With the development of artificial intelligence， target recognition and pose estimation based on computer vision have received widespread attention. At present， computer-vision-based pose estimation technology for cooperative targets is being widely used in space missions， such as in rendezvous and docking. However， for noncooperative targets， complex environments， such as stray-light backgrounds， surface-coating reflections， and dramatic light changes， cause difficulties in feature extraction and pose estimation. In this paper， the methods and applications of visual-based pose estimation in space missions are summarized. Various target recognition and pose estimation algorithms， based on deep-learning algorithms， are systematically outlined. Moreover， current deep-learning algorithms in the context of space missions are discussed. Finally， the task demand of space tasks is analyzed to present some future development trends.