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|Title:||Potentiality of Feed-forward Neural Networks for Classifying Dark Formations to Oil Spills and Look-alikes|
|Authors:||TOPOUZELIS Konstantinos; KARATHANASSI Vassilia; PAVLAKIS PETROS; ROKOS Demetrios|
|Citation:||Geocarto International vol. 24 no. 3 p. 179-191|
|Publisher:||Taylor & Francis|
|Type:||Articles in periodicals and books|
|Abstract:||Radar backscatter values from oil spills are very similar to backscatter values from very calm sea areas and other ocean phenomena. Several studies aiming at oil spill detection have been conducted. Most of these studies rely on the detection of dark areas, which have high Bayesian probability of being oil spills. The drawback of these methods is a complex process, mainly because non-linearly separable datasets are introduced in statistically based decisions. The use of neural networks (NNs) in remote sensing has increased significantly, as NNs can simultaneously handle non-linear data of a multidimensional input space. In this article, we investigate the ability of two commonly used feed-forward NN models: multilayer perceptron (MLP) and radial basis function (RBF) networks, to classify dark formations in oil spills and look-alike phenomena. The appropriate training algorithm, type and architecture of the optimum network are subjects of research. Inputs to the networks are the original synthetic aperture radar image and other images derived from it. MLP networks are recognized as more suitable for oil spill detection.|
|JRC Institute:||Space, Security and Migration|
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