SAR Image Target Recognition Using Diffusion Model and Scattering Information

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 m...

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Bibliographic Details
Published in:IEEE geoscience and remote sensing letters 2024, Vol.21, p.1-5
Main Authors: Sun, Sheng-Kai, He, Zi, Fan, Zhen-Hong, Ding, Da-Zhi
Format: Article
Language:English
Subjects:
Automatic target recognition
Data processing
Diffusion
Diffusion model
Diffusion models
Extreme values
few-shot samples
Image enhancement
Image processing
Image quality
Image recognition
Information processing
Probabilistic models
Radar imaging
Radar polarimetry
SAR (radar)
Scattering
Similarity
Synthetic aperture radar
synthetic aperture radar (SAR)
Target recognition
Training
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Description
Summary: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.
ISSN:1545-598X
1558-0571
DOI:10.1109/LGRS.2024.3466233