Neural Networks for Oil Spill Detection Using ERS-SAR Data

F. Del Frate, A. Petrocchi, J. Lichtenegger, and G. Calabresi

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.

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