Investigation of genetic algorithms contribution to feature selection for oil spill detection

Topouzelis, K; Stathakis, D; Karathanassi, V

dc.contributor.author	Topouzelis, K	en
dc.contributor.author	Stathakis, D	en
dc.contributor.author	Karathanassi, V	en
dc.date.accessioned	2014-03-01T01:30:57Z
dc.date.available	2014-03-01T01:30:57Z
dc.date.issued	2009	en
dc.identifier.issn	0143-1161	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/19690
dc.subject	Feature Selection	en
dc.subject	Genetic Algorithm	en
dc.subject	Oil Spill	en
dc.subject.classification	Remote Sensing	en
dc.subject.classification	Imaging Science & Photographic Technology	en
dc.subject.other	Classification accuracy	en
dc.subject.other	Data sets	en
dc.subject.other	Feature combination	en
dc.subject.other	Feature selection	en
dc.subject.other	Oil spill detection	en
dc.subject.other	SAR Images	en
dc.subject.other	Statistical features	en
dc.subject.other	Hazardous materials spills	en
dc.subject.other	Object recognition	en
dc.subject.other	Oil spills	en
dc.subject.other	accuracy assessment	en
dc.subject.other	artificial neural network	en
dc.subject.other	data set	en
dc.subject.other	detection method	en
dc.subject.other	genetic algorithm	en
dc.subject.other	image classification	en
dc.subject.other	oil spill	en
dc.subject.other	qualitative analysis	en
dc.subject.other	quantitative analysis	en
dc.subject.other	synthetic aperture radar	en
dc.title	Investigation of genetic algorithms contribution to feature selection for oil spill detection	en
heal.type	journalArticle	en
heal.identifier.primary	10.1080/01431160802339456	en
heal.identifier.secondary	http://dx.doi.org/10.1080/01431160802339456	en
heal.language	English	en
heal.publicationDate	2009	en
heal.abstract	Oil spill detection methodologies traditionally use arbitrary selected quantitative and qualitative statistical features (e.g. area, perimeter, complexity) for classifying dark objects on SAR images to oil spills or look-alike phenomena. In our previous work genetic algorithms in synergy with neural networks were used to suggest the best feature combination maximizing the discrimination of oil spills and look-alike phenomena. In the present work, a detailed examination of robustness of the proposed combination of features is given. The method is unique, as it searches though a large number of combinations derived from the initial 25 features. The results show that a combination of 10 features yields the most accurate results. Based on a dataset consisting of 69 oil spills and 90 look-alikes, classification accuracies of 85.3% for oil spills and in 84.4% for look-alikes are achieved.	en
heal.publisher	TAYLOR & FRANCIS LTD	en
heal.journalName	International Journal of Remote Sensing	en
dc.identifier.doi	10.1080/01431160802339456	en
dc.identifier.isi	ISI:000264336100005	en
dc.identifier.volume	30	en
dc.identifier.issue	3	en
dc.identifier.spage	611	en
dc.identifier.epage	625	en