Object-oriented approach to oil spill detection using ENVISAT ASAR images

Abstract

The growing importance of oil spill detection as part of a rapid response system to oil pollution requires theongoing development of algorithms. The aim of this study was to create amethodology for improving manual classification at the scale of entire water bodies, focusing on its repeatability. This paper took an object-oriented approach to radar image analysis and put particular emphasis on adaptation to thespecificity of seas like the Baltic. Pre-processing using optimised filters enhanced the capability of amultilevel hierarchical segmentation, in order to detect spills of different sizes, forms and homogeneity, which occur as a result of shipping activities. Confirmed spills detected in ENVISAT/ASAR images were used to create a decision-tree procedure that classifies every distinct dark object visible inSAR images into one out of four categories, which reflect growing probability of the oil spill presence: look-alikes, dubious spots, blurred spots and potential oil spills. Our objective was to properly mark known spills on ASAR scenes and to reduce the number of false-positives by eliminating (classifying as background or look-alike) as many objects aspossible from the vast initial number of objects appearing on full-scale images. A number of aspects were taken into account in the classification process. The method’s performance was tested on agroup of 26 oil spills recorded by HELCOM: 96.15% ofthem were successfully identified. The final target group was narrowed down to about 4% ofdark objects extracted from ASAR images. Although aspecialist is still needed to supervise thewhole process of oil spill detection, this method gives an initial view, substantial for further evaluation of the scenes and risk estimation. It may significantly accelerate thepace of manual image analysis and enhance the objectivity of assessments, which are key aspects in operational monitoring systems.