Automated Sunspot Detection & Classi cation Using SOHO MDI Imagery

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Sunday, 4 January 2015
Samantha R. Howard, Air Force Institute of Technology, WPAFB, OH; and W. F. Bailey, K. S. Bartlett, and R. D. Loper

Sunspots and their group classifications are the most generally accepted measure of solar activity, but this data is currently generated by individual observers and is often based on hand drawings. This research modifies and expands previous work [1] to automatically identify and classify sunspot groups based on satellite images. By using simple and trusted algorithms that produce repeatable results, the code generates consistent and accurate classifications. The Solar and Heliospheric Observatory (SOHO) satellite collected 14 years of intensity and magnetogram data, which is analyzed to produce a database of sunspot information. In order to apply the algorithms, SOHO images are processed to correct for sensor sensitivities, as well as changes in exposure and window degradation that vary with time. Such a database improves on the currently available data from SWPC in that it does not change in time based on the biases of individual solar observers. In future research, this database could be compared with historical solar flare data to generate updated solar flare occurrence statistics and improve forecasting.

[1] G. M. Spahr. Fully automated sunspot detection and classification using SDO HMI imagery in MATLAB. Master's thesis, Air Force Institute of Technology, Graduate School of Engineering and Management, March 2014. AFIT/ENP/14-M-34.