{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/fast-solar-image-classification-using-deep","title":"Fast Solar Image Classification Using Deep Learning and its Importance for Automation in Solar Physics","arxiv_id":"1905.13575","date":"2019-05-31","proceeding":null,"authors":["John A. Armstrong","Lyndsay Fletcher"],"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.","url_abs":"https://arxiv.org/abs/1905.13575v1","url_pdf":"https://arxiv.org/pdf/1905.13575v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"fast-solar-image-classification-using-deep","repo_url":"https://github.com/rhero12/Slic","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"image-classification","task_name":"Image Classification"},{"task_slug":"transfer-learning","task_name":"Transfer Learning"},{"task_slug":"image-classification","task_name":"image-classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}