Advances in Self-Supervised Learning for Synthetic Aperture Sonar Data
  Processing, Classification, and Pattern Recognition
Brandon Sheffield Frank E. Bobe III Bradley Marchand Matthew S. Emigh
Synthetic Aperture Sonar (SAS) imaging has become a crucial technology for
underwater exploration because of its unique ability to maintain resolution at
increasing ranges, a characteristic absent in conventional sonar techniques.
However, the effective application of deep learning to SAS data processing is
often limited due to the scarcity of labeled data. To address this challenge,
this paper proposes MoCo-SAS that leverages self-supervised learning (SSL) for
SAS data processing, classification, and pattern recognition. The experimental
results demonstrate that MoCo-SAS significantly outperforms traditional
supervised learning methods, as evidenced by significant improvements observed
in terms of the F1-score. These findings highlight the potential of SSL in
advancing the state-of-the-art in SAS data processing, offering promising
avenues for enhanced underwater object detection and classification.