| Title: | Exploration of U-Net in Automated Solar Coronal Loop Segmentation |
| Authors: | Moradi, Shadi Lee, Jong Kwan Tian, Qing |
| Citation: | WSCG 2021: full papers proceedings: 29. International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, p. 227-236. |
| Issue Date: | 2021 |
| Publisher: | Václav Skala - UNION Agency |
| Document type: | conferenceObject konferenční příspěvek |
| URI: | http://hdl.handle.net/11025/45028 |
| ISBN: | 978-80-86943-34-3 |
| ISSN: | 2464-4617 2464–4625(CD/DVD) |
| Keywords: | konvoluční neuronová síť;U-Net;segmentace;aplikace solární fyziky |
| Keywords in different language: | convolutional neural network;U-Net;segmentation;Solar Physics Application |
| Abstract in different language: | This paper presents a deep convolutional neural network (CNN) based method that automatically segments arc-like structures of coronal loops from the intensity images of Sun’s corona. The method explores multiple U-Netarchitecture variants which enable segmentation of coronal loop structures of active regions from NASA’s SolarDynamic Observatory (SDO) imagery. The effectiveness of the method is evaluated through experiments on bothsynthetic and real images, and the results show that the method segments the coronal loop structures accurately. |
| Rights: | © Václav Skala - UNION Agency |
| Appears in Collections: | WSCG 2021: Full Papers Proceedings |
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