SAR Image Segmentation Using Voronoi Tessellation and Bayesian Inference Applied to Dark Spot Feature Extraction

Sensors

Quanhua Zhao¹  Yu Li¹ 

[1] Institute for Remote Sensing Science and Application, School of Geomatics, Liaoning Technical University, Fuxin 123000, China; E-Mails:

关键词: Voronoi tessellation;    Bayesian inference;    feature extraction;    oil spill;    dark spots;   

DOI  :  10.3390/s131114484

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2013 by the authors; licensee MDPI, Basel, Switzerland.