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ARAS-Net: A Meta-Learning-Based Few-Shot Small-Target Detection Network for Aerial Infrared Remote Sensing Images
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DOI：10.1109/lgrs.2026.3713883.png)
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Abstract
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Recent advances in infrared small-target detection and few-shot learning have yielded promising outcomes; however, the existing approaches address these challenges separately, resulting in limited generalization and robustness in complex aerial remote sensing environments. To overcome this limitation, we propose ARAS-Net, a meta-learning-based few-shot small-target detection framework tailored for aerial infrared remote sensing images. The proposed method incorporates an attention-guided context mechanism that enhances the distinction between targets and background in cluttered scenes, a mutual aggregation strategy that promotes rapid adaptation and generalization under limited sample conditions, and a multiscale feature enhancement design that effectively captures and represents small targets. Comprehensive experiments on benchmark aerial infrared datasets confirm that our approach consistently surpasses state-of-the-art methods in both detection accuracy and robustness under few-shot settings. The source code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/jlm-138/ARAS-Net</uri>
Keywords:
Aerial remote sensing
few-shot learning
infrared small-target detection
meta-learning
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