An Effective Procedure to Build Space Object Datasets Based on STK

Abstract

With the development of space technology, deep learning methods, with their excellent generalization ability, are increasingly applied in various space activities. The space object data is difficult to obtain, which greatly limits its application in space activities. The images of the existing public spacecraft dataset are mostly rendered, which not only lack physical meaning but also have limited data. In this paper, we propose an effective construction procedure to build a space object dataset based on STK, which can help to break the limitations of deep learning methods in space activities. Firstly, based on STK, we conduct orbit simulation for 24 space targets and establish the simulation dataset; secondly, we use 600 images of 6 typical targets and label them to build a real-shot validation dataset. Finally, the constructed space object dataset based on STK is verified to be effective through six semantic segmentation networks, which can be used to train the real spacecraft’s semantic segmentation. Lots of experiments show that the accuracy of migrating the training results of the simulation dataset to the real shooting dataset is slightly reduced, but the mPA is still greater than 85%. In particular, after adding orbital physics simulation data, the accuracy of six semantic segmentation methods is generally improved. Therefore, the STK-based physical simulation of orbit is an effective method for space object dataset construction.