Astrometric Calibration for All-sky Camera with Machine Learning
The night images obtained with an all-sky camera can provide spatial and time sampling, which can be used for measurement cloud coverage measurement and meteor monitoring. The astrometric calibration of an all-sky camera is necessary because of strong field distortions. We use machine learning to co...
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| Veröffentlicht in: | Publications of the Astronomical Society of the Pacific 2022-03, Vol.134 (1033), p.35002 |
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| creator | Tian, J. F. Ge, L. Wu, Y. Zhou, Z. Z. |
| description | The night images obtained with an all-sky camera can provide spatial and time sampling, which can be used for measurement cloud coverage measurement and meteor monitoring. The astrometric calibration of an all-sky camera is necessary because of strong field distortions. We use machine learning to complete the calibration of an all-sky camera. In order to prepare the data sets needed for machine learning, a particle swarm optimization algorithm is used to determine the parameters of the method proposed by Borovicka in 1995. Machine learning can transform plate coordinates to celestial coordinates and transform celestial coordinates to plate coordinates. The actual test shows that the standard deviation of residuals is of the order of 1′ for the transformation from plate coordinates to celestial coordinates and the standard deviation of residuals is of the order of 0.3 px for the transformation from celestial coordinates to the plate coordinates. |
| doi_str_mv | 10.1088/1538-3873/ac5316 |
| format | Article |
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The actual test shows that the standard deviation of residuals is of the order of 1′ for the transformation from plate coordinates to celestial coordinates and the standard deviation of residuals is of the order of 0.3 px for the transformation from celestial coordinates to the plate coordinates.</description><subject>All-sky cameras</subject><subject>Calibration</subject><subject>Cameras</subject><subject>Cloud cover</subject><subject>Coordinates</subject><subject>Machine learning</subject><subject>Neural networks</subject><subject>Optimization</subject><subject>Standard deviation</subject><issn>0004-6280</issn><issn>1538-3873</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><recordid>eNp1kDFPwzAQhS0EEqWwM0ZiJfTsS2x3o6ooIBWxwGy5jk1d0iTYqVD_Pa6CYOp00t17754-Qq4p3FGQckJLlDlKgRNtSqT8hIz-VqdkBABFzpmEc3IR4waAUklhRO5nsQ_t1vbBm2yua78Kuvdtk7k2ZLO6zuPnPu23Nujs2_fr7EWbtW9strQ6NL75uCRnTtfRXv3OMXlfPLzNn_Ll6-PzfLbMTWrQ58zwijO-4hUKcIKVnDuGSIE6R1MtIaeUSyGrKThMV4t2WkknrKuYNlrimNwMuV1ov3Y29mrT7kKTXirGC2SipKJIKhhUJrQxButUF_xWh72ioA6c1AGKOkBRA6dkuR0svu3-MzsdO0WxSCZEBVgCMNVV7oj8aPoPKJ91LA</recordid><startdate>20220301</startdate><enddate>20220301</enddate><creator>Tian, J. 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| source | IOP Publishing Journals; Institute of Physics (IOP) Journals - HEAL-Link; Alma/SFX Local Collection |
| subjects | All-sky cameras Calibration Cameras Cloud cover Coordinates Machine learning Neural networks Optimization Standard deviation |
| title | Astrometric Calibration for All-sky Camera with Machine Learning |
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