{"title":"神经网络在错误太阳图像识别中的应用","authors":"Kiran Jain, Mitchell Creelman","doi":"10.1007/s11207-025-02554-5","DOIUrl":null,"url":null,"abstract":"<div><p>For over a century, solar images have been captured across different spectral ranges. Initially, these images were taken on photographic plates, and with the development of CCD cameras, the images transitioned from analogue to digital formats. Analyzing digital images enables us to identify and analyze trends and features on the solar disk more efficiently. However, complications due to instrument malfunction or environmental factors can result in suboptimal images. Traditionally, several statistical parameters are used to check image quality, but these measures do not always yield satisfactory results. In this article, we describe a convolutional classification neural network for near-real time image quality assessment of GONG Dopplergrams. We also present a case study where this approach significantly improved the quality of science data products in an automated data reduction pipeline without any human intervention.</p></div>","PeriodicalId":777,"journal":{"name":"Solar Physics","volume":"300 9","pages":""},"PeriodicalIF":2.4000,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Application of a Neural Network for Identifying Erroneous Solar Images\",\"authors\":\"Kiran Jain, Mitchell Creelman\",\"doi\":\"10.1007/s11207-025-02554-5\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>For over a century, solar images have been captured across different spectral ranges. Initially, these images were taken on photographic plates, and with the development of CCD cameras, the images transitioned from analogue to digital formats. Analyzing digital images enables us to identify and analyze trends and features on the solar disk more efficiently. However, complications due to instrument malfunction or environmental factors can result in suboptimal images. Traditionally, several statistical parameters are used to check image quality, but these measures do not always yield satisfactory results. In this article, we describe a convolutional classification neural network for near-real time image quality assessment of GONG Dopplergrams. We also present a case study where this approach significantly improved the quality of science data products in an automated data reduction pipeline without any human intervention.</p></div>\",\"PeriodicalId\":777,\"journal\":{\"name\":\"Solar Physics\",\"volume\":\"300 9\",\"pages\":\"\"},\"PeriodicalIF\":2.4000,\"publicationDate\":\"2025-09-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Solar Physics\",\"FirstCategoryId\":\"101\",\"ListUrlMain\":\"https://link.springer.com/article/10.1007/s11207-025-02554-5\",\"RegionNum\":3,\"RegionCategory\":\"物理与天体物理\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ASTRONOMY & ASTROPHYSICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Solar Physics","FirstCategoryId":"101","ListUrlMain":"https://link.springer.com/article/10.1007/s11207-025-02554-5","RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ASTRONOMY & ASTROPHYSICS","Score":null,"Total":0}
Application of a Neural Network for Identifying Erroneous Solar Images
For over a century, solar images have been captured across different spectral ranges. Initially, these images were taken on photographic plates, and with the development of CCD cameras, the images transitioned from analogue to digital formats. Analyzing digital images enables us to identify and analyze trends and features on the solar disk more efficiently. However, complications due to instrument malfunction or environmental factors can result in suboptimal images. Traditionally, several statistical parameters are used to check image quality, but these measures do not always yield satisfactory results. In this article, we describe a convolutional classification neural network for near-real time image quality assessment of GONG Dopplergrams. We also present a case study where this approach significantly improved the quality of science data products in an automated data reduction pipeline without any human intervention.
期刊介绍:
Solar Physics was founded in 1967 and is the principal journal for the publication of the results of fundamental research on the Sun. The journal treats all aspects of solar physics, ranging from the internal structure of the Sun and its evolution to the outer corona and solar wind in interplanetary space. Papers on solar-terrestrial physics and on stellar research are also published when their results have a direct bearing on our understanding of the Sun.