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

Abstract

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

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

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