SunSCC: Segmenting, Grouping and Classifying Sunspots From Ground-Based Observations Using Deep Learning

Abstract

We propose a fully automated system to detect, aggregate, and classify sunspot groups according to the McIntosh scheme using ground-based white light (WL) observations from the USET facility located at the Royal Observatory of Belgium. The sunspot detection uses a Convolutional Neural Network (CNN), trained from segmentation maps obtained with an unsupervised method based on mathematical morphology and image thresholding. Given the sunspot mask, a mean-shift algorithm is used to aggregate individual sunspots into sunspot groups. This algorithm accounts for the area of each sunspot as well as for prior knowledge regarding the shape of sunspot group. A sunspot group, defined by its bounding box and location on the Sun, is finally fed into a CNN multitask classifier. The latter predicts the three components Z, p, and c in the McIntosh classification scheme. The tasks are organized hierarchically to mimic the dependency of the second (p) and third (c) components on the first (Z). The resulting CNN-based segmentation is more accurate than classical unsupervised methods, with an enhancement up to 16% of F1 score in detection of the smallest sunspots, and it is robust to the presence of clouds. The automated clustering method was able to separate groups with an accuracy of 80%, when compared to hand-made USET sunspot group catalog. The CNN-based sunspot classifier shows comparable performances to methods using continuum as well as magnetogram images recorded by instruments on space mission. We also show that an ensemble of classifiers allows differentiating reliable and potentially incorrect predictions.