SAR Change Detection Algorithm Combined with FFDNet Spatial Denoising

Authors

  • Yuqing Wu

    Institute of Geospatial Information, Information Engineering University, Zhengzhou, Henan, 450001, China
  • Qing Xu

    Institute of Geospatial Information, Information Engineering University, Zhengzhou, Henan, 450001, China
  • Zheng Zhang

    Institute of Geospatial Information, Information Engineering University, Zhengzhou, Henan, 450001, China
  • Jingzhen Ma

    Institute of Geospatial Information, Information Engineering University, Zhengzhou, Henan, 450001, China
  • Tianming Zhao

    Institute of Geospatial Information, Information Engineering University, Zhengzhou, Henan, 450001, China
  • Xinming Zhu

    Institute of Geospatial Information, Information Engineering University, Zhengzhou, Henan, 450001, China

DOI:

https://doi.org/10.30564/jees.v5i2.5980
Received: 21 September 2023 | Revised: 20 October 2023 | Accepted: 26 October 2023 | Published Online: 10 November 2023

Abstract

Objectives: When detecting changes in synthetic aperture radar (SAR) images, the quality of the difference map has an important impact on the detection results, and the speckle noise in the image interferes with the extraction of change information. In order to improve the detection accuracy of SAR image change detection and improve the quality of the difference map, this paper proposes a method that combines the popular deep neural network with the clustering algorithm.Methods: Firstly, the SAR image with speckle noise was constructed, and the FFDNet architecture was used to retrain the SAR image, and the network parameters with better effect on speckle noise suppression were obtained. Then the log ratio operator is generated by using the reconstructed image output from the network. Finally, K-means and FCM clustering algorithms are used to analyze the difference images, and the binary map of change detection results is generated. Results: The experimental results have high detection accuracy on Bern and Sulzberger's real data, which proves the effectiveness of the method.

Keywords:

SAR change detection, Image noise reduction, FFDNet, Difference diagram, Clustering algorithm

References

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How to Cite

Wu, Y., Xu, Q., Zhang, Z., Ma, J., Zhao, T., & Zhu, X. (2023). SAR Change Detection Algorithm Combined with FFDNet Spatial Denoising. Journal of Environmental & Earth Sciences, 5(2), 88–101. https://doi.org/10.30564/jees.v5i2.5980

Issue

Vol. 5 , Iss. 2 (October 2023)

Article Type

Article

License

Copyright © 2023 Author(s)

Creative Commons License

This is an open access article under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) License.

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