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|Title:||Data Fusion of SAR derived Features and Ancillary Information for Automatic Oil Spill Detection|
|Authors:||VESPE MICHELE; POSADA SANCHEZ MONICA; FERRARO DI SILVI E CASTIGLIONE Guido; VAN WIMERSMA GREIDANUS Herman|
|Citation:||FRESENIUS ENVIRONMENTAL BULLETIN vol. 20 no. 1 p. 36-43|
|Publisher:||PARLAR SCIENTIFIC PUBLICATIONS (P S P)|
|Type:||Articles in periodicals and books|
|Abstract:||Synthetic Aperture Radar (SAR) imagery is widely used for oil spill detection over the sea surface. In the framework of marine pollution preparedness, the need for reliable information to be used in follow-up procedures (law enforcement and contingency planning) implies the increase of detection consistency over the numerous false alarms that typically appear using automatic detection tools. For this reason, at operational level, the satellite based image analysis is currently carried out mostly by visual inspection. This paper describes the potential detection reliability improvements of automatic oil spill detection brought by the combination of SAR information with ancillary data. In particular, Artificial Neural Networks are used to perform data fusion between SAR preliminary detection, metocean and contextual information.|
|JRC Institute:||Space, Security and Migration|
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