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

WANG Chang; ZHANG Yongsheng; WANG Xu

doi:10.13203/j.whugis20190167

Volume 46 Issue 6

Jun. 2021

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Geomatics and Information Science of Wuhan University > 2021 > 46(6): 844-851. > DOI: 10.13203/j.whugis20190167

WANG Chang, ZHANG Yongsheng, WANG Xu. SAR Image Change Detection Based on Variational Method and Markov Random Field Fuzzy Local Information C-Means Clustering Method[J]. Geomatics and Information Science of Wuhan University, 2021, 46(6): 844-851. DOI: 10.13203/j.whugis20190167

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

WANG Chang^{1, 2,},
ZHANG Yongsheng²,
WANG Xu^{2, 3}

1.
School of Civil Engineering, University of Science and Technology Liaoning, Anshan 114051, China
2.
Institute of Geospatial Information, Information Engineering University, Zhengzhou 450001, China
3.
Surveying and Mapping Engineering Institute, Liaoning Vocational College of Ecological Engineering, Shenyang 110101, China

Funds:

The National Natural Science Foundation of China 41671409

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Author Bio:
WANG Chang, PhD, associate professor, specializes in remote sensing image processing. E-mail: Wangchang324@163.com
Received Date: November 22, 2020
Published Date: June 04, 2021

Graphical Abstract

Abstract

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.
- variational method,
- Markov random field,
- fuzzy local information C-means clustering method,
- SAR images,
- change detection

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