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.
| Original language | English |
|---|---|
| Article number | 321 |
| Pages (from-to) | 1499-1505 |
| Number of pages | 7 |
| Journal | European Space Agency, (Special Publication) ESA SP |
| Issue number | 572 |
| Publication status | Published - 2005 |
| Externally published | Yes |
| Event | 2004 Envisat and ERS Symposium - Salzburg, Austria Duration: 6 Sept 2004 → 10 Sept 2004 |