Few-shot SAR target classification with CLPN: a prototypical network combining unsupervised contrastive learning

In the field of Synthetic Aperture Radar (SAR), owing to the limitations of observation and imaging conditions, only a limited number of annotated samples are available for model training. Existing deep learning-based methods struggle to effectively extract discernible features, leading to degraded performance or even complete failure. To address these issues, this paper combines prototypical networks from meta-learning with unsupervised contrastive learning. By utilizing meta-learning, the proposed method ensures the model's classification performance under small sample conditions. Additionally, effective SAR data augmentation techniques are designed to incorporate unsupervised contrastive learning, enabling the model to better capture the similarities and differences between classes and enhance its generalization ability. Our proposed approach leverages both the supervised information from class labels and the unsupervised information inherent in the images, allowing the model to deeply explore the high-order information within the data and achieve accurate classification of few shot SAR targets. The recognition results on the MSTAR dataset, consisting of three target classes, demonstrate that the proposed method (CLPN) exhibits higher average recognition accuracy under the small sample condition and robustness to different depression angles.