Multi-scale region-level graph convolutional network for unsupervised SAR image change detection

Jingxing Zhu; Rui Liu; Feng Wang; Hongjian You

doi:10.1117/12.3029734

21 June 2024 Multi-scale region-level graph convolutional network for unsupervised SAR image change detection

Jingxing Zhu, Rui Liu, Feng Wang, Hongjian You

Proceedings Volume 13167, International Conference on Remote Sensing, Mapping, and Image Processing (RSMIP 2024); 131672A (2024) https://doi.org/10.1117/12.3029734
Event: International Conference on Remote Sensing, Mapping and Image Processing (RSMIP 2024), 2024, Xiamen, China

Abstract

With the rapid advancement of synthetic aperture radar (SAR) sensors, it has become more important to extract change information between high-resolution SAR images. Considering the efficacy and robustness of segmentation-based strategy in solving fine-resolution images, as well as the excellent performance of deep learning in feature extraction, therefore, in this paper, we propose an unsupervised region-level graph convolutional network (URGCN) for SAR change detection. Firstly, we propose a multi-temporal joint segmentation method to generate multi-level regions. This method can simultaneously segment bi-temporal SAR images and automatically align bi-temporal regions during segmentation process. We then perform pre-classification on difference images generated by neighborhood ratio detector. The high confidence training samples are selected through hierarchical clustering. Thirdly, we encode the segmented regions are as a graph structure and construct GCN model. The graph can capture the spatial and temporal structure information. Finally, the GCN model parameters are optimized by labeled samples and inferring unlabeled regions. Experimental results on two VHR SAR change detection datasets demonstrated that the proposed method can extract complete change information with high accuracy compared to alternative approaches.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Jingxing Zhu, Rui Liu, Feng Wang, and Hongjian You "Multi-scale region-level graph convolutional network for unsupervised SAR image change detection", Proc. SPIE 13167, International Conference on Remote Sensing, Mapping, and Image Processing (RSMIP 2024), 131672A (21 June 2024); https://doi.org/10.1117/12.3029734

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KEYWORDS

Synthetic aperture radar

Image segmentation

Education and training

Feature extraction

Matrices

Deep learning

Sensors

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