Reference image generation for space target recognition on ground-based observation

Space object observation by using small ground-based telescopes has become a common supplementary means for usual space object observation because of its flexible operation and convenient carrying. However, due to atmospheric turbulence, urban lighting and other interference factors, the low resolution, blurred contour and scarce texture of the small ground-based telescope space target observation image seriously affect the space target recognition. At the same time, there are few real-time data for space target observation, thus the conventional deep learning method which needs lots of offline trainning data cannot be applicated. Therefore, it is necessary to prepare typical space target reference image by means of three-dimensional model and simulation image. Generative adversarial network provides a simulation way to prepare training data offline. In the preparation of generation images of space target recognition for ground-based observation, the 3D model data of space target are projected in two dimensions by setting elevation angle, roll angle and spin angle. And the two-dimensional generation imagesof space target are obtained. Then, GAN is trained by using real-time observation data of some existing space targets and corresponding two-dimensional generation images as input, and simulation generation images of typical space targets are obtained by using two-dimensional datum map on trained GAN. Experiments show that the reference images simulated by GAN are more authentic than those generated by traditional methods, and have higher correct rate, accuracy and efficiency on recognition mission.