BS-ACGAN: A High-Resolution Multi-Class SAR Image Generation Approach via Conditional GAN

• Published in: 2025 IEEE 2nd International Conference on Deep Learning and Computer Vision (DLCV) pp. 1 - 5
• Main Authors: Han, Bingyu, Li, Jiahui, Wei, Shunjun, Hu, Yifei, Wang, Mou, Zhang, Xiaoling, Zhou, Yuanyuan
• Format: Conference Proceeding
• Language: English
• Published: IEEE 06.06.2025
• Subjects: Accuracy; auxiliary classifier generative adversarial network; Computational modeling; Computer architecture; Generative adversarial networks; image generation; Marine vehicles; Radar polarimetry; SAR; Scattering; self-attention mechanism; Synthetic aperture radar; Target recognition; Training
• DOI: 10.1109/DLCV65218.2025.11088846

Deep learning-based synthetic aperture radar (SAR) target recognition and detection technologies have demonstrated significant potential. However, challenged by the unique imaging mechanism and complex scattering characteristics of SAR systems, acquiring large-scale high-quality SAR image datasets poses substantial challenges, which severely limits model training and performance improvements, which in turn reduces detection effectiveness. To address these issues, this paper proposes a self-attention-enhanced high-resolution multi-category SAR image generative adversarial network named Big Self-attention Auxiliary Classifier Generative Adversarial Network (BS-ACGAN). The model builds upon the BigGAN architecture by embedding self-attention modules into multi-scale feature layers of both generator and discriminator, strengthening cross-category global feature relationship modeling. Simultaneously, it incorporates the conditional adversarial training mechanism of the ACGAN framework to achieve multi-category generation. Experimental results on both a self-built ship dataset and the e Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset demonstrate that compared with Re-ACGAN and ACGAN, BSACGAN delivers superior generation performance. In classification tasks using expanded datasets, the average accuracy shows significant improvement over the original dataset and outperforms existing comparative models.