| 遥感图像的半监督的改进FCM算法 |
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| 引用本文： | 邱磊,李国辉,代科学.遥感图像的半监督的改进FCM算法[J].计算机应用研究,2006,23(7):252-253,260. |
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| 作者姓名： | 邱磊 李国辉 代科学 |
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| 作者单位： | 国防科技大学,信息系统与管理学院,湖南,长沙,410073 |
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| 基金项目： | 国家高技术研究发展计划(863计划);湖南省教育厅高校科研项目 |
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| 摘 要： | 对模糊C均值算法进行了改进，采用更适合遥感图像的Mahalanobis距离代替欧氏距离，并在聚类中加入了先验信息。在聚类过程中，未标签的样本通过与已标签的样本进行相似性比较来提高算法的准确性。实验表明，改进的算法能有效提高算法准确度。
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| 关 键 词： | 模糊C均值算法(FCM算法) 遥感图像 半监督 聚类算法 |
| 文章编号： | 1001-3695（2006）07-0252-02 |
| 收稿时间： | 2005-06-13 |
| 修稿时间： | 2005-06-132005-09-05 |
Semi-supervised Improved Fuzzy C-Means Clustering to Remote-sensing Image |
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| QIU Lei,LI Guo-hui,DAI Ke-xue.Semi-supervised Improved Fuzzy C-Means Clustering to Remote-sensing Image[J].Application Research of Computers,2006,23(7):252-253,260. |
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| Authors: | QIU Lei LI Guo-hui DAI Ke-xue |
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| Abstract: | This article improves the fuzzy C-means method. The improved method adopts Mahalanobis distance which is more likely close to remote-sensing scatter map instead of Euclidean distance. And it adds apriori information into the patterns to change the method as a semi-supervised clustering. In the clustering process, the unlabelled patterns compare similarities with the labeled patterns, then the accuracy of the algorithm can be increased. This method is tested with an experiment, the results shows that the improved method can obviously increase the accuracy of the algorithm efficiently. |
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| Keywords: | Fuzzy C-Means Clustering(FCM) Remote-sensing Image Semi-supervised Clustering |
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