Paper
18 December 2023 Effective framework for space target detection through atmospheric turbulence
Yiming Chen, Jing Wang, Zhehan Song, Haoying Li, Ziran Zhang, Qi Li, Zhihai Xu, Huajun Feng, Yueting Chen
Author Affiliations +
Abstract
Atmospheric turbulence is a major challenge in long-range imaging of ground-based telescopes, especially in the surveillance of space targets, whose observation distance is usually more than 100 km. In this case, space targets are extremely small in images, occupying less than 0.12% of the total image area, and suffer from severe blur and distortion. Consequently, the accuracy of object detection by both conventional and deep-learning-based methods is significantly hampered. Therefore, this paper proposes an effective framework for detecting space target through atmospheric turbulence. The framework incorporates a shallow deblurring module, a transformer-based feature extractor, and a small region proposal network. The training data comprises simulated degraded images of space target images against celestial backgrounds, as well as a selection of images from the Dotav2 dataset. Testing results show that the proposed framework outperforms the general framework, achieving a mean Average Precision (mAP) improvement of over 20%.
(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Yiming Chen, Jing Wang, Zhehan Song, Haoying Li, Ziran Zhang, Qi Li, Zhihai Xu, Huajun Feng, and Yueting Chen "Effective framework for space target detection through atmospheric turbulence", Proc. SPIE 12962, AOPC 2023: Optical Spectroscopy and Imaging; and Atmospheric and Environmental Optics, 1296205 (18 December 2023); https://doi.org/10.1117/12.3005214
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KEYWORDS
Object detection

Atmospheric turbulence

Target detection

Distortion

Transformers

Deblurring

Satellites

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