MHD-Protonet: Margin-Aware Hard Example Mining for SAR Few-Shot Learning via Dual-Loss Optimization

• Published in: Algorithms Vol. 18; no. 8; p. 519
• Main Authors: Zayani, Marii, Toumi, Abdelmalek, Khalfallah, Ali
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 16.08.2025
• Subjects: Accuracy; Adaptation; Armored vehicles; Classification; Datasets; Deep learning; Efficiency; Image classification; Machine learning; Optimization; Prototypes; Queries; Radar imaging; Radar targets; Remote sensing; Semantics; Signal processing; Synthetic aperture radar; Target recognition
• ISSN: 1999-4893; 1999-4893
• DOI: 10.3390/a18080519

Synthetic aperture radar (SAR) image classification under limited data conditions faces two major challenges: inter-class similarity, where distinct radar targets (e.g., tanks and armored trucks) have nearly identical scattering characteristics, and intra-class variability, caused by speckle noise, pose changes, and differences in depression angle. To address these challenges, we propose MHD-ProtoNet, a meta-learning framework that extends prototypical networks with two key innovations: margin-aware hard example mining to better separate confusable classes by enforcing prototype distance margins, and dual-loss optimization to refine embeddings and improve robustness to noise-induced variations. Evaluated on the MSTAR dataset in a five-way one-shot task, MHD-ProtoNet achieves 76.80% accuracy, outperforming the Hybrid Inference Network (HIN) (74.70%), as well as standard few-shot methods such as prototypical networks (69.38%), ST-PN (72.54%), and graph-based models like ADMM-GCN (61.79%) and DGP-NET (68.60%). By explicitly mitigating inter-class ambiguity and intra-class noise, the proposed model enables robust SAR target recognition with minimal labeled data.