Automatic Recognition of Sunspots in HSOS Full-Disk Solar Images

TL;DR

This paper presents an automated method for recognizing sunspots in full-disk solar images, achieving high accuracy and correlation with manual and external data sources, improving efficiency in solar observation analysis.

Contribution

The paper introduces a novel automated procedure combining Gaussian pre-processing, morphological operations, and adaptive thresholding for sunspot recognition and measurement.

Findings

Recognition rate of 95%

Error rate of 1.2%

High correlation (95%) with USAF/NOAA data

Abstract

A procedure is introduced to recognise sunspots automatically in solar full-disk photosphere images obtained from Huairou Solar Observing Station, National Astronomical Observatories of China. The images are first pre-processed through Gaussian algorithm. Sunspots are then recognised by the morphological Bot-hat operation and Otsu threshold. Wrong selection of sunspots is eliminated by a criterion of sunspot properties. Besides, in order to calculate the sunspots areas and the solar centre, the solar limb is extracted by a procedure using morphological closing and erosion operations and setting an adaptive threshold. Results of sunspot recognition reveal that the number of the sunspots detected by our procedure has a quite good agreement with the manual method. The sunspot recognition rate is 95% and error rate is 1.2%. The sunspot areas calculated by our method have high correlation (95%) with the area data from USAF/NOAA.