This paper proposes an automatic detection of oil spills on SAR （synthetic aperture radar） images using DE （differential evolution）, neutral network and BP （back propagation） algorithm. Here, DE and BP are combi...This paper proposes an automatic detection of oil spills on SAR （synthetic aperture radar） images using DE （differential evolution）, neutral network and BP （back propagation） algorithm. Here, DE and BP are combined to train a multilayer perceptron （MLP） network for achieving the global extreme with a better convergence speed. The input data of neural networks are the geometrical characteristics ofoil spills （e.g. area, perimeter, complexity） and the physical behavior ofoil spills （e,g. mean or max backscatter value, standard deviation of the dark formation）. The out data are oil spill or look-alike. We experiment ALOS/PALSAR and EnviSAT ASAR on East sea area of Viet Nam. The experimental results show that the combination algorithm converges faster and has significantly better capability of avoiding local optima.展开更多
文摘This paper proposes an automatic detection of oil spills on SAR （synthetic aperture radar） images using DE （differential evolution）, neutral network and BP （back propagation） algorithm. Here, DE and BP are combined to train a multilayer perceptron （MLP） network for achieving the global extreme with a better convergence speed. The input data of neural networks are the geometrical characteristics ofoil spills （e.g. area, perimeter, complexity） and the physical behavior ofoil spills （e,g. mean or max backscatter value, standard deviation of the dark formation）. The out data are oil spill or look-alike. We experiment ALOS/PALSAR and EnviSAT ASAR on East sea area of Viet Nam. The experimental results show that the combination algorithm converges faster and has significantly better capability of avoiding local optima.
