Oil Spill Detection Methods in SAR Images

Abstract

Oil spill has tremendous effect on marine environment. Therefore, it is significant for protecting marine environment to get oil spill information timely. Presently, the methods which most countries adopt to detect oil spill are mainly direct detection or remote sensing methods. Among these methods, Synthetic Aperture Radar (SAR), one technique of remote sensing methods, has been becoming a hot research field nowadays. This paper concludes main characters of SAR images in oil spill detection. It introduces, compares and analyses the oil spill detection steps and realized techniques. There are four steps in oil spill detection process, including filtering, target detection, feature extraction and classification. Filtering can be completed by several filtering techniques, such as Lee, advanced Lee and Frost. General speaking, there are four methods can realize target detection, which are single threshold, adaptive threshold, wavelet transform and max entropy method. About the third step, feature extraction, 12 features can be used to represent oil spill. Classification is a key and difficult step in oil spill detection, in which algorithms based on Bayes and neural network can be adopted. Finally, the paper points out the development direction of oil spill detection in SAR images.