Abstract-A neural network approach for semi-automatic detection of oil spills in European remote sensing satellite-synthetic aperture radar (ERS-SAR) imagery is presented. The network input is a vector containing the values of a set of features characterizing an oil spill candidate. The classification performance of the algorithm has been evaluated on a data set containing verified examples of oil spill and look-alike. A direct analysis of the information content of the calculated features has been also carried out through an extended pruning procedure of the net.Index Terms-ERS-synthetic aperture radar (SAR), neural networks, oil spill detection.
Abstract-A neural network approach for semi-automatic detection of oil spills in European remote sensing satellite-synthetic aperture radar (ERS-SAR) imagery is presented. The network input is a vector containing the values of a set of features characterizing an oil spill candidate. The classification performance of the algorithm has been evaluated on a data set containing verified examples of oil spill and look-alike. A direct analysis of the information content of the calculated features has been also carried out through an extended pruning procedure of the net.Index Terms-ERS-synthetic aperture radar (SAR), neural networks, oil spill detection.
Parametric uncertainty is propagated through Reynolds-averaged Navier-Stokes (RANS) computations of a prototypical acetone/air aerosol stream flowing in a dry air environment. Two parameters are considered as uncertain: the inflow velocity dissipation and a coefficient that blends the discrete random walk and the gradient-based dispersion models. A Bayesian setting is employed to represent the degree of belief about the parameters of interest in terms of probability theory, such that uncertainty is described with probability density functions. Random variables are represented by means of polynomial chaos expansions. The sensitivity of mean axial velocity and mean vapor mass fraction to the uncertain parameters is discussed.
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