Oil spill detection methodologies traditionally use arbitrary selected quantitative and qualitative statistical features (e.g. area, perimeter, complexity) for classifying dark objects on SAR images to oil spills or look-alike phenomena. In our previous work genetic algorithms in synergy with neural networks were used to suggest the best feature combination maximizing the discrimination of oil spills and look-alike phenomena. In the present work, a detailed examination of robustness of the proposed combination of features is given. The method is unique, as it searches though a large number of combinations derived from the initial 25 features. The results show that a combination of 10 features yields the most accurate results. Based on a dataset consisting of 69 oil spills and 90 look-alikes, classification accuracies of 85.3% for oil spills and in 84.4% for look-alikes are achieved.
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Document Type: Research Article
Joint Research Centre (JRC) of the European Commission, Institute for the Protection and Security of the Citizen, Ispra, 21020 (VA), Italy
Laboratory of Remote Sensing, School of Rural and Surveying Engineering, National Technical University of Athens, Heroon Polytechniou 9, Greece
Publication date: 2009-01-01
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