解决合成孔径雷达目标识别中少量新类学习难题
Few-Shot Class-Incremental Learning For Efficient SAR Automatic Target Recognition
- 双分支结构结合傅里叶变换捕捉长程空间依赖
- 在MSTAR数据集上优于现有方法，实现高效增量识别
- 适合资源受限的实时雷达目标识别场景
合成孔径雷达自动目标识别（SAR-ATR）系统需应对实际应用中的增量识别挑战。数据稀缺仍是传统SAR-ATR技术难以克服的障碍。为此，我们提出一种基于双分支架构的少样本增量学习（FSCIL）框架，聚焦局部特征提取，利用离散傅里叶变换与全局滤波器捕捉长程空间依赖。引入轻量级交叉注意力机制，融合领域特定特征与全局依赖，确保鲁棒特征交互，同时通过极少的尺度偏移参数保持计算效率。框架结合焦点损失以增强类别间区分度，以及中心损失以强化类内紧凑性，提升类别分界。在MSTAR基准数据集上的实验表明，所提框架在少样本增量学习场景下的SAR-ATR任务中持续优于现有先进方法，验证了其在真实场景中的有效性。
原文摘要 · Abstract (English)
Synthetic aperture radar automatic target recognition (SAR-ATR) systems have rapidly evolved to tackle incremental recognition challenges in operational settings. Data scarcity remains a major hurdle that conventional SAR-ATR techniques struggle to address. To cope with this challenge, we propose a few-shot class-incremental learning (FSCIL) framework based on a dual-branch architecture that focuses on local feature extraction and leverages the discrete Fourier transform and global filters to capture long-term spatial dependencies. This incorporates a lightweight cross-attention mechanism that fuses domain-specific features with global dependencies to ensure robust feature interaction, while maintaining computational efficiency by introducing minimal scale-shift parameters. The framework combines focal loss for class distinction under imbalance and center loss for compact intra-class distributions to enhance class separation boundaries. Experimental results on the MSTAR benchmark dataset demonstrate that the proposed framework consistently outperforms state-of-the-art methods in FSCIL SAR-ATR, attesting to its effectiveness in real-world scenarios.
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