Machine Learning-based Identification of Contaminated Images in Light Curve Data Preprocessing

doi:10.1088/1674-4527/ad339e

YNAO OpenIR > 应用天文研究组

	Machine Learning-based Identification of Contaminated Images in Light Curve Data Preprocessing
	Li H(李徽)1,2 ; Li RW(李荣旺)1,3 ; Shu P(舒鹏)1 ; Li YQ(李语强)1,3
发表期刊	RESEARCH IN ASTRONOMY AND ASTROPHYSICS
	2024-04-01
卷号	24 期号:4
DOI	10.1088/1674-4527/ad339e
产权排序	第1完成单位
收录类别	SCI
关键词	techniques: image processing methods: data analysis light pollution
摘要	Attitude is one of the crucial parameters for space objects and plays a vital role in collision prediction and debris removal. Analyzing light curves to determine attitude is the most commonly used method. In photometric observations, outliers may exist in the obtained light curves due to various reasons. Therefore, preprocessing is required to remove these outliers to obtain high quality light curves. Through statistical analysis, the reasons leading to outliers can be categorized into two main types: first, the brightness of the object significantly increases due to the passage of a star nearby, referred to as stellar contamination, and second, the brightness markedly decreases due to cloudy cover, referred to as cloudy contamination. The traditional approach of manually inspecting images for contamination is time-consuming and labor-intensive. However, we propose the utilization of machine learning methods as a substitute. Convolutional Neural Networks and SVMs are employed to identify cases of stellar contamination and cloudy contamination, achieving F1 scores of 1.00 and 0.98 on a test set, respectively. We also explore other machine learning methods such as ResNet-18 and Light Gradient Boosting Machine, then conduct comparative analyses of the results.
资助项目	National Natural Science Foundation of China (NSFC)[12373086]; National Natural Science Foundation of China (NSFC)[12303082]; CAS Light of West China Program, Yunnan Revitalization Talent Support Program in Yunnan Province, National Key R&D Program of China[2022YFC2203800]
项目资助者	National Natural Science Foundation of China (NSFC)[12373086, 12303082] ; CAS Light of West China Program, Yunnan Revitalization Talent Support Program in Yunnan Province, National Key R&D Program of China[2022YFC2203800]
语种	英语
学科领域	天文学 ; 天文学其他学科 ; 计算机科学技术 ; 人工智能
文章类型	Article
出版者	NATL ASTRONOMICAL OBSERVATORIES, CHIN ACAD SCIENCES
出版地	20A DATUN RD, CHAOYANG, BEIJING, 100101, PEOPLES R CHINA
ISSN	1674-4527
URL	查看原文
WOS记录号	WOS:001207482400001
WOS研究方向	Astronomy & Astrophysics
WOS类目	Astronomy & Astrophysics
引用统计
文献类型	期刊论文
版本	出版稿
条目标识符	http://ir.ynao.ac.cn/handle/114a53/27126
专题	应用天文研究组
作者单位	1.Yunnan Observatories, Chinese Academy of Sciences, Kunming 650216, China; [email protected]; 2.University of Chinese Academy of Sciences, Beijing 100049, China; 3.Key Laboratory of Space Object and Debris Observation, Chinese Academy of Sciences, Nanjing 210023, China
第一作者单位	中国科学院云南天文台
推荐引用方式 GB/T 7714	Li H,Li RW,Shu P,et al. Machine Learning-based Identification of Contaminated Images in Light Curve Data Preprocessing[J]. RESEARCH IN ASTRONOMY AND ASTROPHYSICS,2024,24(4).
APA	李徽,李荣旺,舒鹏,&李语强.(2024).Machine Learning-based Identification of Contaminated Images in Light Curve Data Preprocessing.RESEARCH IN ASTRONOMY AND ASTROPHYSICS,24(4).
MLA	李徽,et al."Machine Learning-based Identification of Contaminated Images in Light Curve Data Preprocessing".RESEARCH IN ASTRONOMY AND ASTROPHYSICS 24.4(2024).

条目包含的文件
文件名称/大小	文献类型	版本类型	开放类型	使用许可
Machine Learning-bas（1824KB）	期刊论文	出版稿	开放获取	CC BY-NC-SA	浏览请求全文