SAR vehicle image generation with integrated deep imaging geometric information

Abstract

Deep learning is widely applied in synthetic aperture radar (SAR) target recognition. However, the high cost of collecting real SAR target data leads to insufficient data volume and diversity of SAR target datasets, making it challenging to support deep learning models. Therefore, it is necessary to expand SAR target datasets through data generation. However, existing SAR target data generation methods do not fully utilize the SAR imaging geometric information of targets. This results in generating image data that does not conform to SAR physical imaging laws. To address this problem, this paper proposes a SAR vehicle data generation method that integrates deep imaging geometric information. Compared to SAR, optical data, which contains rich geometric structural information of targets, is relatively easy to collect. Therefore, the proposed method computes the dominant scattering edge (DSE) of targets under different radar incident angles with optical data. Then, based on generative adversarial networks (GAN), a SAR vehicle target data generation model is constructed. To guide the model in learning SAR imaging geometric laws of targets, the computed DSE is integrated into the generation network with multi-scale attention mechanism modules. Compared to existing SAR target data generation methods, our proposed method can generate SAR vehicle target data with distinct dominant scattering edges, and effectively addresses the issue of generated target data not conforming to SAR imaging physical laws. Metrics such as Fréchet Inception Distance (FID), scattering intensity difference, and Structural Similarity Index (SSIM) are adopted for evaluation. The SSIM between the generated and the real SAR target data reaches 0.834, the FID minimum of 96.98, and the scattering intensity difference minimum of 0.37 dB, all indicating that the SAR target data generated by the proposed method is similar to real ones in terms of deep feature, scattering intensity, and image structure. Finally, the SAR target data generated by the proposed method is verified through target recognition experiments with VGG16 and ResNet50 as backbone network. The average recognition accuracy increases by 2.80% and 2.14%, respectively. The target recognition results demonstrate that the generated SAR data can help improve target recognition accuracy, thereby validating the effectiveness of data generated by the proposed method.

Keywords

• Synthetic aperture radar
• Deep learning
• Generating adversarial networks
• SAR data generation