Space target recognition based on deep learning

Automated recognition of spacecraft and space debris using imaging plays an important role in securing space safety and space exploration. Although deep learning is now the most successful solution for image-based object classification, it requires a myriad number of training data, which are not available for most real applications. In this paper, we investigate different single and hybrid data augmentation methods for both training and testing images, and thus propose a data augmentation-based deep learning approach to space target recognition. Experimental results on 400 synthetic space target images rendered by the Systems Tool Kit (STK) demonstrate that our proposed algorithm achieves higher accuracy than several traditional methods.