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Solar Physics

, 293:15 | Cite as

Automated Segmentation of High-Resolution Photospheric Images of Active Regions

  • Meng Yang
  • Yu Tian
  • Changhui Rao
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).

Keywords

Sunspots, umbra Granulation Instrumentation and data management 

Notes

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.

Disclosure of Potential Conflicts of Interest

The authors declare that they have no conflicts of interest.

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Copyright information

© Springer Science+Business Media B.V., part of Springer Nature 2018

Authors and Affiliations

  1. 1.Key Laboratory on Adaptive OpticsChinese Academy of SciencesChengduChina
  2. 2.Institute of Optics and ElectronicsChinese Academy of SciencesChengduChina
  3. 3.University of Chinese Academy of SciencesBeijingChina

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