J. Space Weather Space Clim.
Volume 9, 2019
|Number of page(s)||16|
|Published online||25 October 2019|
Real-time solar image classification: Assessing spectral, pixel-based approaches
Computer Science Department, University of Colorado Boulder, Boulder, CO 80309, USA
2 Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, Boulder, CO 80309, USA
3 National Centers for Environmental Information, National Oceanic and Atmospheric Administration, Boulder, CO 80305, USA
4 Space Weather Prediction Center, National Oceanic and Atmospheric Administration, Boulder, CO 80305, USA
* Corresponding author: email@example.com
Accepted: 12 September 2019
In order to utilize solar imagery for real-time feature identification and large-scale data science investigations of solar structures, we need maps of the Sun where phenomena, or themes, are labeled. Since solar imagers produce observations every few minutes, it is not feasible to label all images by hand. Here, we compare three machine learning algorithms performing solar image classification using Extreme Ultraviolet (EUV) and Hα images: a maximum likelihood model assuming a single normal probability distribution for each theme from Rigler et al. (2012) [Space Weather 10(8): 1–16], a maximum-likelihood model with an underlying Gaussian mixtures distribution, and a random forest model. We create a small database of expert-labeled maps to train and test these algorithms. Due to the ambiguity between the labels created by different experts, a collaborative labeling is used to include all inputs. We find the random forest algorithm performs the best amongst the three algorithms. The advantages of this algorithm 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 classification algorithms would benefit significantly from using spatial structure recognition, compared to only using spectral, pixel-by-pixel brightness distributions.
Key words: classification / algorithm / machine learning / solar image processing / software
© J.M. Hughes et al., Published by EDP Sciences 2015
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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