Ocean disturbance feature detection from SAR images — An adaptive statistical approach

Extraction of features from images has been a goal of researchers since the early days of remote sensing. This paper presents a statistical approach to detect dark curvilinear features due to ocean disturbances caused by wind, movements of surface or underwater objects and oil spill from SAR images. The image is first enhanced to emphasize the dark curvilinear features using a statistical approach. Then the curvilinear features are segmented using an iterative approach. The image is thinned to detect the final position of the disturbance features. Our algorithm is evaluated on actual SAR images from ERS-2, SEASAT, ENVISAT and RADARSAT.