An object‐oriented methodology to detect oil spills

TLDR

The study presents an automated methodology for detecting oil spills in full synthetic aperture radar (SAR) high‑resolution images. The method uses an object‑oriented approach with adaptive thresholding and image segmentation to identify dark formations, applies two empirical formulas and a fuzzy classifier to distinguish oil spills from look‑alikes, and incorporates knowledge bases of sea environments to refine classification. The method achieved 99.5 % accuracy for oil spills and 98.8 % for look‑alikes across 12 SAR images, successfully detecting various types of fresh and degraded spills.

Abstract

A new automated methodology for oil spill detection is presented, by which full synthetic aperture radar (SAR) high‐resolution image scenes can be processed. The methodology relies on the object‐oriented approach and profits from image segmentation techniques to detected dark formations. The detection of dark formations is based on a threshold definition that is fully adaptive to local contrast and brightness of large image segments. For the detection process, two empirical formulas are developed that also permit the classification of oil spills according to their brightness. A fuzzy classification method is used to classify dark formations as oil spills or look‐alikes. Dark formations are not isolated and features of both dark areas and sea environment are considered. Various sea environments that affect oil spill shape and boundaries are grouped in two knowledge bases, used for the classification of dark formations. The accuracy of the method for the 12 SAR images used is 99.5% for the class of oil spills, and 98.8% for that of look‐alikes. Fresh oil spills, fresh spills affected by natural phenomena, oil spills without clear stripping, small linear oil spills, oil spills with broken parts and amorphous oil spills can be successfully detected.