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Ocean Science An interactive open-access journal of the European Geosciences Union

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https://doi.org/10.5194/osd-12-1263-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
Research article
25 Jun 2015
Review status
This discussion paper has been under review for the journal Ocean Science (OS). The revised manuscript was not accepted.
Multi-objective entropy evolutionary algorithm for marine oil spill detection using cosmo-skymed satellite data
M. Marghany Geoscience {&} Digital Earth Center, Research Institute for Sustainability & Environment, Universiti Teknologi Malaysia, 81310 Skudai, UTM, Johor, Malaysia
Abstract. Oil spill pollution has a substantial role in damaging the marine ecosystem. Oil spill that floats on top of water, as well as decreasing the fauna populations, affects the food chain in the ecosystem. In fact, oil spill is reducing the sunlight penetrates the water, limiting the photosynthesis of marine plants and phytoplankton. Moreover, marine mammals for instance, disclosed to oil spills their insulating capacities are reduced, and so making them more vulnerable to temperature variations and much less buoyant in the seawater. This study has demonstrated a design tool for oil spill detection in SAR satellite data using optimization of Entropy based Multi-Objective Evolutionary Algorithm (E-MMGA) which based on Pareto optimal solutions. The study also shows that optimization entropy based Multi-Objective Evolutionary Algorithm provides an accurate pattern of oil slick in SAR data. This shown by 85 % for oil spill, 10 % look-alike and 5 % for sea roughness using the receiver-operational characteristics (ROC) curve. The E-MMGA also shows excellent performance in SAR data. In conclusion, E-MMGA can be used as optimization for entropy to perform an automatic detection of oil spill in SAR satellite data.

Citation: Marghany, M.: Multi-objective entropy evolutionary algorithm for marine oil spill detection using cosmo-skymed satellite data, Ocean Sci. Discuss., 12, 1263-1289, https://doi.org/10.5194/osd-12-1263-2015, 2015.
M. Marghany
M. Marghany

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Short summary
This study has demonstrated work to optimize the oil spill footprint detection in synthetic aperture radar (SAR) data. Therefore, Entropy-based Multi-objective Evolutionary Algorithm (E-MMGA) has implemented with COSMO-SkyMed data during the oil spill event along the coastal water of along Koh Samet island, Thailand. Besides, Pareto optimal solution is implemented with E-MMGA to minimize the difficulties of oil spill footprint boundary detection because of the existence of look-alike in SAR.
This study has demonstrated work to optimize the oil spill footprint detection in synthetic...
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