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In order to utilize solar imagery for real-time feature identification andlarge-scale data science investigations of solar structures, we need maps ofthe Sun where phenomena, or themes, are labeled. Since solar imagers produceobservations every few minutes, it is not feasible to label all images by hand.Here, we compare three machine learning algorithms performing solar imageclassification using extreme ultraviolet and Hydrogen-alpha images: a maximumlikelihood model assuming a single normal probability distribution for eachtheme from Rigler et al. (2012), a maximum-likelihood model with an underlyingGaussian mixtures distribution, and a random forest model. We create a smalldatabase of expert-labeled maps to train and test these algorithms. Due to theambiguity between the labels created by different experts, a collaborativelabeling is used to include all inputs. We find the random forest algorithmperforms the best amongst the three algorithms. The advantages of thisalgorithm are best highlighted in: comparison of outputs to hand-drawn maps;response to short-term variability; and tracking long-term changes on the Sun.Our work indicates that the next generation of solar image classificationalgorithms would benefit significantly from using spatial structurerecognition, compared to only using spectral, pixel-by-pixel brightnessdistributions.

Real-time solar image classification: assessing spectral, pixel-based approaches