结合多阈值法的模糊聚类用于SAR图像变化检测

doi:10.3969/j.issn.1001-2400.2013.06.003

Abstract

Abstract:

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.

Key words: change detection, synthetic aperture radar images, clustering, segmentation, particle swarm optimization

LIU Yi;KOU Weidong;MU Caihong. Change detection for SAR images based on fuzzy clustering using multilevel thresholding[J].J4, 2013, 40(6): 13-18.

References

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

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