Explorations of Contrastive Learning in the Field of Small Sample SAR ATR

Synthetic aperture radar (SAR) image recognition plays a significant role in civil and military target perception, and deep learning methods are commonly used for SAR image recognition. However, for small-sample SAR datasets, deep learning methods suffer from problems such as overfitting and gradient explosion during the training process. In this study, first, we pre-train the deep neural network on the unlabeled SAR image dataset with self-supervised contrastive learning, so as to obtain a deep feature extraction network with better representation ability. Then, we transfer the pre-trained weights to the small-sample SAR image data for fine-tuning and linear evaluation, which in turn can improve the overfitting problem of small-sample SAR image recognition. This research can give scientific and objective small-sample target recognition laws, which will promote the long development of the scientific field of deep representation learning research and can generate great economic benefits in the field of SAR image target recognition.