Abstract
This paper presents a new segmentation-based algorithm for oil spill feature extraction from Synthetic Aperture Radar (SAR) intensity images. The proposed algorithm combines a Voronoi tessellation, Bayesian inference and Markov Chain Monte Carlo (MCMC) scheme. The shape and distribution features of dark spots can be obtained by segmenting a scene covering an oil spill and/or look-alikes into two homogenous regions: dark spots and their marine surroundings. The proposed algorithm is applied simultaneously to several real SAR intensity images and simulated SAR intensity images which are used for accurate evaluation. The results show that the proposed algorithm can extract the shape and distribution parameters of dark spot areas, which are useful for recognizing oil spills in a further classification stage.
Highlights
Oil spills from operational discharges and ship accidents always have calamitous impacts on the marine environment and ecosystems, even with small oil coverage volumes
The basic idea behind the segmentation algorithm for dark spot feature extraction lies in partitioning D into two homogenous regions D1 and D2 corresponding to the dark spot areas and its surroundings, respectively
In this paper the technique is introduced to design a region-based segmentation algorithm for oil spill feature extraction. It means that Voronoi tessellation is used to partition the image domain into sub-regions corresponding to components of oil spill regions or their surroundings
Summary
Oil spills from operational discharges and ship accidents always have calamitous impacts on the marine environment and ecosystems, even with small oil coverage volumes. Other physical phenomena, for example, low-wind areas, wind-shadow areas near coasts, rain cells, currents, upswelling zones, biogenic films, internal waves, and oceanic or atmospheric fronts, can generate dark areas, known as look-alikes, in SAR images [5,6] Another factor which influences the backscatter level and the visibility of oil slicks on the sea surface is the wind level. The work in this paper focuses on the feature extraction of detected dark spots [9] The task at this stage involves defining and acquiring the features existing in SAR images, which can be efficiently used in the classification stage to distinguish oil spills from look-alikes.
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