Simultaneous space object recognition and pose estimation by convolutional neural network

The increasing population of orbital debris is considered as a growing threat to space missions. For this purpose, Convolutional Neural Network was implemented based on transfer learning and data augmentation in order to conduct satellite classification and pose regression. In addition, the effects of un-centered and noisy images as well as different illumination conditions were analyzed by implementing different pre-trained networks. Based on the results, the present method could identify satellites and evaluate their poses against different space conditions effectively.