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SAR Image Segmentation Using Voronoi Tessellation and Bayesian Inference Applied to Dark Spot Feature Extraction
Institute for Remote Sensing Science and Application, School of Geomatics, Liaoning Technical University, Fuxin 123000, China
The Key Laboratory of Marine Oil Spill Identification and Damage Assessment Technology, North China Sea Environmental Monitoring Center, State Oceanic Administration, Qingdao 266033, China
* Author to whom correspondence should be addressed.
Received: 12 September 2013; in revised form: 30 September 2013 / Accepted: 30 September 2013 / Published: 25 October 2013
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.
Keywords: Voronoi tessellation; Bayesian inference; feature extraction; oil spill; dark spots
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Cite This Article
MDPI and ACS Style
Zhao, Q.; Li, Y.; Liu, Z. SAR Image Segmentation Using Voronoi Tessellation and Bayesian Inference Applied to Dark Spot Feature Extraction. Sensors 2013, 13, 14484-14499.
Zhao Q, Li Y, Liu Z. SAR Image Segmentation Using Voronoi Tessellation and Bayesian Inference Applied to Dark Spot Feature Extraction. Sensors. 2013; 13(11):14484-14499.
Zhao, Quanhua; Li, Yu; Liu, Zhenggang. 2013. "SAR Image Segmentation Using Voronoi Tessellation and Bayesian Inference Applied to Dark Spot Feature Extraction." Sensors 13, no. 11: 14484-14499.