volume | PIER Journals

2022-12-22

Landslide Area Identification and Detection Method Based on Micro-Variation Monitoring Radar Images

Shaoshuai Zhang,

Dr. Shaoshuai Zhang

College of Information Engineering

Inner Mongolia University of Technology

China

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Yaolong Qi,

Professor Yaolong Qi

College of Information Engineering

Inner Mongolia University of Technology

China

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Pingping Huang,

Professor Pingping Huang

College of Information Engineering

Inner Mongolia University of Technology

China

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Weixian Tan,

Professor Weixian Tan

College of Information Engineering

Inner Mongolia University of Technology

China

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Yuejuan Chen

Dr. Yuejuan Chen

College of Information Engineering

Inner Mongolia University of Technology

China

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Progress In Electromagnetics Research Letters, Vol. 108, 65-73, 2023

doi:10.2528/PIERL22101110

Abstract

Micro-variation monitoring radar based on the differential interference principle can monitor objects prone to micro deformation. However, it is easily affected by human and environmental factors to cause the radar image to loss coherence in the long-term monitoring work, thus affecting the normal monitoring of radar. Therefore, it is of great significance to study the change detection method of micro-variation monitoring radar images, which can provide reference information and quantitative analysis for monitoring work. In this paper, a method of landslide area identification and detection based on micro-variation radar image is proposed. Based on the radar coherence coefficient image of time series, the difference image is produced by logarithmic ratio cumulation. The difference map is decomposed and denoised by wavelet transform, and then the final difference map is produced by reconstructing the processed wavelet coefficients. Finally, the improved K-means is used to cluster the difference map to get the change detection result image. The actual monitoring data of a mining area is used for variation detection. The results show that the proposed method retains the detailed information of the change area and removes a lot of noise. The difference map is easier to cluster, and the clustering result is more accurate.

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Shaoshuai Zhang, Yaolong Qi, Pingping Huang, Weixian Tan, and Yuejuan Chen, "Landslide Area Identification and Detection Method Based on Micro-Variation Monitoring Radar Images," Progress In Electromagnetics Research Letters, Vol. 108, 65-73, 2023.
doi:10.2528/PIERL22101110

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