An ant colony optimization approach for SAR image segmentation

A novel SAR image segmentation algorithm, based on the Ant Colony Optimization (ACO) method is proposed in this paper. The method extended the ant colony algorithm to threshold optimization, two-dimension fuzzy entropy is used as objective function, and ant move direction is determined by the trail...

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Bibliographic Details
Published in2007 International Conference on Wavelet Analysis and Pattern Recognition Vol. 1; pp. 296 - 300
Main Authors Lan-Ying Cao, Liang-Zheng Xia
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.11.2007
Subjects
2-D fuzzy entropy
Algorithm design and analysis
Ant colony optimization
Entropy
Histograms
Image segmentation
Pixel
Segmentation
Speckle
Synthetic aperture radar
Testing
Wavelet analysis
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ISBN9781424410651
1424410657
ISSN2158-5695
DOI10.1109/ICWAPR.2007.4420682

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Summary:A novel SAR image segmentation algorithm, based on the Ant Colony Optimization (ACO) method is proposed in this paper. The method extended the ant colony algorithm to threshold optimization, two-dimension fuzzy entropy is used as objective function, and ant move direction is determined by the trail pheromone. Each ant in the colony will generate a path based on the relative positions of the nodes and feedback information about the best paths generated by previous colonies. The solution of each ant is improved by using a global optimization procedure. The proposed approach has been tested on different SAR images. Tests results show that, due to its ability of both finding good search paths and escaping from local minima, the proposed method could achieve a near-optimal solution to the SAR image segmentation problem.
ISBN:9781424410651
1424410657
ISSN:2158-5695
DOI:10.1109/ICWAPR.2007.4420682