Deep-learning for position error detection of the secondary mirror in space optical remote sensing system

Yun Gu; Xiaofang Zhang; Bingdao Li; Wenxiu Zhao

doi:10.1117/12.2617931

15 February 2022 Deep-learning for position error detection of the secondary mirror in space optical remote sensing system

Yun Gu, Xiaofang Zhang, Bingdao Li, Wenxiu Zhao

Author Affiliations +

Proceedings Volume 12166, Seventh Asia Pacific Conference on Optics Manufacture and 2021 International Forum of Young Scientists on Advanced Optical Manufacturing (APCOM and YSAOM 2021); 1216677 (2022) https://doi.org/10.1117/12.2617931
Event: Seventh Asia Pacific Conference on Optics Manufacture and 2021 International Forum of Young Scientists on Advanced Optical Manufacturing (APCOM and YSAOM 2021), 2021, Hong Kong, Hong Kong

Abstract

When space optical remote sensing system works in orbit, it is easy to be affected by the external environment such as heat, gravity and platform jitter, which makes the position of components such as secondary mirror be misaligned, resulting in the degradation of image quality. The traditional position misalignment detection technology has the disadvantages of complex device, time-consuming calculation and low accuracy. A deep learning method using convolutional neural network (CNN) is proposed to predict the positional misalignment of the secondary mirror directly from the defocus point spread function (PSF). The simulation results show that the system can be restored to the original design state under a small dynamic range of position error simply and quickly, which is a great significance for space remote sensing system in-orbit alignment.

Citation Download Citation

Yun Gu, Xiaofang Zhang, Bingdao Li, and Wenxiu Zhao "Deep-learning for position error detection of the secondary mirror in space optical remote sensing system", Proc. SPIE 12166, Seventh Asia Pacific Conference on Optics Manufacture and 2021 International Forum of Young Scientists on Advanced Optical Manufacturing (APCOM and YSAOM 2021), 1216677 (15 February 2022); https://doi.org/10.1117/12.2617931

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