Diffusion-augmented direct classification: A few-shot learning framework for Synthetic Aperture Radar image automatic target recognition

Deep learning-based (DL-based) synthetic aperture radar automatic target recognition technology (SAR-ATR) has undergone extensive development, demonstrating superiority over other competitive methods. However, the intrinsic requirement of deep learning for a large labeled dataset restricts its practical application. Moreover, some DL-based few-shot SAR-ATR methods are overly complex, hindering their deployment in real-world applications. In addressing these obstacles, our solution introduces a straightforward yet efficient few-shot learning approach titled Diffusion-Augmented Direct Classification for few-shot SAR-ATR applications. The proposed method adopts a two-stage paradigm, where a diffusion model first learns from unlabeled data and then produces synthetic samples to train a recognition model. In the upstream stage, a lightweight diffusion-based image generator build upon the shuffle-residual network structure is trained on a limited number of annotated SAR images to generate artificial training samples for the downstream recognition model. In the downstream stage, a Siamese network-based recognition model and a similarity training procedure are proposed to train the model on a combination of real-world and artificial samples, thereby improving recognition accuracy. A projection expansion layer is proposed to improve the efficiency of cosine similarity loss in the downstream. Experiments conducted on the Moving and Stationary Target Acquisition and Recognition dataset demonstrated that our method outperformed other few-shot learning methods concerning recognition accuracy in SAR-ATR tasks. Specifically, our method achieves over 73% accuracy in a 5-sample-per-class scenario and over 85% accuracy in a 10-samples-per-class scenario. Source code of our paper is available at https://github.com/yikuizhai/DADC .