Segmentation of Oil Spill Images With Illumination-Reflectance Based Adaptive Level Set Model

Abstract

This paper presents a novel method for segmenting the oil spill regions in the SAR satellite images taken in broad daylight using illumination-reflectance based level set model. These images of oil spills taken in broad daylight appear as a blend of dark areas with scintillations of glitter due to the illumination and reflectance components present. Most of the dark areas in the SAR images are the areas indicating oil spills because the oil dampens the capillary waves on the sea surface. The presence of the glitter induces speckle in SAR images. This does not only reduces the interpreter's ability to resolve fine detail, but also makes automatic segmentation of such images difficult. Segmentation of such images using conventional level set methods makes the process cumbersome and may lead to improper results. The accuracy of segmentation greatly depends on the amount of the illumination and reflectance (IR) components present in the images. To perform segmentation of such images we propose an adaptive level set evolution process based on the IR components in them. This can be achieved by combining a new signed pressure function which is derived from the amount illumination and reflectance present in the image. The IR components present in image are extracted by the process of homomorphic decomposition with the help of filters with specific cut off frequencies. This method is the first application successfully implemented on SAR images and the results are found to be superior when compared with earlier techniques. Comparative analysis is made with the conventional region based level sets in terms of accuracy of segmentation for complex images.