Abstract
In this article, an automated solar flare detection method applied to both full-disk and local high-resolution H\(\upalpha\) images is proposed. An adaptive gray threshold and an area threshold are used to segment the flare region. Features of each detected flare event are extracted, e.g. the start, peak, and end time, the importance class, and the brightness class. Experimental results have verified that the proposed method can obtain more stable and accurate segmentation results than previous works on full-disk images from Big Bear Solar Observatory (BBSO) and Kanzelhöhe Observatory for Solar and Environmental Research (KSO), and satisfying segmentation results on high-resolution images from the Goode Solar Telescope (GST). Moreover, the extracted flare features correlate well with the data given by KSO. The method may be able to implement a more complicated statistical analysis of H\(\upalpha\) solar flares.
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Acknowledgements
We are grateful to the referee for constructive comments and suggestions on our work and manuscript. This work was supported by the 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). The BBSO operation is supported by NJIT, US NSF AGS-1250818, and NASA NNX13AG14G grants, and the GST operation is partly supported by the Korea Astronomy and Space Science Institute and Seoul National University and by the strategic priority research program of CAS with Grant No. XDB09000000. Part of the H\(\upalpha\) data used in this article were provided by the Kanzelhöhe Observatory, University of Graz, Austria.
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Yang, M., Tian, Y., Liu, Y. et al. Automated Solar Flare Detection and Feature Extraction in High-Resolution and Full-Disk H\(\upalpha\) Images. Sol Phys 293, 81 (2018). https://doi.org/10.1007/s11207-018-1300-y
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DOI: https://doi.org/10.1007/s11207-018-1300-y