结合邻域信息粒子群聚类用于SAR图像变化检测

doi:10.3969/j.issn.1001-2400.2015.01.030

J4 ›› 2015, Vol. 42 ›› Issue (1): 187-193.doi: 10.3969/j.issn.1001-2400.2015.01.030

结合邻域信息粒子群聚类用于SAR图像变化检测

刘逸¹;慕彩红¹;刘敬²

(1. 西安电子科技大学电子工程学院，陕西西安 710071；
2. 西安邮电大学电子工程学院，陕西西安 710121)

收稿日期:2013-11-03 出版日期:2015-02-20 发布日期:2015-04-14
通讯作者: 刘逸
作者简介:刘逸(1976-)，男，讲师，博士，E-mail: yiliu01@mail.xidian.edu.cn．
基金资助:
国家自然科学基金资助项目(61003199，61373111，61272279)；中央高校基本科研业务费专项资金资助项目(K5051202019，JB140216)；陕西省教育厅自然科学专项基金资助项目(2013JK1129)；陕西省自然科学基础研究计划资助项目(2014JQ5183)

Change detection for SAR images based on the particle swarm clustering algorithm using neighborhood information

LIU Yi¹;MU Caihong¹;LIU Jing²

(1. School of Electronic Engineering, Xidian Univ., Xi'an 710071, China;
2. School of Electronic Engineering, Xi'an Univ. of Posts ＆ Telecommunications, Xi'an 710121, China)

Received:2013-11-03 Online:2015-02-20 Published:2015-04-14
Contact: LIU Yi

摘要/Abstract

摘要：

SAR图像变化检测可以转化为对差异图的聚类问题．由于SAR图像本身容易受到斑点噪声干扰，为提高聚类效果提出了一种结合邻域信息的自适应粒子群聚类算法．该方法在模糊C均值原目标函数基础上，引入中心像素的邻域信息，并通过自适应粒子群的全局搜索来优化聚类中心．该方法还引入了自学习算子即粒子编码中的中心像素的隶属度，能够向其相邻像素的隶属度学习，并据此修正自身的隶属度值相关．实验结果表明，与模糊C均值和量子免疫克隆聚类算法相比，该方法利用了像素的邻域信息，从而增强了抗噪性能．与模糊局部信息C均值算法相比，该方法对图像细节保持能力较强，运行时间也较少．

关键词: 变化检测, SAR图像, 聚类, 粒子群优化

Abstract:

Change detection for SAR images can be transformed into the clustering for the difference image of SAR images. Since SAR images have speckle noise, a new adaptive particle swarm clustering algorithm using neighborhood information is proposed for improving the clustering results. The degrees of membership of the neighbors around each central pixel are introduced into the new objective function based on the fuzzy c-means (FCM) clustering algorithm, and the centers of clusters are optimized by the global searching of adaptive particle swarm. By the self-study operator of the proposed method, the degree of membership of each pixel can be revised based on the degrees of membership of all the neighboring pixels. Experimental results show that the proposed method is less sensitive to noise than the FCM and quantum-inspired immune clonal clustering algorithm by reason of using neighborhood information, and is better than the fuzzy local information c-means clustering algorithm on image detail preservation and run time.

Key words: change detection, SAR images, clustering, particle swarm optimization

刘逸;慕彩红;刘敬. 结合邻域信息粒子群聚类用于SAR图像变化检测[J]. J4, 2015, 42(1): 187-193.

LIU Yi;MU Caihong;LIU Jing. Change detection for SAR images based on the particle swarm clustering algorithm using neighborhood information[J]. J4, 2015, 42(1): 187-193.

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结合邻域信息粒子群聚类用于SAR图像变化检测

Change detection for SAR images based on the particle swarm clustering algorithm using neighborhood information

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