A Universal Method for Solar Filament Detection from Hα Observations using Semi-supervised Deep Learning | Andrea Diercke
Leibniz Institute for Solar Physics (KIS), Germany
Natural Sciences (Astrophysics and Astrononmy)
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/.
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