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ZHAO Yiming, WU Youming, DAI Wei, et al. A few-shot SAR fine-grained aircraft detection and recognition method guided by strong scattering dynamic prototypes[J]. Journal of Radars, in press. doi: 10.12000/JR26060
Citation: ZHAO Yiming, WU Youming, DAI Wei, et al. A few-shot SAR fine-grained aircraft detection and recognition method guided by strong scattering dynamic prototypes[J]. Journal of Radars, in press. doi: 10.12000/JR26060
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A Few-shot SAR fine-grained Aircraft Detection and Recognition Method Guided by Strong Scattering Dynamic Prototypes

DOI: 10.12000/JR26060 CSTR: 32380.14.JR26060
  • 1.

    Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100190, China

  • 2.

    University of Chinese Academy of Sciences, Beijing 100190, China

  • 3.

    School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100190, China

  • 4.

    National Key Laboratory of Target Cognition and Application Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100190, China

Funds:  The National Natural Science Foundation of China (62425115)
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    Abstract
  • Synthetic aperture radar (SAR) image interpretation has a wide range of applications, with SAR aircraft detection and recognition being a significant branch. However, collecting and annotating SAR aircraft samples is inherently difficult, leading to a scarcity of training data. Thus, developing few-shot methods for SAR aircraft detection and recognition is urgently needed. The complex SAR imaging environment results in unstable target feature representation, making it difficult for detection networks to adaptively manage disturbances from intricate SAR backgrounds. These factors limit the accuracy of aircraft detection and recognition under few-shot conditions. To address these challenges, this study proposes a few-shot SAR fine-grained aircraft detection and recognition method guided by strong scattering dynamic prototypes. This approach integrates strong scattering physical priors into a meta-metric learning framework. Detection and recognition performance are enhanced through two key aspects: target feature enhancement and task-adaptive network adjustment. A dynamic prototype generation module is introduced to extract strong scattering points from SAR images and create physical attention masks. High-level semantic features are anchored to the physical geometric structure of targets, enabling robust feature representation. These features are then adaptively fused with prototypes to produce dynamic prototypes. A dynamic prototype guidance module, which maps dynamic prototypes from semantic space to parameter space, is also proposed. This enables adaptive adjustments to network weight updates, feature inputs, and prediction outputs, thereby improving the model’s rapid adaptation capability for novel categories. The proposed method enhances the stability of SAR target feature representation and reduces background clutter interference. Experiments conducted on the CSAR-AC dataset demonstrate that the proposed method outperforms mainstream few-shot object detection algorithms under both 1- and 5-shot settings, significantly improving few-shot SAR aircraft detection and recognition performance in complex scenes.

     

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