Synthetic Data for Semantic Image Segmentation of Imagery of Unmanned Spacecraft
This addresses the problem of enabling autonomous spacecraft rendezvous for space agencies and researchers, though it is incremental as it adapts existing synthetic data techniques to a specific domain.
The paper tackled the lack of real images for semantic segmentation of spacecraft in space by proposing a method to generate synthetic labeled image data, achieving a benchmark Sørensen-Dice coefficient of 0.8723 on the synthetic dataset.
Images of spacecraft photographed from other spacecraft operating in outer space are difficult to come by, especially at a scale typically required for deep learning tasks. Semantic image segmentation, object detection and localization, and pose estimation are well researched areas with powerful results for many applications, and would be very useful in autonomous spacecraft operation and rendezvous. However, recent studies show that these strong results in broad and common domains may generalize poorly even to specific industrial applications on earth. To address this, we propose a method for generating synthetic image data that are labelled for semantic segmentation, generalizable to other tasks, and provide a prototype synthetic image dataset consisting of 2D monocular images of unmanned spacecraft, in order to enable further research in the area of autonomous spacecraft rendezvous. We also present a strong benchmark result (Sørensen-Dice coefficient 0.8723) on these synthetic data, suggesting that it is feasible to train well-performing image segmentation models for this task, especially if the target spacecraft and its configuration are known.