Construction and Application of Marine Oil Spill Gravity Vector Differences Detection Model

  • Weiguang Su
  • Bo Ping
  • Fenzhen Su
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8157)

Abstract

This paper proposes a new marine oil spill gravity vector differences detection model based on scalability or viscosity of the oil and water. The model used the median filtering, zero pixels elimination, image normalization, nonlinear transformation, and brought in the law of gravity. The research was upon two oil spill incidents which occurred on the Mediterranean Sea in 2004 and the Gulf of Mexico in 2006. Based on the MODIS remote sensing data, we executed the model to detect the two incidents and compared the results with the results of Sobel detection algorithm. The experimental results illustrated that the model introduced in this paper is superior to Sobel detection algorithm. The proposed model is powerful in oil spill detection.

Keywords

marine oil spill gravity vector differences 

References

  1. 1.
    Fiscella, B., Giancaspro, A., Nirchio, F.: Oil Spill Detection Using Marine SAR Images. International Journal of Remote Sensing 21, 3561–3566 (2000)CrossRefGoogle Scholar
  2. 2.
    Solberg, A.H.S., Storvik, G., Solberg, R.: Automatic Detection of Oil Spills in ERS SAR Images. IEEE Transactions on Geoscience and Remote Sensing 4, 1916–1924 (1999)CrossRefGoogle Scholar
  3. 3.
    Solberg, A.H.S., Brekke, C., Husoy, P.O.: Oil Spill Detection in Radarsat and Envisat SAR Images. IEEE Transactions on Geoscience and Remote Sensing 45, 746–755 (2007)CrossRefGoogle Scholar
  4. 4.
    Kanaa, T.F.N., Tonye, E., Mereier, G.: Detection of Oil Slick Signatures in SAR Images by Fusion of Hysteresis Thresholding Responses. In: International Geoscience and Remote Sensing Symposium, vol. 4, pp. 2750–2752 (2003)Google Scholar
  5. 5.
    Mera, D., Cotos, J.M., Varela-Pet, J.: Adaptive Thresholding Algorithm Based on SAR Images and Wind Data to Segment Oil Spills Along the Northwest Coast of the Iberian Peninsula. Marine Pollution Bulletin 64(10), 2090–2096 (2012)CrossRefGoogle Scholar
  6. 6.
    Topouzelis, K., Karathanassi, V., Pavlakis, P.: Detection and Discrimination Between Oil Spills and Look-alike Phenomena Through Neural Networks. ISPRS Journal of Photogrammetry and Remote Sensing 62, 264–270 (2007)CrossRefGoogle Scholar
  7. 7.
    Maged, M.: RADARSAT Automatic Algorithms for Detecting Coastal Oil Spill Pollution. International Journal of Applied Earth Observation and Geoinformation 3, 191–196 (2001)CrossRefGoogle Scholar
  8. 8.
    Poonam, M.B., Sonali, P.: Oil Spill Detection in SAR Images Using Texture Entropy Algorithm and Mahalanobis Classifier. International Journal of Engineering Science and Technology 4(12), 4823–4826 (2012)Google Scholar
  9. 9.
    Wu, S.Y., Liu, A.K.: Towards an Automated Ocean Feature Detection, Extraction and Classification Scheme for SAR imagery. International Journal of Remote Sensing 5, 935–951 (2003)CrossRefGoogle Scholar
  10. 10.
    Derrode, S., Mercier, G.: Unsupervised Multiscale Oil Slick Segmentation From SAR Images Using a Vector HMC Model. Pattern Recognition 40(3), 1135–1147 (2007)CrossRefMATHGoogle Scholar
  11. 11.
    Liu, A.K., Wu, S.Y., Tseng, W.Y.: Wavelet Analysis of SAR Images for Coastal Monitoring. Canadian Journal of Remote Sensing 26, 494–500 (2000)Google Scholar
  12. 12.
    Hu, C.M., Li, X.F., William, G.P.: Detection of Natural Oil Slicks in the NW Gulf of Mexico Using MODIS Imagery. Geophysical Research Letters 36(1) (2009)Google Scholar
  13. 13.
    Srivastava, H., Singh, T.P.: Assessment and Development of Algorithms to Detection of Oil Spills Using MODIS Data. J. Indian Soc. Remote Sens. 38, 161–167 (2010)CrossRefMathSciNetGoogle Scholar
  14. 14.
    Ma, L., Li, Y., Liu, Y.: Oil Spill Monitoring Based on Its Spectral Characteristics. Environmental Forensics 10(4), 317–323 (2009)CrossRefGoogle Scholar
  15. 15.
    Cococcioni, M., Corucci, L., Masini, A.: SVME: An Ensemble of Support Vector Ma-chines for Detecting Oil Spills From Full Resolution MODIS Images. Ocean Dynamics 2, 449–467 (2012)CrossRefGoogle Scholar
  16. 16.
    Jubai, A., Jing, B., Yang, J.: Combining Fuzzy Theory and A Genetic Algorithm for Satel-lite Image Edge Detection. International Journal of Remote Sensing 14, 3013–3024 (2005)Google Scholar
  17. 17.
    Karantzalos, K., Argialas, D.: Automatic Detection and Tracking of Oil Spills in SAR Imagery with Level Set Segmentation. International Journal of Remote Sensing 21, 6281–6296 (2008)CrossRefGoogle Scholar
  18. 18.
    Jing, Y., Jubai, A., Zhaoxia, L.: Edge Detection Algorithm of Oil Spills Remote Sensing Image Based on DBT Denoising and Improved GDNI Edge Linking. Computer Science 38(11), 282–285 (2011)Google Scholar
  19. 19.
    Jing, Y., Jubai, A., Zhaoxia, L.: A Novel Edge Detection Algorithm Based on Global Minimization Active Contour Model for Oil Slick Infrared Aerial Image. IEEE Transactions on Geoscience and Remote Sensing 49(6), 2005–2013 (2011)CrossRefGoogle Scholar
  20. 20.
    Sun, G.Y., Liu, Q.H., Liu, Q.: A Novel Approach for Edge Detection Based on The Theory of Universal Gravity. Pattern Recognition 4, 2766–2775 (2007)CrossRefGoogle Scholar
  21. 21.
    Lopez-Molina, C., Bustinc, H., Fernandez, J.: A Gravitational Approach to Edge Detection Based on Triangular Norms. Pattern Recognition 43, 3730–3741 (2010)CrossRefMATHGoogle Scholar
  22. 22.
    Su, W., Su, F., Zhou, C.: Optical Satellite Remote Sensing Capabilities Analysis of the Marine Oil Spill. Journal of Geo-Information Science 14(4), 523–530 (2012)CrossRefMathSciNetGoogle Scholar
  23. 23.
    Adamo, M., De Carolis, G., De Pasquale, V.: Exploiting Sunglint Signatures From MERIS and MODIS Imagery in Combination to SAR Data to Detect Oil Slicks. In: Envisat Symposium, pp. 23–27 (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Weiguang Su
    • 1
    • 3
    • 4
  • Bo Ping
    • 2
  • Fenzhen Su
    • 3
  1. 1.Key Laboratory of Coastal Zone Environmental ProcessesYantai Institute of Coastal Zone Research(YIC), Chinese Academy of Sciences(CAS), Shandong Provincial Key Laboratory of Coastal Zone Environmental Processes, YICCASYantaiChina
  2. 2.School of Remote Sensing and Information EngineeringWuhan UniversityWuhanChina
  3. 3.LREIS, Institute of Geographic Sciences and Natural Resources ResearchCASBeijingChina
  4. 4.University of Chinese Academy of ScienceChina

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