Comparison of the mapping resolution of the ACID synthetic aperture sonar with existing sidescan sonar systems

Riyait, V.S.; Lawlor, M.A.; Adams, A.E.; Hinton, O.R.; Sharif, B.S.

doi:10.1109/oceans.1994.364260

Cited by 5 publications

(1 citation statement)

References 8 publications

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“…In recent decades, side-scan sonar technology has emerged as an indispensable tool in marine science research owing to its efficient application in ocean exploration and underwater environmental monitoring. Side-scan sonar is capable of generating high-resolution images of underwater topography, which plays a pivotal role in mapping benthic habitats, classifying underwater objects, and conducting detailed studies of seafloor geomorphology [1][2][3]. However, the complexity of side-scan sonar images has historically posed challenges in extracting and interpreting their information [4].…”

Section: Introductionmentioning

confidence: 99%

Side-Scan Sonar Image Matching Method Based on Topology Representation

Yang,

Yu,

Wang

et al. 2024

JMSE

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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.

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