5 Citations (SciVal)


The disposal of unexploded ordnance (UXOs) at sea is a global problem. The mapping and remediation of historic UXOs can be assisted by autonomous underwater vehicles (AUVs) carrying sensor payloads such as synthetic aperture sonar (SAS) and optical cameras. AUVs can image large areas of the seafloor in high resolution, motivating an automated approach to UXO detection. Modern methods commonly use supervised machine learning which requires labelled examples from which to learn. This work investigates the often-overlooked labelling process and resulting dataset using an example historic UXO dumpsite at Skagerrak. A counterintuitive finding of this work is that optical images cannot be relied on for ground truth as a significant number of UXOs visible in SAS images are not in optical images, presumed buried. Given the lack of ground truth, we use an ordinal labelling scheme to incorporate a measure of labeller uncertainty. We validate this labelling regime by quantifying label accuracy compared to optical labels with high confidence. Using this approach, we explore different taxonomies and conclude that grouping objects into shells, bombs, debris, and natural gave the best trade-off between accuracy and discrimination
Original languageEnglish
Article number2619
JournalRemote Sensing
Issue number11
Publication statusPublished - 31 May 2022

Bibliographical note

Funding Information:
Funding: This research was funded by UK Research and Innovation (UKRI EP/S023437/1).

Funding Information:
Acknowledgments: The authors thank the Norwegian Coastal Administration for funding the 2015 and 2016 Skagerrak data collections. The authors also thank the scientists and the crew onboard FFI’s research vessel H.U. Sverdrup II, and the HUGIN AUV operators for collecting the data in the 2015, 2016, and 2019 Skagerrak missions. The authors also acknowledge Erik Makino Bakken at the Norwegian Defence Research Establishment (FFI) for generating the camera mosaics of Skagerrak used in this study.

Publisher Copyright:
© 2022 by the authors. Licensee MDPI, Basel, Switzerland.


  • machine learning
  • synthetic aperture sonar (SAS)
  • unexplored ordnance (UXO)

ASJC Scopus subject areas

  • Earth and Planetary Sciences(all)


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