Unsupervised change detection method for ground-based radar images based on convolutional neural network

Shaoshuai Zhang; Yaolong Qi; Pingping Huang; Weixian Tan; Yuejuan Chen

doi:10.1117/12.3005164

20 October 2023 Unsupervised change detection method for ground-based radar images based on convolutional neural network

Shaoshuai Zhang, Yaolong Qi, Pingping Huang, Weixian Tan, Yuejuan Chen

Author Affiliations +

Proceedings Volume 12916, Third International Conference on Signal Image Processing and Communication (ICSIPC 2023); 1291608 (2023) https://doi.org/10.1117/12.3005164
Event: Third International Conference on Signal Image Processing and Communication (ICSIPC 2023), 2023, Kunming, China

Abstract

Ground-based radar has been widely used for deformation monitoring and early warning of geological hazard potential areas. However, during long-term monitoring, ground-based radar images are vulnerable to human and environmental factors leading to severe decoherence. The study of ground-based radar image change detection can provide reference information for its long-term monitoring. Based on this, an unsupervised change detection method based on convolutional neural network (CNN) for ground-based radar images is proposed in this paper. First, the interference principle to extract change information is used for the first time for the change detection task, aiming to improve the accuracy of the initial extraction of change regions. Secondly, the fuzzy c-means clustering algorithm is used to obtain the pseudo-label matrix with categories, and the appropriate neighborhood image blocks with pseudo-labels are selected as training samples to train CNN. Finally, the change detection results of ground-based radar images are obtained using the trained CNN. Experiments were conducted using actual measurement data from ground-based radar in a monitoring task in a mining area in China and compared with other methods to verify the effectiveness of this paper's method and more accurate detection results.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Shaoshuai Zhang, Yaolong Qi, Pingping Huang, Weixian Tan, and Yuejuan Chen "Unsupervised change detection method for ground-based radar images based on convolutional neural network", Proc. SPIE 12916, Third International Conference on Signal Image Processing and Communication (ICSIPC 2023), 1291608 (20 October 2023); https://doi.org/10.1117/12.3005164

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KEYWORDS

Radar sensor technology

Radar

Education and training

Image filtering

Tunable filters

Binary data

Convolution

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