A Statistical Approach for Automatic Detection of Ocean Disturbance Features From SAR Images

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 holes in the segmented image are then filled using a recursive scanning method. The image is thinned and unwanted branches are removed using a graph-theory-based technique. Finally, an efficient linking algorithm based on geometric properties is proposed to detect the disturbance features. Our algorithm is evaluated using on both synthetic images with by various levels of added Gaussian noise and on actual SAR images from ERS-2, SEASAT, ENVISAT, and RADARSAT. The results of our approach is compared with those from existing approaches. Results show that, in comparison with the algorithms in literature, our algorithm is more accurate in extracting the features both in terms of the area and shape. In addition, our algorithm runs significantly faster.