An Automatic Detection for Solar Active Regions Based on Scale-Invariant Feature Transform and Clustering by Fast Search and Find of Density Peaks |
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| Institution: | 1. Shanghai Astronomical Observatory, Chinese Academy of Sciences, Shanghai 200030 China;2. University of Chinese Academy of Sciences, Beijing 100049, China;1. College of Mathematics and Physics Science, Hunan University of Arts and Science, Changde 415000, China;2. Center for Astrophysics, Guangzhou University, Guangzhou 510006, China;1. School of Physics and Electronic Science, Guizhou Normal University, Guiyang 550025 China;2. Guizhou Provincial Key Laboratory of Radio Astronomy and Data Processing, Guizhou Normal University, Guiyang 550001, China;1. Department of Astronomy, University of Science and Technology of China, Hefei 230026, China;2. Polar Research Institute of China, Shanghai 200136, China;3. National Astronomical Observatories, Chinese Academy of Sciences, Beijing 100101, China;4. Shanghai Astronomical Observatory, Chinese Academy of Sciences, Shanghai 200030, China;5. School of Physics and Electronic Information, Anhui Normal University, Wuhu 241002, China;1. Department of Information Engineering, Dezhou Mechanical and Electrical Engineering School, Dezhou 251200, China;2. Department of Information Engineering, Technological Vocational College of Dezhou, Dezhou 251200, China;1. School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093;2. Shanghai Astronomical Observatory, Chinese Academy of Sciences, Shanghai 200030;3. Key Laboratory of Radio Astronomy, Chinese Academy of Sciences, Nanjing 210023;4. Shanghai Key Laboratory of Space Navigation and Position Techniques, Shanghai 20003 |
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| Abstract: | The solar active regions are the regions where various active phenomena occur in the solar atmosphere. Accurate detection and identification of the solar active regions are of great scientific significance to understand the formation mechanism of the solar magnetic field. In this paper, we propose an automatic detection and recognition technology for solar active regions based on the advantages of Scale Invariant Feature Transform (SIFT) and Clustering by Fast Search and Find of Density Peaks (DPC). Firstly, enhance the contrast of longitudinal magnetic image of Helioseismic and Magnetic Imager (HMI) of Solar Dynamics Observatory (SDO). Then extract the feature points by SIFT. Finally, cluster the feature points by fast search and find of density peaks so as to automatically detect and identify the solar active regions. The results show that the combination of SIFT and DPC can accurately detect the solar active region without human-computer interaction. |
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