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LI Can, WANG Zengfu, ZHANG Xiaoxuan, et al. Land-sea clutter classification methodbased on multi-channel graph convolutional networks[J]. Journal of Radars, in press. doi: 10.12000/JR24165
Citation: LI Can, WANG Zengfu, ZHANG Xiaoxuan, et al. Land-sea clutter classification methodbased on multi-channel graph convolutional networks[J]. Journal of Radars, in press. doi: 10.12000/JR24165
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Land-sea Clutter Classification Method Based on Multi-channel Graph Convolutional Networks

DOI: 10.12000/JR24165

  • School of Automation, Northwestern Polytechnical University, Xi’an 710129, China

Funds:  The National Natural Science Foundation of China (62473317, U21B2008)
More Information
  • Corresponding author: WANG Zengfu, wangzengfu@nwpu.edu.cn
  • Received Date: 2024-08-15
  • Rev Recd Date: 2024-10-03
    • Available Online: 2024-10-12
    Abstract
  • Land-sea clutter classification is essential for boosting the target positioning accuracy of skywave over-the-horizon radar. This classification process involves discriminating whether each azimuth-range cell in the Range-Doppler (RD) map is overland or sea. Traditional deep learning methods for this task require extensive, high-quality, and class-balanced labeled samples, leading to long training periods and high costs. In addition, these methods typically use a single azimuth-range cell clutter without considering intra-class and inter-class relationships, resulting in poor model performance. To address these challenges, this study analyzes the correlation between adjacent azimuth-range cells, and converts land-sea clutter data from Euclidean space into graph data in non-Euclidean space, thereby incorporating sample relationships. We propose a Multi-Channel Graph Convolutional Networks (MC-GCN) for land-sea clutter classification. MC-GCN decomposes graph data from a single channel into multiple channels, each containing a single type of edge and a weight matrix. This approach restricts node information aggregation, effectively reducing node attribute misjudgment caused by data heterogeneity. For validation, RD maps from various seasons, times, and detection areas were selected. Based on radar parameters, data characteristics, and sample proportions, we construct a land-sea clutter original dataset containing 12 different scenes and a land-sea clutter scarce dataset containing 36 different configurations. The effectiveness of MC-GCN is confirmed, with the approach outperforming state-of-the-art classification methods with a classification accuracy of at least 92%.

     

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