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SAR Image Target Recognition Using Diffusion Model and Scattering Information
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AI
DOI：10.1109/LGRS.2024.3466233.png)
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Abstract
En 中文
The synthetic aperture radar (SAR) imaging environment, especially with limited samples, poses a serious challenge to automatic target recognition (ATR) in modern electronic reconnaissance systems. To enhance SAR image recognition performance, this study proposes a technique leveraging a diffusion model and scattering information. This method involves SAR image generation and scattering information processing to improve generalization with few-shot samples. First, the number of few-shot SAR samples was augmented using the denoising diffusion probabilistic model (DDPM). Then, the scattering information is extracted to stably calculate SAR image similarity from the scattering mechanism. Finally, the recognition task is effectively accomplished through the optimal integration of the recognition network and scattering similarity. Simulation results demonstrate that the proposed method achieves superior SAR image generation quality and recognition accuracy compared to the existing methods when the available data are extremely limited.
Keywords:
Radar polarimetry
Scattering
Target recognition
Image recognition
Training
Diffusion models
Information processing
Diffusion model
few-shot samples
synthetic aperture radar (SAR)
target recognition
Journal
IF:
16.4
Papers:
1.0W
Citations:
5.1K
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