Volume 40 Issue 3
May  2020
Turn off MathJax
Article Contents
LI Ling, CUI Yanmei, LIU Siqing, LEI Lei. Automatic Detection of Sunspots and Extraction of Sunspot Characteristic Parameters[J]. Chinese Journal of Space Science, 2020, 40(3): 315-322. doi: 10.11728/cjss2020.03.315
Citation: LI Ling, CUI Yanmei, LIU Siqing, LEI Lei. Automatic Detection of Sunspots and Extraction of Sunspot Characteristic Parameters[J]. Chinese Journal of Space Science, 2020, 40(3): 315-322. doi: 10.11728/cjss2020.03.315

Automatic Detection of Sunspots and Extraction of Sunspot Characteristic Parameters

doi: 10.11728/cjss2020.03.315
  • Received Date: 2019-04-15
  • Rev Recd Date: 2019-11-22
  • Publish Date: 2020-05-15
  • 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 size of solar image data archives, the automated detection and verification of various features of interest are becoming increasingly important for the reliable forecast of solar activity and space weather. In order to use high time-cadence SDO/HMI data and extract the main sunspot features for forecasting solar activities, we constructed an automatic detecting sunspot procedure with a mathematical morphology tool and calculated sunspot group area and sunspot number. By comparing our results with those obtained from Solar Region Summary compiled by NOAA/SWPC, it is found that sunspot group area and sunspot number computed with our algorithm are in good agreement with the active region values compiled by SWPC, and the corresponding correlation coefficients of sunspot group area and sunspot number are 0.77 and 0.79, respectively. In this study, high time-cadence feature parameters obtained from HMI data can provide timely and accurate inputs for solar activity forecasting.

     

  • loading
  • [1]
    GIBSON E G, CHAPMAN R D. The quiet sun[J]. Phys. Today, 1974, 303(1):69-72
    [2]
    HARVEY K L, ZWAAN C. Properties and emergence of bipolar active regions[J]. Sol. Phys., 1993, 148(1):85-118
    [3]
    ZHARKOV S, ZHARKOVA V, IPSON S, et al. Technique for automated recognition of sunspots on Full-Disk solar images[J]. Eurasip. J. Adv. Signal Proc., 2005, 15:2573-2584
    [4]
    CURTO J J, BLANCA M, MARTINEZ E. Automatic sunspots detection on full-disk solar images using mathematical morphology[J]. Sol. Phys., 2008, 250(2). DOI: 10.1007/s11207-008-9224-6
    [5]
    DJAFER D, IRBAH A, MEFTAH M. Identification of sunspots on full-disk solar images using wavelet analysis[J]. Sol. Phys., 2012, 281(2):863-875
    [6]
    FOZZANI P, VANDAME B, BIJAOUI A, et al. A multiscale vision model applied to analyze EIT images of the solar corona[J]. Sol. Phys., 2001, 201(2):271-287
    [7]
    TURMON M, PAP J M, MUKHTAR S. Statistical pattern recognition for labeling solar active regions:application to SOHO/MDI imagery[J]. Astrophys. J., 2008, 568(1):396-407
    [8]
    COLAK T, QAHWAJI R. Automated McIntosh-based classification of sunspot groups using MDI images[J]. Sol. Phys., 2008, 248(2):277-296
    [9]
    JIAO Weixin. Science of Space Weather[M]. Beijing:China Meteorological Press, 2003(焦维新. 空间天气学[M]. 北京:气象出版社, 2003)
    [10]
    578.DOI:10. 1007/s11207-014-0529-3
    [11]
    SERRA J. Image Analysis and Mathematical Morphology[M]. New York:Academic Press, 1983
    [12]
    MAJED S F, ABDUL M A, ZHARKOVA V. Automated detection of sunspots and sunspot groups in full-disk solar images[J]. Technol. Dev. Netw., 2010:297-301.DOI: 10.1007/978-90-481-9151-2_52
    [13]
    HARALICK R M, STEMBERG S R, ZHUANG X. Image analysis using mathematical morphology[J]. IEEE Trans. Pattern Anal. Mach. Intel., 1987, 9:532-550
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Article Metrics

    Article Views(1033) PDF Downloads(103) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return