西安电子科技大学学报（自然科学版）2025，Vol.52Issue(2)：101-112,12.DOI:10.19665/j.issn1001-2400.20250105
ARWCGAN:一种高质量的多类别SAR图像生成方法
ARWCGAN:a method for high-quality multi-category SAR image generation
摘要
Abstract
In the field of Synthetic Aperture Radar(SAR)Automatic Target Recognition(ATR),the availability of high-quality training datasets is often severely limited.Existing SAR image generation methods based on Generative Adversarial Networks(GANs)suffer from training instability and low-quality outputs.To address these challenges,we propose the Attentional Residual Wasserstein Conditional Generative Adversarial Network(ARWCGAN)for generating high-quality multi-category SAR images.ARWCGAN features attentional residual layers to enhance SAR image feature extraction,thus improving the detail and texture of the generated images.It also utilizes a combined WGAN-GP(Wasserstein Generative Adversarial Network with Gradient Penalty)loss function and classification loss function to improve the training stability and generated image diversity.We conducted generation experiments on the MSTAR dataset and evaluated the generated images from three perspectives:qualitative visual inspection,quantitative quality assessment,and contribution to the ATR model performance.Experimental results demonstrate that ARWCGAN is capable of generating high-quality images,significantly enhancing the recognition accuracy of ATR models.关键词
合成孔径雷达/图像生成/自动目标识别/生成对抗网络Key words
Synthetic Aperture Radar(SAR)/image generation/automatic target recognition/generative adversarial network分类
计算机与自动化引用本文复制引用
郑洋,王榕旭,郭开泰,梁继民..ARWCGAN:一种高质量的多类别SAR图像生成方法[J].西安电子科技大学学报（自然科学版）,2025,52(2):101-112,12.基金项目
国家自然科学基金项目(62101416,62476205,62301405) （62101416,62476205,62301405）
