Design of a Simulation Dataset for Fine-grained Detection of Space Target Sequence Images

Authors

• Shiyan Wang, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China; Key Laboratory of Technology in Geo-spatial Information Processing and Application System, Chinese Academy of Sciences, Beijing 100190, China; Key Laboratory of Target Cognition and Application Technology (TCAT), Chinese Academy of Sciences, Beijing 100190, China; School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China
• Danpu Zhao, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China; Key Laboratory of Technology in Geo-spatial Information Processing and Application System, Chinese Academy of Sciences, Beijing 100190, China; Key Laboratory of Target Cognition and Application Technology (TCAT), Chinese Academy of Sciences, Beijing 100190, China; School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China
• Haikun Hong, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China; Key Laboratory of Technology in Geo-spatial Information Processing and Application System, Chinese Academy of Sciences, Beijing 100190, China; Key Laboratory of Target Cognition and Application Technology (TCAT), Chinese Academy of Sciences, Beijing 100190, China; School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China

Abstract

Abstract:

With the increasing number of space objects such as satellites, there is a growing need for fine-grained detection and recognition of distant space targets. However, obtaining real observational data remains difficult due to high costs and insufficient annotation detail. To overcome these limitations, asynthetic dataset generation system based on multi-module collaboration is developed. Using joint simulation with STK and Blender, the proposed system simulates optical imaging conditions of space targets and automatically produces sequential images along with corresponding fine-grained labels. A large-scale simulation dataset is constructed for fine-grained detection in space target image sequences. It contains images of 12 categories of space targets and includes component-level labels for 3 types of key parts. Experiments are carried out on space target classification and fine-grained component detection, providing baseline results for relevant research to use. This dataset supports model training and algorithm verification in ground-based applications. It promotes the development of space target detection technology and lays a foundation for autonomous space monitoring and intelligent decision-making.

Recommended Citation

Wang, Shiyan; Zhao, Danpu; and Hong, Haikun (2026) "Design of a Simulation Dataset for Fine-grained Detection of Space Target Sequence Images," Journal of System Simulation: Vol. 38: Iss. 9, Article 19.
DOI: 10.16182/j.issn1004731x.joss.25-1073
Available at: https://dc-china-simulation.researchcommons.org/journal/vol38/iss9/19

First Page

2711

Last Page

2724

CLC

TP391.4

Recommended Citation

Wang Shiyan, Zhao Danpu, Hong Haikun. Design of a Simulation Dataset for Fine-grained Detection of Space Target Sequence Images[J]. Journal of System Simulation, 2026, 38(9): 2711-2724.

Corresponding Author

Zhao Danpu

DOI

10.16182/j.issn1004731x.joss.25-1073