An object-oriented methodology to detect oil spills

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

dc.contributor.author	Karathanassi, V	en
dc.contributor.author	Topouzelis, K	en
dc.contributor.author	Pavlakis, P	en
dc.contributor.author	Rokos, D	en
dc.date.accessioned	2014-03-01T01:23:34Z
dc.date.available	2014-03-01T01:23:34Z
dc.date.issued	2006	en
dc.identifier.issn	0143-1161	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/17032
dc.subject	Object Oriented	en
dc.subject	Oil Spill	en
dc.subject.classification	Remote Sensing	en
dc.subject.classification	Imaging Science & Photographic Technology	en
dc.subject.other	Fuzzy systems	en
dc.subject.other	Image resolution	en
dc.subject.other	Image segmentation	en
dc.subject.other	Radar imaging	en
dc.subject.other	Remote sensing	en
dc.subject.other	Synthetic aperture radar	en
dc.subject.other	Fuzzy classification method	en
dc.subject.other	High-resolution image	en
dc.subject.other	Oil spill detection	en
dc.subject.other	Oil spills	en
dc.subject.other	accuracy assessment	en
dc.subject.other	detection method	en
dc.subject.other	empirical analysis	en
dc.subject.other	fuzzy mathematics	en
dc.subject.other	image analysis	en
dc.subject.other	image resolution	en
dc.subject.other	oil spill	en
dc.subject.other	synthetic aperture radar	en
dc.title	An object-oriented methodology to detect oil spills	en
heal.type	journalArticle	en
heal.identifier.primary	10.1080/01431160600693575	en
heal.identifier.secondary	http://dx.doi.org/10.1080/01431160600693575	en
heal.language	English	en
heal.publicationDate	2006	en
heal.abstract	A new automated methodology for oil spill detection is presented, by which full synthetic aperture radar (SAR) high-resolution image scenes can be processed. The methodology relies on the object-oriented approach and profits from image segmentation techniques to detected dark formations. The detection of dark formations is based on a threshold definition that is fully adaptive to local contrast and brightness of large image segments. For the detection process, two empirical formulas are developed that also permit the classification of oil spills according to their brightness. A fuzzy classification method is used to classify dark formations as oil spills or look-alikes. Dark formations are not isolated and features of both dark areas and sea environment are considered. Various sea environments that affect oil spill shape and boundaries are grouped in two knowledge bases, used for the classification of dark formations. The accuracy of the method for the 12 SAR images used is 99.5% for the class of oil spills, and 98.8% for that of look-alikes. Fresh oil spills, fresh spills affected by natural phenomena, oil spills without clear stripping, small linear oil spills, oil spills with broken parts and amorphous oil spills can be successfully detected.	en
heal.publisher	TAYLOR & FRANCIS LTD	en
heal.journalName	International Journal of Remote Sensing	en
dc.identifier.doi	10.1080/01431160600693575	en
dc.identifier.isi	ISI:000244182300007	en
dc.identifier.volume	27	en
dc.identifier.issue	23	en
dc.identifier.spage	5235	en
dc.identifier.epage	5251	en