@article{JRC40319,
	address = {ABINGDON (ENGLAND)},
	year = {2006},
	author = {Karathanassi V and Topouzelis K and Pavlakis P and Rokos D},
	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.
		},
	title = {An Object-Oriented Methodology to Detect Oil Spills},
	type = {},
	url = {},
	volume = {27},
	number = {23},
	journal = {INTERNATIONAL JOURNAL OF REMOTE SENSING},
	pages = {5235-5251},
	issn = {0143-1161},
	publisher = {TAYLOR & FRANCIS LTD},
	doi = {10.1080/01431160600693575}
}