Segmentation of SAR Intensity Imagery With a Voronoi Tessellation, Bayesian Inference, and Reversible Jump MCMC Algorithm

This paper presents a region-based approach to segmentation of the satellite synthetic aperture radar (SAR) intensity imagery. The approach is based on a Voronoi tessellation, the Bayesian inference, and the reversible jump Markov chain Monte Carlo (RJMCMC) algorithm. By Voronoi tessellation, the ap...

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Bibliographic Details
Published in:IEEE transactions on geoscience and remote sensing 2010-04, Vol.48 (4), p.1872-1881
Main Authors: Yu Li, Li, J., Chapman, M.A.
Format: Article
Language:English
Subjects:
Algorithms
Applied geophysics
Bayesian analysis
Bayesian inference
Bayesian methods
Data processing
Earth sciences
Earth, ocean, space
Exact sciences and technology
Image segmentation
Inference algorithms
Internal geophysics
Labels
Layout
Markov analysis
maximum a posteriori (MAP)
Monte Carlo methods
Polygons
Power measurement
reversible jump Markov chain Monte Carlo (RJMCMC)
Satellites
Segmentation
Speckle
Studies
Synthetic aperture radar
synthetic aperture radar (SAR)
Tessellation
Voronoi tessellation
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Summary:This paper presents a region-based approach to segmentation of the satellite synthetic aperture radar (SAR) intensity imagery. The approach is based on a Voronoi tessellation, the Bayesian inference, and the reversible jump Markov chain Monte Carlo (RJMCMC) algorithm. By Voronoi tessellation, the approach partitions a SAR image into a set of polygons corresponding to the components of the segmented homogenous regions. Each polygon is assigned a label to indicate a homogeneous region. The labels for all the polygons form a label field, which is characterized by an improved Potts model. The intensities of pixels in each polygon are assumed to satisfy identical and independent gamma distributions in terms of their label. Following the Bayesian paradigm, the posterior distribution that characterizes the SAR image segmentation can be obtained up to the integration constant. Then, a RJMCMC scheme is designed to simulate the posterior distribution and estimate its parameters. Finally, an optimal segmentation is obtained by the maximum a posteriori algorithm. The results obtained on both real Radarsat-1/2 and simulated SAR intensity images show that our approach works well and is very promising.
ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2009.2033588