Detection and discrimination between oil spills and look-alike phenomena through neural networks

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	2014-03-01T01:26:05Z
dc.date.available	2014-03-01T01:26:05Z
dc.date.issued	2007	en
dc.identifier.issn	0924-2716	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/17923
dc.subject	Neural networks	en
dc.subject	Oil spill	en
dc.subject	Pollution	en
dc.subject	SAR	en
dc.subject	Training	en
dc.subject.classification	Geography, Physical	en
dc.subject.classification	Geosciences, Multidisciplinary	en
dc.subject.classification	Remote Sensing	en
dc.subject.classification	Imaging Science & Photographic Technology	en
dc.subject.other	Clouds	en
dc.subject.other	Gravitation	en
dc.subject.other	Oil spills	en
dc.subject.other	Remote sensing	en
dc.subject.other	Synthetic aperture radar	en
dc.subject.other	Water pollution	en
dc.subject.other	Backscatter values	en
dc.subject.other	Dark formation detection	en
dc.subject.other	Short gravity sea waves	en
dc.subject.other	Neural networks	en
dc.subject.other	artificial neural network	en
dc.subject.other	backscatter	en
dc.subject.other	detection method	en
dc.subject.other	marine environment	en
dc.subject.other	marine pollution	en
dc.subject.other	oil spill	en
dc.subject.other	synthetic aperture radar	en
dc.title	Detection and discrimination between oil spills and look-alike phenomena through neural networks	en
heal.type	journalArticle	en
heal.identifier.primary	10.1016/j.isprsjprs.2007.05.003	en
heal.identifier.secondary	http://dx.doi.org/10.1016/j.isprsjprs.2007.05.003	en
heal.language	English	en
heal.publicationDate	2007	en
heal.abstract	Synthetic Aperture Radar (SAR) images are extensively used for dark formation detection in the marine environment, as their recording is independent of clouds and weather. Dark formations can be caused by man made actions (e.g. oil spill discharging) or natural ocean phenomena (e.g. natural slicks, wind front areas). Radar backscatter values for oil spills are very similar to backscatter values for very calm sea areas and other ocean phenomena because they damp the capillary and short gravity sea waves. The ability of neural networks to detect dark formations in high resolution SAR images and to discriminate oil spills from look-alike phenomena simultaneously was examined. Two different neural networks are used; one to detect dark formations and the second one to perform a classification to oil spills or look-alikes. The proposed method is very promising in detecting dark formations and discriminating oil spills from look-alikes as it detects with an overall accuracy of 94% the dark formations and discriminate correctly 89% of examined cases. (C) 2007 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V All rights reserved.	en
heal.publisher	ELSEVIER SCIENCE BV	en
heal.journalName	ISPRS Journal of Photogrammetry and Remote Sensing	en
dc.identifier.doi	10.1016/j.isprsjprs.2007.05.003	en
dc.identifier.isi	ISI:000249860900002	en
dc.identifier.volume	62	en
dc.identifier.issue	4	en
dc.identifier.spage	264	en
dc.identifier.epage	270	en