Oil spill detection by means of neural networks algorithms: a sensitivity analysis

Synthetic Aperture Radar (SAR) images provided by satellite missions may provide a significant support for oil spill detection over the sea. In particular neural networks algorithms have recently demonstrated their potentialities for discrimination between oil spills and objects which resemble oil spills (called "look-alikes"). The main steps of the classification procedure are the identification of dark spots over the sea, the computing of a set of parameters (features) for each dark spot and the classification of the oil spill candidate using a trained neural network, where the network input is a vector containing the values of the features extracted. The features so far mainly consist of physical-geometrical characteristics of the dark spot. This study presents a new neural network algorithm for the oil spill detection. The results also report a sensitivity analysis of the classification performance on the quantities that are given as input to the neural network. Among the considered inputs the value of the local wind speed has been also included.