An attention supervision transformer full-resolution residual network for space satellite image segmentation

The growing number of satellites in orbit has resulted in a rise in defunct satellites and space debris, posing a significant risk to valuable spacecraft like normal satellites and space stations. Therefore, the removal of defunct satellites and space debris has become increasingly crucial. This article presents a segmentation method for satellite images captured in the visible light spectrum in space. Firstly, due to the lack of real space satellite images, we used optical simulation and Unsupervised Generative Attentional Networks with Adaptive Layer-Instance Normalization for Image-To-Image Translation (U-GAT-IT) to generate realistic space satellite images in the visible light spectrum and constructed a dataset. Secondly, we proposed an Attention Supervision Transformer Full-Resolution Residual Network (ASTransFRRN), which integrates transformer, attention mechanism and deep supervision, to segment satellite bodies, solar panels, and the cosmic background. Finally, we evaluated the proposed method using the U-GAT-IT simulated dataset and compared its performance with state-of-The-Art methods. The proposed method achieved a segmentation accuracy of 90.77%±7.04% for satellite bodies, 90.61%±16.48% for satellite solar panels, and 97.66%±1.94% for the cosmic background. The overall pixel segmentation accuracy was 97.22%±2.78%, outperforming the compared methods in terms of segmentation accuracy. The proposed ASTransFRRN demonstrated a significant improvement in the segmentation accuracy of the main components of space satellites.