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Machine Learning for the Detection of Oil Spills in Satellite Radar Images
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  • Published: February 1998

Machine Learning for the Detection of Oil Spills in Satellite Radar Images

  • Miroslav Kubat1,1,
  • Robert C. Holte1,1 &
  • Stan Matwin1,1 

Machine Learning volume 30, pages 195–215 (1998)Cite this article

  • 20k Accesses

  • 823 Citations

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Abstract

During a project examining the use of machine learning techniques for oil spill detection, we encountered several essential questions that we believe deserve the attention of the research community. We use our particular case study to illustrate such issues as problem formulation, selection of evaluation measures, and data preparation. We relate these issues to properties of the oil spill application, such as its imbalanced class distribution, that are shown to be common to many applications. Our solutions to these issues are implemented in the Canadian Environmental Hazards Detection System (CEHDS), which is about to undergo field testing.

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Authors and Affiliations

  1. School of Information Technology and Engineering, University of Ottawa, 150 Louis Pasteur, Ottawa, Ontario, K1N 6N5, Canada

    Miroslav Kubat, Miroslav Kubat, Robert C. Holte, Robert C. Holte, Stan Matwin & Stan Matwin

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  1. Miroslav Kubat
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  2. Robert C. Holte
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  3. Stan Matwin
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Kubat, M., Holte, R.C. & Matwin, S. Machine Learning for the Detection of Oil Spills in Satellite Radar Images. Machine Learning 30, 195–215 (1998). https://doi.org/10.1023/A:1007452223027

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  • Issue Date: February 1998

  • DOI: https://doi.org/10.1023/A:1007452223027

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