Automatic detection of oil spills from SAR images

• Published in: International journal of remote sensing Vol. 26; no. 6; pp. 1157 - 1174
• Main Authors: Nirchio, F., Sorgente, M., Giancaspro, A., Biamino, W., Parisato, E., Ravera, R., Trivero, P.
• Format: Journal Article
• Language: English
• Published: Abingdon Taylor & Francis Group 01.03.2005; Taylor and Francis
• Subjects: Applied geophysics; Earth sciences; Earth, ocean, space; Engineering and environment geology. Geothermics; Exact sciences and technology; Internal geophysics; Marine geology; Pollution, environment geology; Sea surface; detection; Space remote sensing; ERS; classification; pollution; offshore; synthetic aperture radar; winds; imagery; radiometry; oil spills; surface properties
• ISSN: 0143-1161; 1366-5901
• DOI: 10.1080/01431160512331326558

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 being tested. 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. Some analyses have been conducted using images with different radiometric and geometric resolutions to test its suitability with SAR images different from European Remote Sensing (ERS) satellite ones. The system and its ability to detect and classify oil and non-oil surface features are described. Starting from a set of verified oil spills detected offshore and over the coastline, the ability of SAR to reveal oil spills is tested by analysing wind intensity, deduced from the image itself, and the distance from the coast.