Machine Learning-based Identification of Contaminated Images in Light Curve Data Preprocessing
文献类型：期刊论文
| 作者 | 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
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| 刊名 | RESEARCH IN ASTRONOMY AND ASTROPHYSICS
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| 出版日期 | 2024-04-01 |
| 卷号 | 24期号:4 |
| 关键词 | techniques: image processing methods: data analysis light pollution |
| ISSN号 | 1674-4527 |
| DOI | 10.1088/1674-4527/ad339e |
| 产权排序 | 第1完成单位 |
| 文献子类 | Article |
| 英文摘要 | 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. |
| 学科主题 | 天文学 ; 天文学其他学科 ; 计算机科学技术 ; 人工智能 |
| URL标识 | 查看原文 |
| 出版地 | 20A DATUN RD, CHAOYANG, BEIJING, 100101, PEOPLES R CHINA |
| 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] |
| WOS研究方向 | Astronomy & Astrophysics |
| 语种 | 英语 |
| WOS记录号 | WOS:001207482400001 |
| 出版者 | NATL ASTRONOMICAL OBSERVATORIES, CHIN ACAD SCIENCES |
| 资助机构 | 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] |
| 版本 | 出版稿 |
| 源URL | [http://ir.ynao.ac.cn/handle/114a53/27126]  |
| 专题 | 云南天文台_应用天文研究组 |
| 作者单位 | 1.Yunnan Observatories, Chinese Academy of Sciences, Kunming 650216, China; lirw@ynao.ac.cn; 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 |
| 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). |
入库方式： OAI收割
来源：云南天文台
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