基于变分法与Markov随机场模糊局部信息聚类法的SAR影像变化检测

王昶; 张永生; 王旭

doi:10.13203/j.whugis20190167

基于变分法与Markov随机场模糊局部信息聚类法的SAR影像变化检测

SAR Image Change Detection Based on Variational Method and Markov Random Field Fuzzy Local Information C-Means Clustering Method

摘要

摘要: 为了提高合成孔径雷达(synthetic aperture radar, SAR)影像变化检测的精度, 提出一种基于变分法与马尔可夫随机场模糊局部信息聚类(Markov random field fuzzy local information C-means clustering, MRFFLICM)的SAR影像变化检测方法。首先融合对数比影像和对数均值比影像来构建差异影像; 然后采用变分去噪模型去除差异影像的噪声; 最后利用马尔可夫随机场将空间邻域信息引入到模糊局部信息C均值聚类算法中, 提高聚类的性能。对两组不同时相真实SAR影像数据进行对比实验, 结果表明, 提出的变分去噪方法能够避免去除微小变化区域, 有效抑制SAR影像的斑点噪声, 同时MRFFLICM方法可以有效提高变化检测的精度, 提升了变化检测方法的适应性。

Abstract:
Objectives In order to improve the accuracy of SAR(synthetic aperture radar) image change detection, this paper proposes a method of SAR image change detection based on variational method and Markov random field fuzzy local information C-means clustering(MRFFLICM) method.
Methods Firstly, we fuse the logarithmic ratio images and logarithmic mean ratio images to construct the difference image. Secondly, variational denoising model is established to remove the noise from difference images. Finally, the spatial neighborhood information is introduced into fuzzy local information C-means clustering method by using Markov random field to improve the clustering performance.
Results Experiments on two real SAR datasets show that the proposed variational denoising method can avoid removing the small change region and effectively suppress speckle noise of SAR image.
Conclusions The MRFFLICM method can effectively improve the precision of change detection, thus enhancing the adaptability of change detection method.

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