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020 _a9789811927461
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024 7 _a10.1007/978-981-19-2746-1
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072 7 _aSCI004000
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082 0 4 _a520
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082 0 4 _a500.5
_223
100 1 _aXu, Long.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
245 1 0 _aDeep Learning in Solar Astronomy
_h[electronic resource] /
_cby Long Xu, Yihua Yan, Xin Huang.
250 _a1st ed. 2022.
264 1 _aSingapore :
_bSpringer Nature Singapore :
_bImprint: Springer,
_c2022.
300 _aXIV, 92 p. 1 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSpringerBriefs in Computer Science,
_x2191-5776
505 0 _aChapter 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.
520 _aThe 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.
650 0 _aAstronomy.
650 0 _aAstronomy
_xObservations.
650 0 _aMachine learning.
650 0 _aImage processing
_xDigital techniques.
650 0 _aComputer vision.
650 1 4 _aAstronomy, Cosmology and Space Sciences.
650 2 4 _aAstronomy, Observations and Techniques.
650 2 4 _aMachine Learning.
650 2 4 _aComputer Imaging, Vision, Pattern Recognition and Graphics.
650 2 4 _aComputer Vision.
700 1 _aYan, Yihua.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
700 1 _aHuang, Xin.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9789811927454
776 0 8 _iPrinted edition:
_z9789811927478
830 0 _aSpringerBriefs in Computer Science,
_x2191-5776
856 4 0 _uhttps://doi.org/10.1007/978-981-19-2746-1
912 _aZDB-2-SCS
912 _aZDB-2-SXCS
942 _cSPRINGER
999 _c185487
_d185487