LAUSR

As the number of space targets increases year by year, space situational awareness plays an important role in satellite security. To keep satellite secure, we use space target monitoring, removal, and avoidance to complete space situational awareness. These tasks usually use space target classification algorithms, and the algorithms should be compared fairly with a space target dataset. But at present, real data collection is difficult, and the existing simulation datasets lack some space models. These incomplete space target datasets limit the sustainable development of this field. Therefore, this article proposes a space target dataset to make up for the shortcomings of the existing space target dataset. 1) More realistic. Our dataset considers the real distribution and operation of space targets and counts their real number and orbital position. We simulate the real space target environment to ensure that the dataset can be migrated to practical applications. 2) More diverse. Our dataset includes 11 classes of satellites and 35 classes of space debris. In addition, we use a large number of lighting angles and shooting orbits for each model. 3) More reliable. In order to reduce artificial data bias, we count the attributes of the dataset to analyze the consistent distribution of attributes such as the resolution and contrast of space target images in different classes. Then, we made some targeted modifications. In addition, we propose a feature-based quantitative evaluation method for reliability, which calculates the distribution among different classes in the space target dataset.