结合邻域信息粒子群聚类用于SAR图像变化检测

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。

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

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.