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

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...

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Bibliographic Details
Published inRemote sensing (Basel, Switzerland) Vol. 14; no. 11; p. 2619
Main Authors Bryan, Oscar, Hansen, Roy Edgar, Haines, Tom S. F., Warakagoda, Narada, Hunter, Alan
Format Journal Article
LanguageEnglish
Norwegian
Published Basel MDPI AG 01.01.2022
Subjects
Acoustic surveying
Acoustics
Aperture
Automation
Autonomous underwater vehicles
Biological & chemical weapons
Bombs
Cameras
Deep learning
Explosives detection
Labeling
Labels
Machine learning
Military supplies
Ocean floor
Payloads
Sediments
Sensors
Sonar
synthetic aperture sonar (SAS)
Synthetic apertures
Target recognition
Taxonomy
Underwater vehicles
Unexploded ordnance
unexplored ordnance (UXO)
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Summary: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.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs14112619