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Automated Segmentation of High-Resolution Photospheric Images of Active Regions

Solar Physics volume 293, Article number: 15 (2018) Cite this article

Abstract

Due to the development of ground-based, large-aperture solar telescopes with adaptive optics (AO) resulting in increasing resolving ability, more accurate sunspot identifications and characterizations are required. In this article, we have developed a set of automated segmentation methods for high-resolution solar photospheric images. Firstly, a local-intensity-clustering level-set method is applied to roughly separate solar granulation and sunspots. Then reinitialization-free level-set evolution is adopted to adjust the boundaries of the photospheric patch; an adaptive intensity threshold is used to discriminate between umbra and penumbra; light bridges are selected according to their regional properties from candidates produced by morphological operations. The proposed method is applied to the solar high-resolution TiO 705.7-nm images taken by the 151-element AO system and Ground-Layer Adaptive Optics prototype system at the 1-m New Vacuum Solar Telescope of the Yunnan Observatory. Experimental results show that the method achieves satisfactory robustness and efficiency with low computational cost on high-resolution images. The method could also be applied to full-disk images, and the calculated sunspot areas correlate well with the data given by the National Oceanic and Atmospheric Administration (NOAA).

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References

  1. Berdyugina, S., Solanki, S., Frutiger, C.: 2003, The molecular Zeeman effect and diagnostics of solar and stellar magnetic fields – II. Synthetic Stokes profiles in the Zeeman regime. Astron. Astrophys. 412, 513. DOI . ADS .

    ADS  Article  Google Scholar 

  2. Berger, T.E., Berdyugina, S.V.: 2003, The observation of sunspot light-bridge structure and dynamics. Astrophys. J. 589, 2. DOI . ADS .

    Article  Google Scholar 

  3. Caselles, V., Kimmel, R., Sapiro, G.: 1997, Geodesic active contours. Int. J. Comput. Vis. 22, 61. DOI .

    Article  MATH  Google Scholar 

  4. Chan, T., Vese, L.: 2001, Active contours without edges. IEEE Trans. Image Process. 10, 266. DOI . ADS .

    ADS  Article  MATH  Google Scholar 

  5. Colak, T., Qahwaji, R.: 2008, Automated McIntosh-based classification of sunspot groups using MDI images. Solar Phys. 248, 277. DOI . ADS .

    ADS  Article  Google Scholar 

  6. Colak, T., Qahwaji, R.: 2013, Prediction of Extreme Ultraviolet Variability Experiment (EVE)/ Extreme Ultraviolet Spectro-Photometer (ESP) Irradiance from Solar Dynamics Observatory (SDO)/ Atmospheric Imaging Assembly (AIA) images using fuzzy image processing and machine learning. Solar Phys. 283, 143. DOI . ADS .

    ADS  Article  Google Scholar 

  7. Curto, J.J., Blanca, M., Martínez, E.: 2008, Automatic sunspots detection on full-disk solar images using mathematical morphology. Solar Phys. 250, 411. DOI . ADS .

    ADS  Article  Google Scholar 

  8. Falco, M., Borrero, J.M., Guglielmino, S.L., Romano, P., Zuccarello, F., Criscuoli, S., Cristaldi, A., Ermolli, I., Jafarzadeh, S., Voort, L.R.V.D.: 2016, Kinematics and magnetic properties of a light bridge in a decaying sunspot. Solar Phys. 291, 1939. DOI . ADS .

    ADS  Article  Google Scholar 

  9. Fonte, C.C., Fernandes, J.: 2009, Application of fuzzy sets to the determination of sunspot areas. Solar Phys. 260, 21. DOI . ADS .

    ADS  Article  Google Scholar 

  10. Goel, S., Mathew, S.K.: 2014, Automated detection, characterization, and tracking of sunspots from SOHO/MDI continuum images. Solar Phys. 289, 1413. DOI . ADS .

    ADS  Article  Google Scholar 

  11. Györi, L.: 1998, Automation of area measurement of sunspots. Solar Phys. 180, 109. DOI . ADS .

    ADS  Article  Google Scholar 

  12. Kong, L., Zhu, L., Bao, H., Guo, Y., Rao, X., Zhong, L., Rao, C.: 2016, Prototype of solar ground layer adaptive optics at the 1 m New Vacuum Solar Telescope. Chin. Opt. Lett. 14, 6. DOI . ADS .

    Google Scholar 

  13. Kong, L., Zhu, L., Zhang, L., Bao, H., Rao, C.: 2017, Real-time controller based on FPGA and DSP for solar ground layer adaptive optics prototype system at 1-m NVST. IEEE Photonics J. 9, 1. DOI .

    ADS  Google Scholar 

  14. Li, C., Kao, C.Y., Gore, J.C., Ding, Z.: 2008, Minimization of region-scalable fitting energy for image segmentation. IEEE Trans. Image Process. 17, 1940. DOI . ADS .

    ADS  MathSciNet  Article  MATH  Google Scholar 

  15. Li, C., Xu, C., Gui, C., Fox, M.D.: 2010, Distance regularized level set evolution and its application to image segmentation. IEEE Trans. Image Process. 19, 3243. DOI . ADS .

    ADS  MathSciNet  Article  MATH  Google Scholar 

  16. Li, C., Huang, R., Ding, Z., Gatenby, J.C., Metaxas, D.N., Gore, J.C.: 2011, A level set method for image segmentation in the presence of intensity inhomogeneities with application to MRI. IEEE Trans. Image Process. 20, 2007. DOI . ADS .

    ADS  MathSciNet  Article  MATH  Google Scholar 

  17. Liu, Z., Xu, J., Gu, B., Wang, S., You, J., Shen, L., Lu, R., Jin, Z., Chen, L., Lou, K., Li, Z., Liu, G., Xu, Z., Rao, C., Hu, Q., Li, R., Fu, H., Wang, F., Bao, M., Wu, M., Zhang, B.: 2014, New vacuum solar telescope and observations with high resolution. Res. Astron. Astrophys. 14, 705. DOI . ADS .

    ADS  Article  Google Scholar 

  18. Louis, R.E., Ravindra, B., Mathew, S.K., Rubio, L.R.B., Bayanna, A.R., Venkatakrishnan, P.: 2012, Analysis of a fragmenting sunspot using Hinode observations. Astrophys. J. 755, 516. DOI . ADS .

    Article  Google Scholar 

  19. Maltby, P., Avrett, E.H., Carlsson, M., Kjeldsethmoe, O., Kurucz, R.L., Loeser, R.: 1986, A new sunspot umbral model and its variation with the solar cycle. Astrophys. J. 306, 284. DOI . ADS .

    ADS  Article  Google Scholar 

  20. Otsu, N.: 1979, A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 9, 62. DOI .

    Article  Google Scholar 

  21. Pettauer, T., Brandt, P.N.: 1997, On novel methods to determine areas of sunspots from photoheliograms. Solar Phys. 175, 197. DOI . ADS .

    ADS  Article  Google Scholar 

  22. Rao, C., Zhu, L., Rao, X., Guan, C., Chen, D., Lin, J., Liu, Z.: 2010a, 37-element solar adaptive optics for 26-cm solar fine structure telescope at Yunnan Astronomical Observatory. Chin. Opt. Lett. 8, 966. DOI .

    Article  Google Scholar 

  23. Rao, C., Zhu, L., Rao, X., Guan, C., Chen, D., Chen, S., Lin, J., Liu, Z.: 2010b, Performance of the 37-element solar adaptive optics for 26-cm solar fine structure telescope at Yunnan Astronomical Observatory. Appl. Opt. 49, G129. DOI . ADS .

    Article  Google Scholar 

  24. Rao, C., Zhu, L., Rao, X., Zhang, L., Bao, H., Kong, L., Guo, Y., Ma, X., Li, M., Wang, C., Zhang, X., Fan, X., Chen, D., Feng, Z., Wang, X., Gu, N., Wang, Z.: 2015, Second generation solar adaptive optics for 1-m New Vacuum Solar Telescope at the Fuxian Solar Observatory. Chin. Opt. Lett. 13, 120101. DOI . ADS .

    Article  Google Scholar 

  25. Rao, C.H., Zhu, L., Rao, X.J., Zhang, L.Q., Bao, H., Ma, X.A., Gu, N.T., Guan, C.L., Chen, D.H., Wang, C.: 2016a, First generation solar adaptive optics system for 1-m New Vacuum Solar Telescope at Fuxian Solar Observatory. Res. Astron. Astrophys. 16, 19. DOI . ADS .

    ADS  Article  Google Scholar 

  26. Rao, C., Zhu, L., Rao, X., Zhang, L., Bao, H., Kong, L., Guo, Y., Zhong, L., Ma, X., Li, M.: 2016b, Instrument description and performance evaluation of a high-order adaptive optics system for the 1 m New Vacuum Solar Telescope at Fuxian Solar Observatory. Astrophys. J. 833, 210. DOI . ADS .

    ADS  Article  Google Scholar 

  27. Riethmueller, T.L., Solanki, S.K., Zakharov, V., Gandorfer, A.: 2008, Brightness, distribution, and evolution of sunspot umbral dots. Astron. Astrophys. 492, 233. DOI . ADS .

    ADS  Article  Google Scholar 

  28. Schleicher, H., Balthasar, H., Wöhl, H.: 2003, Velocity field of a complex sunspot with light bridges. Solar Phys. 215, 261. DOI . ADS .

    ADS  Article  Google Scholar 

  29. Sobotka, M.: 2003, Solar activity II: Sunspots and pores. Astron. Nachr. 324, 369. DOI . ADS .

    ADS  Article  Google Scholar 

  30. Song, D., Chae, J., Yurchyshyn, V., Lim, E.K., Cho, K.S., Yang, H., Cho, K., Kwak, H.: 2017, Chromospheric plasma ejections in a light bridge of a sunspot. Astrophys. J. 835, 2. DOI . ADS .

    Article  Google Scholar 

  31. Steinegger, M., Bonet, J.A., Vázquez, M.: 1997, Simulation of seeing influences on the photometric determination of sunspot areas. Solar Phys. 171, 303. DOI . ADS .

    ADS  Article  Google Scholar 

  32. Turmon, M., Pap, J.M., Mukhtar, S.: 2002, Statistical pattern recognition for labeling solar active regions: Application to SOHO/MDI imagery. Astrophys. J. 568, 396. DOI . ADS .

    ADS  Article  Google Scholar 

  33. Watson, F., Fletcher, L., Dalla, S., Marshall, S.: 2009, Modelling the longitudinal asymmetry in sunspot emergence: The role of the Wilson depression. Solar Phys. 260, 5. DOI . ADS .

    ADS  Article  Google Scholar 

  34. Zhang, K., Zhang, L., Song, H., Zhang, D.: 2013, Reinitialization-free level set evolution via reaction diffusion. IEEE Trans. Image Process. 22, 258. DOI . ADS .

    ADS  MathSciNet  Article  MATH  Google Scholar 

  35. Zhao, C., Lin, G.H., Deng, Y.Y., Yang, X.: 2016, Automatic recognition of sunspots in HSOS full-disk solar images. Publ. Astron. Soc. Aust. 33, 1. DOI . ADS .

    Article  Google Scholar 

  36. Zharkov, S., Zharkova, V., Ipson, S., Benkhalil, A.: 2005, Technique for automated recognition of sunspots on full-disk solar images. EURASIP J. Appl. Signal Process. 15, 2573. DOI . ADS .

    MATH  Google Scholar 

  37. Zharkova, V., Aboudarham, J., Zharkov, S., Ipson, S., Benkhalil, A., Fuller, N.: 2005, Solar feature catalogues in EGSO. Solar Phys. 228, 361. DOI . ADS .

    ADS  Article  Google Scholar 

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Acknowledgments

This work was supported by Natural National Science Foundation of China (No. 11727805 and No. 11703029) and the Laboratory Innovation Foundation of the Chinese Academy of Sciences (Grant No. YJ16K006). We are grateful to Lei Zhu, Xuejun Rao, Lanqiang Zhang, Hua Bao, Lin Kong, Youming Guo, Libo Zhong, Xue’an Ma, Mei Li, Cheng Wang, Xiaojun Zhang, Xinlong Fan, Donghong Chen, Zhongyi Feng, Naiting Gu, Yangyi Liu of the Institute of Optics and Electronics (IOE), Chinese Academy of Sciences for their help during the solar observations. The full-disk images used in the article were kindly provided by the NASA/SDO and the HMI science team. The sunspot-area data were obtained from USAF/NOAA Sunspot Data.

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Affiliations

  1. Key Laboratory on Adaptive Optics, Chinese Academy of Sciences, Chengdu, Sichuan, 610209, China
    • Meng Yang
    • , Yu Tian
    •  & Changhui Rao
  2. Institute of Optics and Electronics, Chinese Academy of Sciences, Chengdu, Sichuan, 610209, China
    • Meng Yang
    • , Yu Tian
    •  & Changhui Rao
  3. University of Chinese Academy of Sciences, Beijing, 100049, China
    • Meng Yang
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  1. Meng Yang
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Correspondence to Changhui Rao.

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Yang, M., Tian, Y. & Rao, C. Automated Segmentation of High-Resolution Photospheric Images of Active Regions. Sol Phys 293, 15 (2018). https://doi.org/10.1007/s11207-017-1236-7

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Keywords

  • Sunspots, umbra
  • Granulation
  • Instrumentation and data management