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Oil spill detection: imaging system modeling and advanced image processing using optimized SDC algorithm

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Abstract

Oil spill on the sea surface might happen without any previous caution and is seen relatively often. Efficient and effective oil spill monitoring and detection can reduce response time, minimize remediation costs and limit dangerous impacts to the environment. An innovative satellite-based oil pollution detection framework is demonstrated in this paper, including satellite imaging system modeling—communication link configuration, noise model for image transmission and preprocessing. Finally, an optimized sequential detection of change-based image object detection algorithm is proposed to detect oil spill on the ocean surface from the enhanced remote sensing data. Moderate resolution imaging spectroradiometer images of the Gulf of Mexico accident from NASA between May and June 2010 are adopted for testing in our framework. The results of this research show that the proposed algorithms can effectively distinguish the spill covering vast areas of the marine environment even with severe additive noise and have good separation properties against complex signatures, such as the vicinity to the irregular coast or foggy and cloudy weather conditions.

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Authors and Affiliations

  1. Department of Electrical Engineering, University of Colorado Denver, Denver, CO, 80217, USA

    Ehab A. Etellisi & Yiming Deng

  2. Department of Bioengineering, University of Colorado Denver, Denver, CO, 80217, USA

    Yiming Deng

  3. Colorado Translational Research Imaging Center, University of Colorado Denver, Denver, CO, 80217, USA

    Yiming Deng

Authors
  1. Ehab A. Etellisi
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  2. Yiming Deng
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Correspondence to Yiming Deng.

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Etellisi, E.A., Deng, Y. Oil spill detection: imaging system modeling and advanced image processing using optimized SDC algorithm. SIViP 8, 1405–1419 (2014). https://doi.org/10.1007/s11760-012-0371-8

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  • DOI: https://doi.org/10.1007/s11760-012-0371-8

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