BCNet: Background Conversion Network for SAR Data Generation

Collecting large-scene synthetic aperture radar (SAR) images with targets of interest (TOI) has been a challenging task. To embed TOI slices into measured large scenes can be a good solution. Current methods for SAR TOI slice generation are mainly based on a single data source. Poor background variability of generated images leads to difficulty in naturally embedding TOI slices into large scenes. This article presents a SAR target background conversion network (BCNet), which combines TOI slices with large-scene slices under the same operating condition. The background of TOI slices is converted to large-scene background while preserving the target scattering characteristics to enhance the target background diversity and variability. Background conversion is a special image style transfer, and BCNet uses CycleGAN as the baseline model. The problem that the baseline model may result in target missing is analyzed by Bayesian theory, and then, a new loss function Bysloss is designed to preserve the characteristics of target shadow and scattering center. A new image fusion module has been developed to generate training data for robust background conversion. In addition, the generated high-quality background conversion images are used for two-way recognition performance verification, large scene, and generated TOI slices fusion verification, respectively. The experimental results have shown that the generated data can be successfully used for SAR automatic target recognition in few-shot conditions, and also have strong potential in generating large-scene SAR images with TOI, SAR deception jamming, and augmenting the target detection dataset.