SAR Image Segmentation Based on Immune Genetic Algorithm and Gaussian Mixture Models

In this paper, an effective synthetic aperture radar image segmentation method is proposed. Gaussian mixture models optimized by greedy expectation maximization algorithm are applied. The immune genetic algorithm is employed to initialize greedy expectation maximization algorithm and search the opti...

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Bibliographic Details
Main Authors: Ya-nan Liu, Yu-tang Guo, Qin Lin, Bin Luo
Format: Conference Proceeding
Language:English
Subjects:
Analytical models
Computer science
Gaussian Mixture Models
Genetic algorithms
Greedy EM Algorithm
Image segmentation
Immune Genetic Algorithm
Immune system
Medical simulation
Optical imaging
Remote sensing
Signal processing algorithms
Synthetic aperture radar
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Summary:In this paper, an effective synthetic aperture radar image segmentation method is proposed. Gaussian mixture models optimized by greedy expectation maximization algorithm are applied. The immune genetic algorithm is employed to initialize greedy expectation maximization algorithm and search the optimal values in the whole range, instead of general k-means algorithm, which is different from the traditional algorithm. Experimental results show our method can get better results for target segmentation. It can effectively segment the object from SAR images and inhibit speckle noise.
DOI:10.1109/AICI.2009.319