摘要
基于合成孔径雷达(SAR)图像的变化检测是遥感领域中一项具有挑战性的任务,如何在噪声鲁棒性和有效保留细节之间取得平衡是一个迫切需要解决的问题。然而,大多数SAR图像变化检测方法为了更好地抑制斑点噪声,不可避免地会在一定程度上丢失图像细节。为了解决这一问题,提出了一种基于演化多目标聚类的SAR图像变化检测多目标聚类算法,将变化检测问题转化为一个多目标优化问题。该方法同时构建了两个相互冲突的目标,即分别基于原始差异图与噪声滤波后差异图的聚类能量函数,并用基于分解的演化多目标优化算法MOEA/D对以上目标函数进行优化,实现对差异图不变区域与变化区域的聚类。利用该技术可得到一组变化检测图,用户可以根据自己的需求选择合适的结果。最后,在两个SAR图像数据集上进行了充分的实验,结果表明了该方法的有效性。
SAR images change detection is a challenging task in the field of remote sensing,and it is an urgent problem to keep trade-off between robustness to noise and effectiveness of preserving the details.However,in order to better suppress speckle noise,it is inevitable that most of change detection methods loss image details to some extent.In order to solve this problem,a multi-objective clustering algorithm based on MOEA/D is proposed for change detection in SAR images.The change detection problem is formulated as a multi-objective optimization problem.Two conflicting objectives are constructed and then optimized by the proposed multi-objective clustering algorithm simultaneously.Finally,we obtain a set of change detection maps by the proposed technique.And the users can choose an appropriate one to satisfy their requirements.Experimental results on two SAR images show that the proposed method works well.
作者
周宇
杨俊岭
党可林
ZHOU Yu;YANG Junling;DANG Kelin(School of Electronic Engineering,Xidian University,Xi’an 710071,China;Military Science Information Research Center,Academy of Military Sciences,Beijing 100142,China)
出处
《计算机科学》
CSCD
北大核心
2024年第9期140-146,共7页
Computer Science
关键词
SAR图像
变化检测
斑点噪声
图像细节
多目标优化
聚类
SAR images
Change detection
Speckle noise
Image details
Multi-objective optimization
Clustering
作者简介
周宇,born in 1983,Ph.D.His main research interests include machine learning and evolutionary computation.zhouyu@xidian.edu.cn;通信作者:杨俊岭,born in 1975,Ph.D.His main research interest is national defense artificial intelligence.20y02@sohu.com。
