A Neural Network Approach to Oil Spill Detection using SAR Data

Synthetic Aperture Radar (SAR) images are extensively used for the determination of oil spills in the marine environment, as they are not affected by local weather condition and cloudiness. However, radar backscatter values for oil spills are very similar to backscatter values for very calm sea areas, because dampen capillary and short gravity waves is caused by the presence of an oil spill. Most classification algorithms for oil spill detection rely on statisticbased decisions. In previous study features which lead to a successful oil spill detection were defined and evaluated through an hybrid multi-segmentation – expert system procedure. In this paper a classification scheme based on neural networks is presented. In order the neural network to be functional and the classification procedure to be simple, the inputs of the neural network are images. Thus, several images were generated from the original SAR, each one presenting a texture or geometry key-feature. The potential of the Multilayer Perceptron neural network and different training algorithms for oil spill classification were investigated. As appropriate topology for oil spill detection is specified the 4:2:1 and training algorithm the standard backpropagation for a certain number of epochs and afterwards the conjugate gradient or resilient backpropagation. Methods overall accuracy for the examined areas vary from 99.3% to 99.6%.