结合多阈值法的模糊聚类用于SAR图像变化检测  

Change detection for SAR images based on fuzzy clustering using multilevel thresholding

作　　者：刘逸[1,2] 寇卫东[1] 慕彩红[2] 

机构地区：[1]西安电子科技大学通信工程学院,陕西西安710071 [2]西安电子科技大学电子工程学院,陕西西安710071

出　　处：《西安电子科技大学学报》2013年第6期13-18,共6页Journal of Xidian University

基　　金：国家自然科学基金资助项目(61003199);中央高校基本科研业务费专项资金资助项目(K50510020015;K5051202019)

摘　　要：针对模糊局部信息C均值算法运算量较高的问题,提出了一种新的结合多阈值法的模糊聚类算法,并用于合成孔径雷达图像变化检测中的差异图聚类.首先利用多阈值法对差异图进行预分割,得到变化类、非变化类以及待判别类;之后利用模糊局部信息C均值算法对待判别类中的像素点集进行聚类,而在聚类过程中涉及到邻域像素点不属于待判别类时,其隶属度值将取确定值0或1.该方法提高了对合成孔径雷达图像变化检测的精度,且运算量较低.相关的实验结果表明,与模糊C均值算法和模糊局部信息C均值算法相比较,该方法的检测性能更好,而运行时间比模糊局部信息C均值算法的运算时间降低了70%多.A new fuzzy clustering algorithm using multilevel thresholding is proposed to reduce the computational complexity of the fuzzy local information c-means （FLICM） algorithm for solving the clustering problem on the difference image of change detection for SAR images. First, the pixels in the difference image are classified into the ＂changed＂ pixels, ＂unchanged＂ pixels and unknown status pixels by the multilevel thresholding procedure. Then the unknown status pixels are clustered by the FLICM. If the neighboring pixels in the FLICM are not the unknown status pixels, their degrees of membership are set to 1 or 0. The proposed method improves the precision in the change detection for SAR images with the low computational complexity. Experimental results show that the proposed method has the better performance than fuzzy c-means （FCM） and FLICM algorithms on the change detection for SAR images and that its run time is about 70% less than that of the FLICM algorithm.

关 键 词：变化检测 合成孔径雷达图像 聚类 分割 粒子群优化 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]