Journal of Marine Science and Engineering (May 2024)

Side-Scan Sonar Image Matching Method Based on Topology Representation

  • Dianyu Yang,
  • Jingfeng Yu,
  • Can Wang,
  • Chensheng Cheng,
  • Guang Pan,
  • Xin Wen,
  • Feihu Zhang

DOI
https://doi.org/10.3390/jmse12050782
Journal volume & issue
Vol. 12, no. 5
p. 782

Abstract

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In the realm of underwater environment detection, achieving information matching stands as a pivotal step, forming an indispensable component for collaborative detection and research in areas such as distributed mapping. Nevertheless, the progress in studying the matching of underwater side-scan sonar images has been hindered by challenges including low image quality, intricate features, and susceptibility to distortion in commonly used side-scan sonar images. This article presents a comprehensive overview of the advancements in underwater sonar image processing. Building upon the novel SchemaNet image topological structure extraction model, we introduce a feature matching model grounded in side-scan sonar images. The proposed approach employs a semantic segmentation network as a teacher model to distill the DeiT model during training, extracting the attention matrix of intermediate layer outputs. This emulates SchemaNet’s transformation method, enabling the acquisition of high-dimensional topological structure features from the image. Subsequently, utilizing a real side-scan sonar dataset and augmenting data, we formulate a matching dataset and train the model using a graph neural network. The resulting model demonstrates effective performance in side-scan sonar image matching tasks. These research findings bear significance for underwater detection and target recognition and can offer valuable insights and references for image processing in diverse domains.

Keywords