Oil spills automatic detection from SAR images

F. Nirchio, S. Di Tomaso, W. Biamino, E. Parisato, P. Trivero, A. Ciancaspro

Risultato della ricerca: Contributo su rivistaArticolo da conferenzapeer review

Abstract

A probabilistic method has been developed that distinguishes oil spills from other similar sea surface features in Synthetic Aperture Radar (SAR) images. It considers both the radiometric and the geometric characteristics of the areas under test. In order to minimize the operator intervention, it adopts automatic selection criteria to extract the potentially polluted areas from the images. The method has an a priori percentage of correct classification higher than 90% on the training dataset; the performance is confirmed on a different dataset of verified slicks. The system and its ability to detect and classify oil and non-oil surface features are described. The ERS probability to detect an oil pollution event is estimated using a set of verified oil spills and analysing the wind intensity, deduced from the image itself. The present performances of an off line processing data centre are also analysed, in term of throughput and response time, in order to verify them against the user requirements.

Lingua originaleInglese
Numero di articolo321
pagine (da-a)1499-1505
Numero di pagine7
RivistaEuropean Space Agency, (Special Publication) ESA SP
Numero di pubblicazione572
Stato di pubblicazionePubblicato - 2005
Pubblicato esternamente
Evento2004 Envisat and ERS Symposium - Salzburg, Austria
Durata: 6 set 200410 set 2004

Fingerprint

Entra nei temi di ricerca di 'Oil spills automatic detection from SAR images'. Insieme formano una fingerprint unica.

Cita questo