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Synthetic Aperture Sonar (SAS) imaging has become a crucial technology forunderwater exploration because of its unique ability to maintain resolution atincreasing ranges, a characteristic absent in conventional sonar techniques.However, the effective application of deep learning to SAS data processing isoften limited due to the scarcity of labeled data. To address this challenge,this paper proposes MoCo-SAS that leverages self-supervised learning (SSL) forSAS data processing, classification, and pattern recognition. The experimentalresults demonstrate that MoCo-SAS significantly outperforms traditionalsupervised learning methods, as evidenced by significant improvements observedin terms of the F1-score. These findings highlight the potential of SSL inadvancing the state-of-the-art in SAS data processing, offering promisingavenues for enhanced underwater object detection and classification.

Advances in Self-Supervised Learning for Synthetic Aperture Sonar Data Processing, Classification, and Pattern Recognition