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| 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 |
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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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Received: 08 June 2006
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2008, Vol. 21 
