Self-Supervised Learning for Improved Synthetic Aperture Sonar Target
  Recognition
BW Sheffield
This study explores the application of self-supervised learning (SSL) for
improved target recognition in synthetic aperture sonar (SAS) imagery. The
unique challenges of underwater environments make traditional computer vision
techniques, which rely heavily on optical camera imagery, less effective. SAS,
with its ability to generate high-resolution imagery, emerges as a preferred
choice for underwater imaging. However, the voluminous high-resolution SAS data
presents a significant challenge for labeling; a crucial step for training deep
neural networks (DNNs).
  SSL, which enables models to learn features in data without the need for
labels, is proposed as a potential solution to the data labeling challenge in
SAS. The study evaluates the performance of two prominent SSL algorithms,
MoCov2 and BYOL, against the well-regarded supervised learning model, ResNet18,
for binary image classification tasks. The findings suggest that while both SSL
models can outperform a fully supervised model with access to a small number of
labels in a few-shot scenario, they do not exceed it when all the labels are
used.
  The results underscore the potential of SSL as a viable alternative to
traditional supervised learning, capable of maintaining task performance while
reducing the time and costs associated with data labeling. The study also
contributes to the growing body of evidence supporting the use of SSL in remote
sensing and could stimulate further research in this area.