# Fast Solar Image Classification Using Deep Learning and its Importance for Automation in Solar Physics

Source: [Fast Solar Image Classification Using Deep Learning and its Importance for Automation in Solar Physics](<https://www.sota2.com/research/paper/68fa3a041e209131ce8c7546>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-24T23:49:36.375Z

Authors: John A. Armstrong, Lyndsay Fletcher

Published: 2019-05-31T12:27:55.000Z

## Abstract

The volume of data being collected in solar physics has exponentially increased over the past decade and with the introduction of the $\\textit{Daniel K. Inouye Solar Telescope}$ (DKIST) we will be entering the age of petabyte solar data. Automated feature detection will be an invaluable tool for post-processing of solar images to create catalogues of data ready for researchers to use. We propose a deep learning model to accomplish this; a deep convolutional neural network is adept at feature extraction and processing images quickly. We train our network using data from $\\textit{Hinode/Solar Optical Telescope}$ (SOT) H$\\alpha$ images of a small subset of solar features with different geometries: filaments, prominences, flare ribbons, sunspots and the quiet Sun ($\\textit{i.e.}$ the absence of any of the other four features). We achieve near perfect performance on classifying unseen images from SOT ($\\approx$99.9\\%) in 4.66 seconds. We also for the first time explore transfer learning in a solar context. Transfer learning uses pre-trained deep neural networks to help train new deep learning models $\\textit{i.e.}$ it teaches a new model. We show that our network is robust to changes in resolution by degrading images from SOT resolution ($\\approx$0.33$^{\\prime \\prime}$ at $\\lambda$=6563\\AA{}) to $\\textit{Solar Dynamics Observatory/Atmospheric Imaging Assembly}$ (SDO/AIA) resolution ($\\approx$1.2$^{\\prime \\prime}$) without a change in performance of our network. However, we also observe where the network fails to generalise to sunspots from SDO/AIA bands 1600/1700\\AA{} due to small-scale brightenings around the sunspots and prominences in SDO/AIA 304\\AA{} due to coronal emission.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1905.13575>)

[PDF](<https://arxiv.org/pdf/1905.13575.pdf>)