Refined SAR Image Segmentation Algorithm Based on K-means Clustering

被引：0

作者：

Xing, Tao ^{[1
]}

Hu, Qingrong ^{[1
]}

Li, Jun ^{[1
]}

Wang, Guanyong ^{[1
]}

机构：

[1] Beijing Inst Radio Measurement, Beijing, Peoples R China

来源：

2016 CIE INTERNATIONAL CONFERENCE ON RADAR (RADAR) | 2016年

关键词：

synthetic aperture radar(SAR); image segmentation; clustering; K-means;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Study on SAR image segmentation based on K-means clustering. Analyzes and refined the adaptive moving K-means clustering algorithm by refined the adaptation degree function computation method which dividing the raw adaptation degree function by a direct ratio function of the sample number in clustering and presenting a new sample point separating rule on the clustering area which has the largest adaptation degree function. Millimeter SAR image segment results verify that the refined algorithm have better quality than K-means clustering algorithms in paper for city, road and bridge. Refined K-means clustering algorithm are more efficiency than the adaptive moving K-means clustering algorithm.

引用

页数：2

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