Unsupervised SAR Image SegmentationBased on Immune K-Means Clustering
BO Hua1, MA Fu-Long2, JIAO Li-Cheng3
1.College of Information Engineering, Shanghai Maritime University, Shanghai 200135 1.Philips Research Asia, Shanghai 2002333. Institute of Intelligent Information Processing, Xidian University, Xi'an 710071
Abstract Combined with the information entropy characteristic of image texture and the co-occurrence-matrix concept, a practical unsupervised SAR image segmentation algorithm is presented based on immune K-means clustering. It overcomes the disadvantages of local optima and sensitivity to the values and noises, and has the same fast-convergence advantage as K-means method. The theoretical analysis and experimental results show that the proposed algorithm has low computing complexity and strong robustness.
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2008, Vol. 21 
