A Universal Method for Solar Filament Detection from Hα Observations using Semi-supervised Deep Learning | Andrea Diercke
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27.10.2023
Co-author
Affiliation
Leibniz Institute for Solar Physics (KIS), Germany
Main category
Natural Sciences (Astrophysics and Astrononmy)
Caption
Filaments are omnipresent features in the solar chromosphere. Their location, properties and time evolution can provide important information about changes in solar activity and assist the operational space weather forecast. Therefore, filaments have to be identified in full-disk images and their properties extracted from these images. Manual extraction is tedious and takes too much time; extraction with morphological image processing tools produces a large number of false-positive detections. Automatic object detection, segmentation, and extraction in a reliable manner allows us to process more data in a shorter time. Read more at https://espos.stream/2023/10/19/Diercke/.
Further information
Further reading
Link to the European Solar Physics Online Seminars (ESPOS) webpage: https://espos.stream/2023/10/19/Diercke/
Language
English
DOI
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