Automatic Detection of Sunspots and Extractionof Their Feature Parameters

Abstract

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 quantity of solar image data, the automated detection and verification of various solar features have become increasingly important for the accurate and timely forecasts of solar activity and space weather. In order to use the high time-cadence SDO/HMI data to extract the main sunspot features for forecasting solar activities, we have established an automatic detection method of sunspots based on mathematical morphology, and calculated the sunspot group area and sunspot number. By comparing our results with those obtained from the Solar Region Summary compiled by NOAA/SWPC, it is found that the sunspot group areas and sunspot numbers computed with our algorithm are in good agreement with the active region values released by SWPC, and the corresponding correlation coefficients for the sunspot group area and sunspot number are 0.77 and 0.79, respectively. By using the method of this paper, the high time-cadence feature parameters can be obtained from the HMI data to provide the timely and accurate inputs for the solar activity forecast.