# Self-Supervised Learning for Improved Synthetic Aperture Sonar Target Recognition

arXiv: https://arxiv.org/abs/2307.15098 · HTML: https://mlsanity.com/paper/2307.15098/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 34 references that are in the map.
- First posted on arXiv 27 Jul 2023.

## Builds on (most cited first, 17 of 17)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Bootstrap your own latent: A new approach to self-supervised Learning](https://mlsanity.com/paper/2006.07733/) (2020)
- [Improved Baselines with Momentum Contrastive Learning](https://mlsanity.com/paper/2003.04297/) (2020)
- [Revisiting Self-Supervised Visual Representation Learning](https://mlsanity.com/paper/1901.09005/) (2019)
- [Geography-Aware Self-Supervised Learning](https://mlsanity.com/paper/2011.09980/) (2020)
- [Self-supervised Learning in Remote Sensing: A Review](https://mlsanity.com/paper/2206.13188/) (2022)
- [SSL4EO-S12: A Large-Scale Multi-Modal, Multi-Temporal Dataset for Self-Supervised Learning in Earth Observation](https://mlsanity.com/paper/2211.07044/) (2022)
- [Self-Supervised Learning of Remote Sensing Scene Representations Using Contrastive Multiview Coding](https://mlsanity.com/paper/2104.07070/) (2021)
- [Self-supervised Vision Transformers for Joint SAR-optical Representation Learning](https://mlsanity.com/paper/2204.05381/) (2022)
- [Self-supervised remote sensing feature learning: Learning Paradigms, Challenges, and Future Works](https://mlsanity.com/paper/2211.08129/) (2022)
- [Self-supervised Learning for Sonar Image Classification](https://mlsanity.com/paper/2204.09323/) (2022)
- [Evaluating Self and Semi-Supervised Methods for Remote Sensing Segmentation Tasks](https://mlsanity.com/paper/2111.10079/) (2021)
- [Semi-supervised learning for joint SAR and multispectral land cover classification](https://mlsanity.com/paper/2108.09075/) (2021)
- [Evaluating the Label Efficiency of Contrastive Self-Supervised Learning for Multi-Resolution Satellite Imagery](https://mlsanity.com/paper/2210.06786/) (2022)
- [Urban feature analysis from aerial remote sensing imagery using self-supervised and semi-supervised computer vision](https://mlsanity.com/paper/2208.08047/) (2022)
- [Multiband SAS Imagery](https://mlsanity.com/paper/1808.02792/) (2018)
- [Iterative, Deep, and Unsupervised Synthetic Aperture Sonar Image Segmentation](https://mlsanity.com/paper/2107.14563/) (2021)

## Built on by (most cited first, 2 of 2)

- [Advances in Self-Supervised Learning for Synthetic Aperture Sonar Data Processing, Classification, and Pattern Recognition](https://mlsanity.com/paper/2308.11633/) (2023)
- [BenthicDINO: Physics-Informed Self-Distillation for View-Invariant Side-Scan Sonar Representations](https://mlsanity.com/paper/2608.23215/) (2026)

## 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.