Multiresolution approach to oil spill detection in ERS-1 SAR images

SAR images from the ERS satellites have proved to be helpful data for identification of oil spills. Because the presence of oil slicks on sea surface increases the surface tension of sea water, the surface wave motion is significantly depressed. This effect relatively reduces the sea surface roughness, decreases the radar backscattered energy and enables oil slicks to be discernible from the radar image. The use of fractal dimension, which is related to the concept of surface "roughness", as a feature for classification, improves the oil spill detection, since enhances texture discrimination with respect to first and second order derivative operators, e.g., DoG and LoG. This paper describes cc multi-resolution approach based on fractal geometry for oil spilt detection in ERS SAR images. The proposed multi-resolution algorithm is based on the normalized Laplacian pyramid which provides a band-pass description of the image. Thanks to normalization of each layer of the pyramid by its lour-pass version, the image noise becomes independent on the image signal, and a reliable estimate of the fractal dimension can be computed from ratio of power spectra at different scales. The experimental results carried out both an synthetic and ERS-1 SAR images prove the effectiveness of the fractal-based approach for the classification of oil spills.