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

Hang Li

doi:10.1117/12.2535669

14 February 2020 Reference image generation for space target recognition on ground-based observation

Hang Li

Proceedings Volume 11429, MIPPR 2019: Automatic Target Recognition and Navigation; 1142902 (2020) https://doi.org/10.1117/12.2535669
Event: Eleventh International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2019), 2019, Wuhan, China

Abstract

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 images of 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.

Citation Download Citation

Hang Li "Reference image generation for space target recognition on ground-based observation", Proc. SPIE 11429, MIPPR 2019: Automatic Target Recognition and Navigation, 1142902 (14 February 2020); https://doi.org/10.1117/12.2535669

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KEYWORDS

Imaging systems

Cameras

Gallium nitride

3D modeling

Target recognition

Data modeling

Computer simulations

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