Automatic detection of oil spills in ERS SAR images

We present algorithms for the automatic detection of oil spills in SAR images. The developed framework consists of first detecting dark spots in the image, then computing a set of features for each dark spot, before the spot is classified as either an oil slick or a "lookalike" (other oceanographic phenomena which resemble oil slicks), The classification rule is constructed by combining statistical modeling with a rule-based approach. Prior knowledge about the higher probability for the presence of oil slicks around ships and oil platforms is incorporated into the model, In addition, knowledge about the external conditions like mind level and slick surroundings are taken into account. The presented algorithms are tested on 84 SAR images. The algorithm can discriminate between oil slicks and lookalikes with high accuracy. 94% of the oil slicks and 99% of the lookalikes mere correctly classified.