A Coarse-to-Fine Hierarchical Feature Learning for SAR Automatic Target Recognition With Limited Data

• Published in: IEEE journal of selected topics in applied earth observations and remote sensing 2024, Vol.17, p.13646-13656
• Main Authors: Wen, Yan, Wang, Xihao, Peng, Lihe, Qiao, Yu
• Format: Article
• Language: English
• Subjects: Ablation; Automatic target recognition; Automatic target recognition (ATR); coarse-to-fine; Data augmentation; Data models; Deep learning; Effectiveness; Feature extraction; Feature recognition; hierarchical feature learning; Machine learning; Optimization; Parameter identification; Radar polarimetry; SAR (radar); Synthetic aperture radar; synthetic aperture radar (SAR); Target recognition; Training
• ISSN: 1939-1404; 2151-1535
• DOI: 10.1109/JSTARS.2024.3423377

With the rapid advancements in deep learning, Synthetic Aperture Radar (SAR) Automatic Target Recognition (ATR) has seen significant improvements in performance. However, the effectiveness of even the most advanced deep-learning-based ATR methods is limited by the scarcity of training samples. This challenge has sparked growing interest in SAR ATR under data-constrained conditions in recent years. Most current approaches for SAR ATR with limited data enhance recognition through data augmentation, specialized modules, or contrastive learning-based loss functions. However, effectively utilizing limited supervision signals to identify key features remains a significant challenge that existing methods have not thoroughly addressed. In our research, we introduce a novel coarse-to-fine hierarchical feature learning strategy for SAR ATR with limited data. Starting with a feature extractor that produces multi-level features, we implement a coarse-to-fine gradual feature constraint to optimize each level using limited supervision signals. This approach simplifies parameter search and ensures effective feature utilization from coarse to fine granularity. Additionally, our method enhances the compactness within classes and the separability between classes of features at various levels. This is achieved by capitalizing on the consistency of features across multiple levels, thereby progressively enhancing the features and, in turn, boosting the model's overall performance. To validate our approach, we conducted recognition and comparative experiments on the MSTAR and OpenSARShip datasets. The results demonstrate our method's exceptional performance in limited-sample recognition scenarios. Moreover, ablation studies confirm the robustness of our approach, underscoring its potential in addressing the challenges of SAR ATR with limited data.