Top

Published in:

2023 | OriginalPaper | Chapter

Research for Non-cooperative Space Objects Detection Methods Based on Image

Authors : Yunfan Lei, Hongjun Zhong, Long Wang, Yanpeng Wu

Published in: Signal and Information Processing, Networking and Computers

Publisher: Springer Nature Singapore

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

This paper takes a research on non-cooperative space objects detection methods based on the image. In order to design a new method suited for optical observation equipment with super-large field of view and high sensitivity, paper investigates existing methods and summaries the development trend from the appearing of object detection till now. According to the research, the main driving force for space objects detection methods is the upgrading of hardware used in observation equipment. Nowadays, methods in this field have gradually gain the unity of high sensitivity, high real-time performance and high accuracy. In the future, with the application of novel algorithms, new methods will achieve fast and accurate detection of darker or even obscured objects.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

previous chapter Autonomous Mission Planning Software Design and Testing of the Agile Earth Observation Satellite

next chapter Research on a BSDiff-Based System-Level On-Orbit Maintenance Technology of Spacecraft

Ming, L., Zizheng, G., Guoqing, L.: Monitor and remove cutting-edge technology and system development of space debris. Chin. Sci. Bull. 63(25), 2570–2591 (2018)

Yueyan, W.: Development status research of foreign space target detection and identification systems. Spacecr. Eng. 27(03), 86–94 (2018). (In Chinese)

Schildknecht, T., Hugentobler, T., Verdun, A.: Algorithms for ground based optical detection of space debris. Adv. Space Res. 16(11), 47–50 (1995)

Nakajima, A., et al.: Space Debris Observation by Ground-Based Optical Telescopes. Japan Society for Aeronautical and Space Sciences and ISTS (2020)

Yanagisawa, T., Kurosaki, H., Nakajima, A.: Present status of space debris optical observational facility of JAXA at Mt. Nyukasa. In: 5th European Conference on Space Debris’. ESA SP-672 (2009)

XiuJuan, S., BingLiang, H., Peng, Y.: Research on target recognition algorithm for microspacecraft. Mod. Electron. Technol. 33(04), 163–165 (2010). (In Chinese)

Zongling, L., Luyuan, W., Jiyang, Y., Shuai, J., Yuhang, W.: On-orbit real time monitoring and processing method for space debris target. Spacecr. Eng. 28(06), 58–64 (2019). (In Chinese)

Min, W., Jinyu, Z., Tao, C., Bochuan, C.: Moving point object detection from faint space based on temporal-spatial domain. J. Electron. Inf. Technol. 39(07), 1578–1584 (2017). (In Chinese)

Min, W., Jinyu, Z., Tao, C., Bochuan, C.: Moving target detection in star map based on distance matrix. Opt. Precis. Eng. 25(07), 1954–1960 (2017). (In Chinese)

10.

Song, H., Zhiyuan, H, Lei, D., Zhuanghao, L., Jieying, F., Wei, Z.: Research on canny operator in image optimization processing. Comput. Technol. Dev. 30(10), 106–110+209(2020). (In Chinese)

11.

CSDN. https://blog.csdn.net/yu132563/article/details/99228303. Accessed 11 Aug 2019. (In Chinese)

12.

CSDN. https://blog.csdn.net/fengyhack/article/details/41873119. Accessed 11 Dec 2014. (In Chinese)

13.

CSDN. https://blog.csdn.net/qq_15971883/article/details/80583364. 14 June 2018. (In Chinese)

14.

David, B., Lian Lim, P.: Satellite detection in ACS/HST images. In: Marco, M., Keith, S., Fabio, P. (eds.) ASP Conference Series, vol. 521. Astronomical Society of the Pacific (2019)

15.

Alessandro, V., Klaus, S., Thomas, S.: Streak detection algorithm for space debris detection on optical images. In: 2015 AMOS Technical Conference (2015)

16.

Hickson, P.: A fast algorithm for the detection of faint orbital debris tracks in optical images [J]. Adv. Space Res. 62(11), 3078-3085 2018

17.

Luping, Z.: The Target Detection and Tracking under Star Background. National University of Defense Technology (2010). (In Chinese)

18.

Wusheng, T.: Research in identification algorithm of weak and small target in Space based on the principle of star tracker. National University of Defense Technology (2015). (In Chinese)

19.

CSDN. https://blog.csdn.net/dan_teng/article/details/87192430. Accessed 13 Feb 2019

20.

Duev Dmitry, A., Mahabal ,A., Ye, Q., Tirumala, K., Belicki, J., Dekany R, Frederick, S., Graham Matthew, J., Laher Russ, R., Masci Frank, J., Prince Thomas, A., Riddle, R., Rosnet, P., Soumagnac Maayane, T.: Deep streaks: identifying fast-moving objects in the Zwicky Transient Facility data with deep learning. Mon. Not. R. Astron. Soc. 486(3), 4158–4165 (2019)

21.

Nguyen, V.N., Jenssen, R., Roverso, D.: LS-Net: fast single-shot line-segment detector. Mach. Vis. Appl. 32(1), 12 (2019)

22.

Muneeb, A., Sibt Ul, H.: Line Detection using Convolutional Neural Networks. (Class project) (2016)

23.

Thshifumi, Y., Hirohisa, K., Hajime, B., Yukihiti, K., Masahiko, U., Toshiya, H.: Comparison between four detection algorithms For GEO objects Toshifumi Yanagisawa. In: Advanced Maui Optical and Space Surveillance Technologies Conference (2012)

24.

Sijie, K.: Dim Target Extraction Technique in Dense Stellar Background. University of Chinese Academy of Sciences (2019). (In Chinese)

25.

Yuanzhao, Y., Luwei, Y., Xiaonan, M., Xiaojun, Y., Xunjiang, Z.: Algorithm of space target quick acquisition in the complex background of the sky. Acta Photonica Sinca 49(07), 68–77 (2020). (In Chinese)

26.

Wu, X., Wenquan, T., Xing, G., Cong, W.: Moveing target detection based on hybrid Gaussian model and five frame difference. J. Chin. Comput. Syst. 42(04), 785–790 (2021). (In Chinese)

Title: Research for Non-cooperative Space Objects Detection Methods Based on Image
Authors: Yunfan Lei
Hongjun Zhong
Long Wang
Yanpeng Wu
Publisher: Springer Nature Singapore
Book: Signal and Information Processing, Networking and Computers
Print ISBN: 978-981-19-3386-8

Electronic ISBN: 978-981-19-3387-5

Copyright Year: 2023
DOI: https://doi.org/10.1007/978-981-19-3387-5_32