Automated Sunspot Detection as an Alternative to Visual Observations

TL;DR

This paper presents an automated method for detecting sunspots in solar images, achieving accuracy comparable to traditional visual observations, and suitable for use by various observatories and amateurs.

Contribution

The paper introduces a novel adaptive image processing technique for automated sunspot detection that accounts for varying observational conditions and reduces seeing effects.

Findings

Automated detection matches visual observation accuracy.

Method effectively processes images from different sites.

Suitable for use by amateurs and professionals alike.

Abstract

We developed an automated method for sunspot detection using digital white-light solar images to achieve a performance similar to that of visual drawing observations in sunspot counting. To identify down to small, isolated spots correctly, we pay special attention to the accurate derivation of the quiet-disk component of the Sun, which is used as a reference to identify sunspots using a threshold. This threshold is determined using an adaptive method to process images obtained under various conditions. To eliminate the seeing effect, our method can process multiple images taken within a short time. We applied the developed method to digital images captured at three sites and compared the detection results with those of visual observations. We conclude that the proposed sunspot detection method has a similar performance to that of visual observation. This method can be widely used by public observatories and amateurs as well as professional observatories as an alternative to hand-drawn visual observation for sunspot counting.