoogle Scholar
  11. 11.
    Schapire, R.E.: The strength of weak learnability. Machine Learning 5(2), 197–227 (1990)Google Scholar
  12. 12.
    Freund, Y., Schapire, R.E.: A Short Introduction to Boosting. Journal of Japanese Society for Artificial Intelligence 14(5), 771–780 (1999)Google Scholar
  13. 13.
    Yin, X.-C., Liu, C.-P., Han, Z.: Feature combination using boosting. Pattern Recognition Letters 26, 2195–2205 (2005)CrossRefGoogle Scholar
  14. 14.
    Cai, Y.-D., Feng, K.-Y., Lu, W.-C., Chou, K.-C.: Using LogitBoost classifier to predict protein structural classes. Journal of Theoretical Biology 238, 172–176 (2006)CrossRefGoogle Scholar
  15. 15.
    Feng, K.-Y., Cai, Y.-D., Chou, K.-C.: Boosting classifier for predicting protein domain structural class. Biochemical and Biophysical Research Communications 334, 213–217 (2005)CrossRefGoogle Scholar
  16. 16.
    Ramalho, G.L.B., de Medeiros, F.N.S.: Using boosting to improve oil spill detection in SAR images. In: 18th International Conference on Pattern Recognition, vol. 2, pp. 1066–1069 (2006)Google Scholar
  17. 17.
    Freund, Y., Schapire, R.E.: A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting. Journal of Computer and System Sciences 55, 119–139 (1997)MATHCrossRefMathSciNetGoogle Scholar
  18. 18.
    Gomez-Verdejo, V., Ortega-Moral, M., Arenas-García, J., Figueiras-Vidal, A.R.: Boosting by weighting critical and erroneous samples. Neurocomputing 69, 679–685 (2006)CrossRefGoogle Scholar
  19. 19.
    Dettling, M., Bühlmann, P.: Boosting for tumor classification with gene expression data. Bioinformatics 19(9), 1061–1069 (2003)CrossRefGoogle Scholar
  20. 20.
    Friedman, J., Hastie, T., Tibshirani, R.: Additive logistic regression: a statistical view of boosting. The. Annals of Statistics 38(2), 337–374 (2000)CrossRefMathSciNetGoogle Scholar
  21. 21.
    Bühlmann, P.: Boosting Methods: Why They Can be Useful for High-Dimensional Data. In: Proceedings on Distributed Statistical Computing, Vienna (2003)Google Scholar
  22. 22.
    Breiman, L., Friedman, J., Olshen, R., Stone, C.: Classification and Regression Trees. Wadsworth and Brooks/Cole, Montery, CA (1984)MATHGoogle Scholar
  23. 23.
    Calabresi, G., Del Frate, F., Lichtenegger, J., Petrocchi, A.: Neural networks for oil spill detection using ERS-SAR data. Ieee Transactions On Geoscience And Remote Sensing 38(5), 2282–2287 (2000)CrossRefGoogle Scholar
  24. 24.
    Haralick, R.M., Shanmugam, K., Dinstein, I.: Textural Features for Image Classification. IEEE Transactions on Systems, Man, and Cybernetics 3(6), 610–621 (1973)CrossRefGoogle Scholar
  25. 25.
    Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn. Wiley, England (2000)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Geraldo L. B. Ramalho
    • 1
  • Fátima N. S. de Medeiros
    • 1
  1. 1.Image Processing Research Group, Federal University of Ceará, 60455-760 - Fortaleza, CEBrazil

Personalised recommendations