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 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.