Potentiality of feed-forward neural networks for classifying dark formations to oil spills and look-alikes

Topouzelis, K; Karathanassi, V; Pavlakis, P; Rokos, D

dc.contributor.author	Topouzelis, K	en
dc.contributor.author	Karathanassi, V	en
dc.contributor.author	Pavlakis, P	en
dc.contributor.author	Rokos, D	en
dc.date.accessioned	2014-03-01T01:31:41Z
dc.date.available	2014-03-01T01:31:41Z
dc.date.issued	2009	en
dc.identifier.issn	10106049	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/19884
dc.subject	Neural networks	en
dc.subject	Oil spill	en
dc.subject	Pollution	en
dc.subject	SAR	en
dc.subject	Sea	en
dc.subject.other	algorithm	en
dc.subject.other	artificial neural network	en
dc.subject.other	Bayesian analysis	en
dc.subject.other	detection method	en
dc.subject.other	image analysis	en
dc.subject.other	image classification	en
dc.subject.other	nonlinearity	en
dc.subject.other	numerical model	en
dc.subject.other	oil pollution	en
dc.subject.other	oil spill	en
dc.subject.other	probability	en
dc.subject.other	radar imagery	en
dc.subject.other	synthetic aperture radar	en
dc.title	Potentiality of feed-forward neural networks for classifying dark formations to oil spills and look-alikes	en
heal.type	journalArticle	en
heal.identifier.primary	10.1080/10106040802488526	en
heal.identifier.secondary	http://dx.doi.org/10.1080/10106040802488526	en
heal.publicationDate	2009	en
heal.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.	en
heal.journalName	Geocarto International	en
dc.identifier.doi	10.1080/10106040802488526	en
dc.identifier.volume	24	en
dc.identifier.issue	3	en
dc.identifier.spage	179	en
dc.identifier.epage	191	en