Combining Features to Improve Oil Spill Classification in SAR Images

  • Darby F. de A. Lopes
  • Geraldo L. B. Ramalho
  • Fátima N. S. de Medeiros
  • Rodrigo C. S. Costa
  • Regia T. S. Araújo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4109)


As radar backscatter values for oil slicks are very similar to backscatter values for very calm sea areas and other ocean phenomena, dark areas in Synthetic Aperture Radar (SAR) imagery tend to be misinterpreted. In this paper three feature sets are used to identify the oil slicks in SAR images. These images are submitted to different MLP architectures to verify the separability performance over each feature set. This analysis is very suitable for remote sensing of environment applications concerning marine oil pollution. The estimated resulting performance points out which feature set is the best suitable for the suggested application.


Feature Selection Grey Level Hide Neuron Synthetic Aperture Radar Synthetic Aperture Radar Image 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Darby F. de A. Lopes
    • 1
  • Geraldo L. B. Ramalho
    • 1
  • Fátima N. S. de Medeiros
    • 1
  • Rodrigo C. S. Costa
    • 1
  • Regia T. S. Araújo
    • 1
  1. 1.Image Processing Research GroupUniversidade Federal do CearaFortalezaBrazil

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