Few-Shot Class-Incremental Learning For Efficient SAR Automatic Target Recognition

• URL: http://arxiv.org/abs/2505.19565v1
• Date: Mon, 26 May 2025 06:25:30 GMT
• Title: Few-Shot Class-Incremental Learning For Efficient SAR Automatic Target Recognition
• Authors: George Karantaidis, Athanasios Pantsios, Ioannis Kompatsiaris, Symeon Papadopoulos,
• Abstract summary: We propose a few-shot class-incremental learning (FSCIL) framework based on a dual-branch architecture that focuses on local feature extraction.<n>This incorporates a lightweight cross-attention mechanism that fuses domain-specific features with global dependencies to ensure robust feature interaction.<n> Experimental results on the MSTAR benchmark dataset demonstrate that the proposed framework consistently outperforms state-of-the-art methods in FSCIL SAR-ATR.
• Score: 13.783950035836593
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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