Multi-resolution Technique-Based Oil Spill Look-Alikes Detection in X-Band SAR Data

  • Mari-Cortes Benito-Ortiz
  • David Mata-MoyaEmail author
  • Maria-Pilar Jarabo-Amores
  • Miguel Maganto-Pascual
  • Pedro-Jose Gomez-del-Hoyo
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 797)


This paper tackles the oil spill look-alikes detection based on multi-resolution technique using Synthetic Aperture Radar (SAR) images acquired in X band. SAR sensors are powerful tools for Earth Observation but automatic interpretation is very difficult. As oil spills appear as dark spots, the first processing stage is the look-alikes detection. A segmentation based on texture parameters, to define the potential areas with dark spots, and sea clutter statistics, to determine an adaptive threshold, is proposed. The dark spot detection scheme is evaluated with real X-Band SAR data, and detection performances allow to improve the accuracy in later feature extraction tasks.


SAR data Multi-resolution technique Radar detection Radar image processing 



This work has been partially funded by the Spanish “Ministerio de Economía, Industria y Competitividad,” under project TEC2015-71148-R, the University of Alcalá, under project CCG2017/EXP056 and by Hisdesat under project 37/2016.


  1. 1.
    ESA (2017) Monitoring oil spills from space.
  2. 2.
    REMPEC (2017) Reports by year.
  3. 3.
    Curlander JC, McDonough RN (1991) Synthetic aperture radar: systems and signal processing. Wiley-Interscience, New YorkzbMATHGoogle Scholar
  4. 4.
    Brekke C, Solberg AHS (2005) Oil spill detection by satellite remote sensing. Remote Sens Environ 95(1):1–13CrossRefGoogle Scholar
  5. 5.
    Pisano A, De Dominicis M, Biamino W, Bignami F, Gherardi S, Colao F, Coppini G, Marullo S, Sprovieri M, Trivero P, Zambianchi E, Santoleri R (2016) An oceanographic survey for oil spill monitoring and model forecasting validation using remote sensing and in situ data in the Mediterranean Sea. Deep Sea Res Part II Top Stud Oceanogr 133:132–145CrossRefGoogle Scholar
  6. 6.
    Lounis B, Aissa AB (2006) A contextual Segmentation of Sea SAR images to detect dark spots in Mediterranean Sea. In: Information and Communication Technologies (ICTTA), pp 371–376Google Scholar
  7. 7.
    Solberg AHS, Brekke C, Husoy PO (2007) Oil spill detection in Radarsat and Envisat SAR images. IEEE Trans Geosci Remote Sens 45(3):746–755CrossRefGoogle Scholar
  8. 8.
    Li XM, Jia T, Velotto D (2016) Spatial and temporal variations of oil spills in the North Sea observed by the satellite constellation of TerraSAR-X and TanDEM-X. IEEE J Sel Top Appl Earth Observations Remote Sens 9(11):4941–4947CrossRefGoogle Scholar
  9. 9.
    Nirchio F, Sorgente M, Giancaspro A, Biamino W, Parisato E, Ravera R, Trivero P (2005) Automatic detection of oil spills from SAR images. Int J Remote Sens 26(6):1157–1174CrossRefGoogle Scholar
  10. 10.
    Berizzi F, Martorella M, Bertini G, Garzelli A, Nencini F, Dell’Acqua F, Gamba P (2004) Sea SAR image analysis by fractal data fusion. In: IEEE international geoscience and remote sensing symposium (IGARSS), pp 93–96Google Scholar
  11. 11.
    Hu X, Zhu M (2009) Distributed targets detection based on local spectral histograms and agents. In: IEEE international geoscience and remote sensing symposium (IGARSS), vol 3, pp 646–649Google Scholar
  12. 12.
    De Grandi G, Hoekman D, Lee JS, Schuler D, Ainsworth T (2004) A wavelet multiresolution technique for polarimetric texture analysis and segmentation of SAR images. In: IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp 710–713Google Scholar
  13. 13.
    Brekke C, Solberg AHS (2008) Classifiers and confidence estimation for oil spill detection in ENVISAT ASAR images. IEEE Geosci Remote Sens Lett 5(1):65–69CrossRefGoogle Scholar
  14. 14.
    TERRASAR-X (2017) Germany’s radar eye in space.
  15. 15.
    Martin-de-Nicolas J, Jarabo-Amores MP, Mata-Moya D, del-Rey-Maestre N, Barcena-Humanes JL (2014) Statistical analysis of SAR sea clutter for classification purposes. Remote Sens 6(1):9379–9411CrossRefGoogle Scholar
  16. 16.
    Martin-de-Nicolas J, Jarabo-Amores MP, del-Rey-Maestre N, Mata-Moya D, Barcena-Humanes JL (2015) A non-parametric CFAR detector based on SAR sea clutter statistical modeling. In: IEEE International Conference on Image Processing (ICIP), pp 4426-4430Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Mari-Cortes Benito-Ortiz
    • 1
  • David Mata-Moya
    • 1
    Email author
  • Maria-Pilar Jarabo-Amores
    • 1
  • Miguel Maganto-Pascual
    • 1
  • Pedro-Jose Gomez-del-Hoyo
    • 1
  1. 1.Signal Theory and Communications Department, Superior Polytechnic SchoolUniversity of AlcaláAlcalá de HenaresSpain

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