Automatic Detection of Sunspots and Extractionof Their Feature Parameters |
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| Affiliation: | 1. National Space Science Center, Chinese Academy of Sciences, Beijing 100190;2. University of Chinese Academy of Sciences, Beijing 100049;1. School of Engineering Science, University of Chinese Academy of Science, Beijing 100049;2. Key Lab of Optical Astronomy, National Astronomical Observatories, Chinese Academy of Sciences, Beijing 100101;3. Key Laboratory of Space Astronomy and Technology, National Astronomical Observatories, Chinese Academy of Sciences, Beijing 100101;4. School of Artificial Intelligence, Beijing Normal University, Beijing 100875;1. National Time Service Center, Chinese Academy of Sciences, Xi’an 710600;2. Key Laboratory of Time and Frequency Primary Standards, National Time Service Center, Chinese Academy of Sciences, Xi’an 710600;3. University of Chinese Academy of Sciences, Beijing 100049;4. School of Astronomy and Space Science, University of Chinese Academy of Sciences, Beijing 100049;1. Purple Mountain Observatory, Chinese Academy of Sciences, Nanjing 210023;2. School of Physics and Optoelectronic Engineering, Nanjing University of Information Science and Technology, Nanjing 210044;3. School of Astronomy and Space Science, University of Science and Technology of China, Hefei 230026;1. Key Laboratory foe Dark Matter and Space Astronomy, Purple Mountain Observatory, Chinese Academy of Sciences, Nanjing 210023;2. School of Astronomy and Space Science, University of Science and Technology of China, Hefei 230026;3. Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi’an 710119;4. Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033;1. College of Mathematics and Physics Science, Hunan University of Arts and Science, Changde 415000;2. Center for Astrophysics, Guangzhou University, Guangzhou 510006 |
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| Abstract: | Sunspots are solar features located in active regions of the Sun, whose number is an indicator of the Sun's magnetic activity. With a substantial increase in the quantity of solar image data, the automated detection and verification of various solar features have become increasingly important for the accurate and timely forecasts of solar activity and space weather. In order to use the high time-cadence SDO/HMI data to extract the main sunspot features for forecasting solar activities, we have established an automatic detection method of sunspots based on mathematical morphology, and calculated the sunspot group area and sunspot number. By comparing our results with those obtained from the Solar Region Summary compiled by NOAA/SWPC, it is found that the sunspot group areas and sunspot numbers computed with our algorithm are in good agreement with the active region values released by SWPC, and the corresponding correlation coefficients for the sunspot group area and sunspot number are 0.77 and 0.79, respectively. By using the method of this paper, the high time-cadence feature parameters can be obtained from the HMI data to provide the timely and accurate inputs for the solar activity forecast. |
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| Keywords: | Sunspot group area sunspot number automatic detection mathematical morphology |
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