Oil Spill Identification based on Textural Information of SAR Image

Oil spill pollution is a major environmental threat for many countries in the world, which can cause serious damage to marine environment. Synthetic aperture radar (SAR) has become a valuable tool for marine oil spill monitoring, because of its all-weather and all-day capabilities. However, interpretation of marine SAR imagery is often ambiguous, and some other look-alike features often pose a fundamental challenge to the identification of oil spills and make the discrimination between oil spills and the look-alikes become a necessary procedure. In this paper, co-occurrence matrix method is employed to extract textural features of marine SAR image first, then these features are analyzed and optimized, and then support machine vector (SVM) method is used to identify oil spills in SAR images. Experiments on several SAR images show that method proposed in this paper can improve the detection and identification of oil spill in SAR images.