A note on genetic algorithms for large-scale feature selection
Pattern Recognition Letters
PDP models and general issues in cognitive science
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Floating search methods in feature selection
Pattern Recognition Letters
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Automatic identification of oil spills on satellite images
Environmental Modelling & Software
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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.