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.

Key words: Synthetic Aperture Radar(SAR), image generation, automatic target recognition, generative adversarial network