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| 基于免疫K-means聚类的无监督SAR图像分割 | |
| 引用本文： | 薄华,马缚龙,焦李成. 基于免疫K-means聚类的无监督SAR图像分割[J]. 模式识别与人工智能, 2008, 21(3): 376-380 |
| 作者姓名： | 薄华 马缚龙 焦李成 |
| 作者单位： | 上海海事大学信息工程学院,上海,200135;飞利浦亚洲研究院,上海,200233;西安电子科技大学智能信息处理研究所,西安,710071 |
| 摘 要： | 利用图像纹理的信息熵特征,并结合空间矩阵的概念,提出一种基于免疫K-means聚类的无监督SAR图像分割算法.免疫规划的K-means聚类克服收敛结果易陷于局部极值的缺点,且保持K-means算法快速收敛的特点.信息熵的应用可有效抑制相干斑噪声的影响,空间矩阵的引入实现聚类过程中类别的自动合并.该算法执行复杂度不高,对噪声的影响有较强的鲁棒性,分割结果较好,是一种实用的SAR图像分割算法. |
| 关 键 词： | 合成孔径雷达(SAR)图像 免疫K-means聚类 信息熵 无监督分割 |
| 收稿时间： | 2006-06-08 |
Unsupervised SAR Image Segmentation Based on Immune K-Means Clustering |
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| BO Hua,MA Fu-Long,JIAO Li-Cheng. Unsupervised SAR Image Segmentation Based on Immune K-Means Clustering[J]. Pattern Recognition and Artificial Intelligence, 2008, 21(3): 376-380 | |
| Authors: | BO Hua MA Fu-Long JIAO Li-Cheng |
| Affiliation: | 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. |
| Keywords: | Synthetic Aperture Radar (SAR) Image Immune K-Means Clustering Information Entropy Unsupervised Segmentation |
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