Synthetic SAR Data Generator Using Pix2pix cGAN Architecture for Automatic Target Recognition

Abstract

Synthetic Aperture Radar (SAR) technology has unique advantages but faces challenges in obtaining enough data for noncooperative target classes. We propose a method to generate synthetic SAR data using a modified pix2pix Conditional Generative Adversarial Networks (cGAN) architecture. The cGAN is trained to create synthetic SAR images with specific azimuth and elevation angles, demonstrating its capability to closely mimic authentic SAR imagery through convergence and collapsing analyses. The study uses a model-based algorithm to assess the practicality of the generated synthetic data for Automatic Target Recognition (ATR). The results reveal that the classification accuracy achieved with synthetic data is comparable to that attained with original data, highlighting the effectiveness of the proposed method in mitigating the limitations imposed by noncooperative SAR data scarcity for ATR. This innovative approach offers a promising solution to craft customized synthetic SAR data, ultimately enhancing ATR performance in remote sensing.

Keywords

• Automatic target recognition
• classification
• conditional generative adversarial networks
• data augmentation
• Pix2Pix
• synthetic aperture radar