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Communication

Challenges of Labelling Unknown Seabed Munition Dumpsites from Acoustic and Optical Surveys: A Case Study at Skagerrak

1
Faculty of Engineering and Design, University of Bath, Bath BA2 7AY, UK
2
Norwegian Defence Research Establishment (FFI), 2007 Kjeller, Norway
3
Faculty of Mathematics and Natural Sciences, University of Oslo, 0315 Oslo, Norway
*
Author to whom correspondence should be addressed.
Academic Editors: Gil Rito Gonçalves and Umberto Andriolo
Remote Sens. 2022, 14(11), 2619; https://doi.org/10.3390/rs14112619
Received: 28 March 2022 / Revised: 3 May 2022 / Accepted: 5 May 2022 / Published: 31 May 2022
(This article belongs to the Special Issue Remote Sensing for Mapping and Monitoring Anthropogenic Debris)
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. View Full-Text
Keywords: synthetic aperture sonar (SAS); unexplored ordnance (UXO); machine learning synthetic aperture sonar (SAS); unexplored ordnance (UXO); machine learning
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MDPI and ACS Style

Bryan, O.; Hansen, R.E.; Haines, T.S.F.; Warakagoda, N.; Hunter, A. Challenges of Labelling Unknown Seabed Munition Dumpsites from Acoustic and Optical Surveys: A Case Study at Skagerrak. Remote Sens. 2022, 14, 2619. https://doi.org/10.3390/rs14112619

AMA Style

Bryan O, Hansen RE, Haines TSF, Warakagoda N, Hunter A. Challenges of Labelling Unknown Seabed Munition Dumpsites from Acoustic and Optical Surveys: A Case Study at Skagerrak. Remote Sensing. 2022; 14(11):2619. https://doi.org/10.3390/rs14112619

Chicago/Turabian Style

Bryan, Oscar, Roy E. Hansen, Tom S.F. Haines, Narada Warakagoda, and Alan Hunter. 2022. "Challenges of Labelling Unknown Seabed Munition Dumpsites from Acoustic and Optical Surveys: A Case Study at Skagerrak" Remote Sensing 14, no. 11: 2619. https://doi.org/10.3390/rs14112619

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