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Deep Learning in Solar Astronomy / by Long Xu, Yihua Yan, Xin Huang
(SpringerBriefs in Computer Science. ISSN:21915776)

データ種別	電子ブック
版	1st ed. 2022.
出版者	(Singapore : Springer Nature Singapore : Imprint: Springer)
出版年	2022
大きさ	XIV, 92 p. 1 illus : online resource
著者標目	*Xu, Long author Yan, Yihua author Huang, Xin author SpringerLink (Online service)

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URL	巻　次	配架場所	請求記号	登録番号	コメント	刷　年	状　態	利用注記	ISBN	予約	請求メモ
URL		射水-電子	007	EB0004742	Computer Scinece R0 2005-6,2022-3				9789811927461

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一般注記	Chapter 1: Introduction -- Chapter 2: Classical deep learning models -- Chapter 3: Deep learning in solar image classification tasks -- Chapter 4: Deep learning in solar object detection tasks -- Chapter 5: Deep learning in solar image generation tasks -- Chapter 6: Deep learning in solar forecasting tasks The volume of data being collected in solar astronomy has exponentially increased over the past decade and we will be entering the age of petabyte solar data. Deep learning has been an invaluable tool exploited to efficiently extract key information from the massive solar observation data, to solve the tasks of data archiving/classification, object detection and recognition. Astronomical study starts with imaging from recorded raw data, followed by image processing, such as image reconstruction, inpainting and generation, to enhance imaging quality. We study deep learning for solar image processing. First, image deconvolution is investigated for synthesis aperture imaging. Second, image inpainting is explored to repair over-saturated solar image due to light intensity beyond threshold of optical lens. Third, image translation among UV/EUV observation of the chromosphere/corona, Ha observation of the chromosphere and magnetogram of the photosphere is realized by using GAN, exhibiting powerful image domain transfer ability among multiple wavebands and different observation devices. It can compensate the lack of observation time or waveband. In addition, time series model, e.g., LSTM, is exploited to forecast solar burst and solar activity indices. This book presents a comprehensive overview of the deep learning applications in solar astronomy. It is suitable for the students and young researchers who are major in astronomy and computer science, especially interdisciplinary research of them HTTP:URL=https://doi.org/10.1007/978-981-19-2746-1
件　名	LCSH:Astronomy LCSH:Astronomy -- Observations 全ての件名で検索 LCSH:Machine learning LCSH:Image processing -- Digital techniques 全ての件名で検索 LCSH:Computer vision FREE:Astronomy, Cosmology and Space Sciences FREE:Astronomy, Observations and Techniques FREE:Machine Learning FREE:Computer Imaging, Vision, Pattern Recognition and Graphics FREE:Computer Vision
分　類	LCC:QB1-991 DC23:520 DC23:500.5
書誌ID	EB00004130
ISBN	9789811927461