Sunspot identification and tracking with OpenCV

Abstract

Space weather forecasting has gained interest over the past five decades since mankind started exploring space and began using highly developed technology. Dangers such as coronal mass ejections (CME) can negatively affect the technology on which modern society relies heavily for communication and navigation. This study uses continuum images of the Sun to identify sunspots which lead to CMEs and track them over time. The study consisted of two phases where the first was to identify every sunspot on a continuum image by using OpenCV methods in Python. The second phase tracked those sunspots over a video consisting of continuum images. The most important part of the study was to correctly identify each sunspot on the image, for it determined the rest of the study's results. The key method used in the identification phase was the Canny edge detection method that identified all the edges of the sunspots. The tracking main method made use of the multi-object tracker function of OpenCV to track multiple sunspots over the video. In this study, background is provided to help understand the reasons for developing such a system as well as the results obtained. The results obtained using the above methods were promising, indicating promising future work into this field and project