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

被引：0

作者：

Liu, Yi ^{[1
,2
]}

Kou, Weidong ^{[1
]}

Mu, Caihong ^{[2
]}

机构：

[1] School of Telecommunication Engineering, Xidian Univ., Xi'an 710071, China

[2] School of Electronic Engineering, Xidian Univ., Xi'an 710071, China

来源：

Xi'an Dianzi Keji Daxue Xuebao/Journal of Xidian University | 2013年 / 40卷 / 06期

关键词：

Algorithm for solving - Change detection - Clustering - Clustering problems - Difference images - Local information - Low computational complexity - Multilevel thresholding;

D O I：

10.3969/j.issn.1001-2400.2013.06.003

中图分类号：

学科分类号：

摘要：

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.

引用

页码：13 / 18

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