PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.
The identification of launch event based on infrared image processing is an important machine learning technology for space-based early warning system. Due to the long-range detection and short duration of the powered phase of trajectory, launch event represents as a point target in the infrared image. Therefore, the main challenge is to address the issue of distinguishing the different types of infrared point target. To tackle this problem, we propose a novel method for recognizing images of rocket exhaust flame point targets at different temperatures. To validate our approach, we conducted experimental validation using simulated detecting images obtained from high-orbit infrared early warning satellites. These images are generated based on the EO/IR module in STK software. The experimental results verify the effectiveness of our proposed method in solving the challenge of infrared point target recognition.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Yan Ouyang,BinBin Shi,XiaoBin Huang,Li Lu, andYuan Jiang
"Research on infrared point target recognition method based on space-based early warning system", Proc. SPIE 12969, International Conference on Algorithm, Imaging Processing, and Machine Vision (AIPMV 2023), 129692U (9 January 2024); https://doi.org/10.1117/12.3014467
ACCESS THE FULL ARTICLE
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.
The alert did not successfully save. Please try again later.
Yan Ouyang, BinBin Shi, XiaoBin Huang, Li Lu, Yuan Jiang, "Research on infrared point target recognition method based on space-based early warning system," Proc. SPIE 12969, International Conference on Algorithm, Imaging Processing, and Machine Vision (AIPMV 2023), 129692U (9 January 2024); https://doi.org/10.1117/12.3014467