Dataset Augmentation Learning Method for Earth Background and Space Target Textures

A novel data augmentation approach is introduced to mitigate the challenge of insufficient realism in supervised training datasets for aerospace applications, where simulated Earth backgrounds and target textures often diverge from actual space scenarios. Initially, virtual space background images are synthesized using a modified WGAN -GP framework, which captures latent Earth background features from original imagery and generates synthetically varied textures to expand the diversity of terrestrial patterns. Concurrently, a stochastic style transfer network is developed to dynamically alter surface texture characteristics of dataset targets. This network maintains spatial target integrity and high - level semantic consistency while introducing texture variability through adversarial style recombination. Finally, synthesized Earth backgrounds and stylized target surfaces are fused under illumination consistency constraints, ensuring photorealistic integration and yielding a comprehensive augmented dataset. Simulation experiments on pose estimation networks indicate that the augmented dataset constructed with this method significantly improves pose estimation accuracy compared to the original training set.