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Detection and discrimination between oil spills and look-alike phenomena through neural networks / Konstantinos Topouzelis in ISPRS Journal of photogrammetry and remote sensing, vol 62 n° 4 (September 2007)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 62 n° 4 (September 2007) . - pp 264 - 270 Titre : Detection and discrimination between oil spills and look-alike phenomena through neural networks Type de document : Article/Communication Auteurs : Konstantinos Topouzelis, Auteur ; V. Karathanassi, Auteur ; P. Pavlakis, Auteur ; D. Rokos, Auteur Année de publication : 2007 Article en page(s) : pp 264 - 270 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] analyse discriminante [Termes IGN] classification par réseau neuronal [Termes IGN] détection automatique [Termes IGN] image radar moirée [Termes IGN] marée noire [Termes IGN] radargrammétrie Résumé : (Auteur) 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. Numéro de notice : A2007-428 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2007.05.003 En ligne : https://doi.org/10.1016/j.isprsjprs.2007.05.003 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28791 [article]

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