1887

Abstract

Summary

Accurate object detection and identification is a requirement when mapping the seafloor, either for environmental or commercial purposes. Synthetic aperture sonar (SAS) is improving the ability to correctly identify objects with greater ranges, higher resolution, and faster collection speeds. Kraken Robotics’ MINSAS has been used in harbor surveys to identify debris, and the 3 x 3.33 cm resolution provides very clear images, resulting in definitive object identification and classification. SAS imagery also provides information on object or seabed composition, distinguishing between dense or soft materials based on the intensity of signal returns. For example, metals are represented as bright features while non-metals (softer objects or marine life), return softer features with lower intensity values. The ability to accurately identify surface-based debris allows for improved planning of debris removal or remediation work.

Loading

Article metrics loading...

/content/papers/10.3997/2214-4609.202520046
2025-09-07
2026-02-15
Loading full text...

Full text loading...

References

  1. Abu, A., & Diamant, R. (2022). Underwater object classification combining SAS and transferred optical-to-SAS Imagery. The Hatter Department of Marine Technologies, Haifa University, 199 Aba Koushy Ave., Mount Carmel, Haifa, 3498838, Israel. https://arxiv.org/pdf/2304.11875
    [Google Scholar]
  2. Amr Z.Hamouda (2021. Distinguish seabed objects utilizing different marine acoustic techniques. National Institute of Oceanography and Fisheries. Egyptian Journal of Petroleum
    [Google Scholar]
  3. Dillon, J., & Charron, R. (2019). Resolution measurement for Synthetic Aperture Sonar. OCEANS 2019 MTS/IEEE SEATTLE, 1–6. https://doi.org/10.23919/oceans40490.2019.8962823
    [Google Scholar]
  4. International Hydrographic Organization (IHO). n.d. Manual on Hydrography (C-13). Accessed April 23, 2025. https://iho.int/uploads/user/pubs/cb/c-13/english/C-13_Chapter_4.pdf
    [Google Scholar]
  5. Steele, S., & Lyons, A. P. (2024). An experimental test of end fire synthetic aperture sonar for sediment characterisation. IET Radar, Sonar & Navigation, 18(11), 2057–2065. https://doi.org/10.1049/rsn2.12615
    [Google Scholar]
  6. Steele, S.-M. (2023). “A Unified Semantic Segmentation and Object Detection Framework for Synthetic Aperture Sonar Imagery,” 2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW), Rhodes Island, Greece, 2023, pp. 1–5, doi: 10.1109/ICASSPW59220.2023.10193155.
    https://doi.org/10.1109/ICASSPW59220.2023.10193155 [Google Scholar]
/content/papers/10.3997/2214-4609.202520046
Loading
/content/papers/10.3997/2214-4609.202520046
Loading

Data & Media loading...

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error