Oil Spill Detection in Synthetic Aperture Radar Images Using Lipschitz-Regularity and Multiscale Techniques

Abstract

This research adapts an effective change detection approach originally applied to mapping fire scar from a stationary synthetic aperture radar (SAR) scene to the problem of oil spills from SAR data. The method presented here combines several advanced image processing techniques to mitigate some of the common performance limitations of SAR-based oil spill detection. Principally among these limitations are the following. First, the radar cross section of the areas affected by an oil spill strongly depends on wind and wave effects, and is, therefore, highly variable. Second, the radar cross section of oil covered water is often indistinguishable from other dark ocean features such as low wind areas, which leads to errors and uncertainties in oil spill detection. In this paper, we introduce a multi-image analysis, the Lipschitz regularity (LR), and wavelet transforms as a combined approach to mitigate these performance limitations. We show that the LR parameter is much less sensitive to variations of wind and waves in an oil spill SAR imagery, lending itself well for normalizing and suppressing ocean background using image ratio processing. We combine LR processing with a multiscale technique based on the wavelet transform to additionally achieve high-quality noise suppression. To describe the performance of this approach under controlled conditions, we applied our method to simulated SAR data of wind-driven oceans with oil spills and low wind areas. We also applied our method to several real-world oil spill data acquired during the 2010 Deep Water Horizon spill in the Gulf of Mexico.