Journal
SENSORS
Volume 13, Issue 11, Pages 14484-14499Publisher
MDPI AG
DOI: 10.3390/s131114484
Keywords
Voronoi tessellation; Bayesian inference; feature extraction; oil spill; dark spots
Funding
- Key Laboratory of Marine Oil Spill Identification and Damage Assessment Technology [201211]
- National Natural Science Foundation of China [41271435, 41301479]
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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.
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