| 基于动态K均值聚类算法的SAR图像分割 |
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| 作者姓名： | 邢涛 黄友红 胡庆荣 李军 王冠勇 |
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| 作者单位： | 1.中国航天二院二十三所, 北京 100854;2.中国人民解放军驻航天二院二十三所军代表室, 北京 100854 |
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| 基金项目： | 国家自然科学基金（61271417）和高分专项青年创新基金（GFZX04060103）资助 |
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| 摘 要： | 针对SAR图像的分割问题，对K均值聚类算法进行研究.分析动态K均值聚类算法，用聚类样本数的正比函数对该聚类适应度函数进行平均，改进适应度函数的计算.毫米波SAR图像分割实验结果表明，对于城区建筑及路、桥场景的分割，改进后的动态K均值聚类算法和自适应动态K均值聚类算法的分割质量与改进前相同，但是分割时间有一定的减少，改进适应度函数后分割效率得到了提高.
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| 关 键 词： | 合成孔径雷达 图像分割 聚类 K均值 |
| 收稿时间： | 2016-01-11 |
| 修稿时间： | 2016-04-05 |
SAR image segmentation based on dynamical K-means clustering algorithm |
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| Authors: | XING Tao HUANG Youhong HU Qingrong LI Jun WANG Guanyong |
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| Institution: | 1.No. 23 Institute of the Second Academy of China Aerospace, Beijing 100854, China;2.The PLA Office in No. 23 Institute of the Second Academy of China Aerospace, Beijing 100854, China |
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| Abstract: | We present our study on SAR image segmentation based on K-means clustering. We analyze dynamical K-means clustering algorithms and improve the adaptation degree function computation method which divides the raw adaptation degree function by a direct ratio function of the sample number in clustering. Millimeter SAR image segmentation results verify that, for urban area, road, and bridge scenes segmentation, dynamical K-means clustering algorithm and adaptive dynamical K-means clustering algorithm with the improved adaptation degree function computation method have the same segmentation quality while the segmentation efficiency is higher than before. |
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| Keywords: | synthetic aperture radar(SAR) image segmentation clustering K-means |
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