A Statistical Approach to Preprocess and Enhance C-Band SAR Images in Order to Detect Automatically Marine Oil Slicks

Abstract

The aim of this paper was to propose a new methodology for preprocessing and enhancing C-band synthetic aperture radar (SAR) images for the automatic detection of marine oil slicks. The proposed methodology includes three processing levels: preprocessing, thresholding, and binary cleaning. The first level is to correct the heterogeneity of brightness in SAR images caused by the non-Lambertian reflection of the radar signal on the sea surface. This heterogeneity can be justified by: the distance from the nadir (incidence angle effect), the interaction between wind direction and radar pulse, and the wide swath mode. The second level consists of a thresholding step. The third level is to clean the binary output images from noise residues. Several preprocessing and cleaning methods have been tested and evaluated by a qualification engine that compares the automatically detected patches with a training data set of manually detected dark patches. The training data set includes oil slicks and lookalikes. As a result, the “best” preprocessing method that homogenizes the brightness of C-band SAR scenes and optimizes the automatic detection of marine oil slicks is based on an adaptation to the C-band MODel. As for the cleaning process, the tested morphological methods show that small object removal followed by a morphological closing optimizes the automatic detection of marine oil slicks.