MSMC: Multi-Scale Embedding and Meta-Contrastive Learning for Few-Shot Fine-Grained SAR Target Classification

Constrained by observation conditions and high inter-class similarity, effective feature extraction and classification of synthetic aperture radar (SAR) targets in few-shot scenarios remains a persistent challenge. To address this issue, this article proposes a few-shot fine-grained SAR target classification method based on multi-scale embedding network and meta-contrastive learning (MSMC). Specifically, the MSMC integrates two complementary training pipelines; the first employs metric-based meta-learning to facilitate few-shot classification, while the second adopts an auxiliary training strategy to enhance feature diversity through contrastive learning. Furthermore, a shared multi-scale embedding network (MSEN) is designed to extract discriminative multi-scale features via adaptive candidate region generation and joint multi-scale embedding. The experimental results on the MSTAR dataset demonstrate that the proposed method achieves superior few-shot fine-grained classification performance compared to existing methods.