mlsanityData to 7 Oct 2026

Self-Supervised Learning for Improved Synthetic Aperture Sonar Target Recognition

arXiv 2307.15098 ↗First posted 27 Jul 2023cs.CV cs.LG eess.IVcited by 2

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Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 17 of its 34 references that are in the map. First posted on arXiv 27 Jul 2023.

Builds on

17 of 34 references.

References · 17

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2020Bootstrap your own latent: A new approach to self-supervised Learningcited by 4,712
  3. 2020Improved Baselines with Momentum Contrastive Learningcited by 2,282
  4. 2019Revisiting Self-Supervised Visual Representation Learningcited by 250
  5. 2020Geography-Aware Self-Supervised Learningcited by 190
  6. 2022Self-supervised Learning in Remote Sensing: A Reviewcited by 137
  7. 2022SSL4EO-S12: A Large-Scale Multi-Modal, Multi-Temporal Dataset for Self-Supervised Learning in Earth Observationcited by 82
  8. 2021Self-Supervised Learning of Remote Sensing Scene Representations Using Contrastive Multiview Codingcited by 58
  9. 2022Self-supervised Vision Transformers for Joint SAR-optical Representation Learningcited by 40
  10. 2022Self-supervised remote sensing feature learning: Learning Paradigms, Challenges, and Future Workscited by 24
  11. 2022Self-supervised Learning for Sonar Image Classificationcited by 12
  12. 2021Evaluating Self and Semi-Supervised Methods for Remote Sensing Segmentation Taskscited by 7
5 more
  1. 2021Semi-supervised learning for joint SAR and multispectral land cover classificationcited by 4
  2. 2022Evaluating the Label Efficiency of Contrastive Self-Supervised Learning for Multi-Resolution Satellite Imagerycited by 3
  3. 2022Urban feature analysis from aerial remote sensing imagery using self-supervised and semi-supervised computer visioncited by 2
  4. 2018Multiband SAS Imagerycited by 1
  5. 2021Iterative, Deep, and Unsupervised Synthetic Aperture Sonar Image Segmentationcited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 0; 2025: 0; 2026: 1.

2 papers.

  1. 2023Advances in Self-Supervised Learning for Synthetic Aperture Sonar Data Processing, Classification, and Pattern Recognitioncited by 1
  2. 2026BenthicDINO: Physics-Informed Self-Distillation for View-Invariant Side-Scan Sonar Representationscited by 0