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.

这项研究探讨了自监督学习（SSL）在合成孔径声纳（SAS）图像识别改进中的应用，结果表明，尽管两个SSL模型在少样本情况下可以优于完全监督模型，但在使用全部标签时不能超过其性能，这有助于减少数据标记的时间和成本，同时提供了远程感知中使用SSL的证据。

自监督学习提升合成孔径声纳目标识别