Open AccessExploration of U-Net in Automated Solar Coronal Loop Segmentation
Author(s) -
Shadi Moradi,
Jong Kwan Lee,
Qi Tian
Publication year - 2021
Publication title -
computer science research notes
Language(s) - English
Resource type - Conference proceedings
SCImago Journal Rank - 0.11
H-Index - 4
eISSN - 2464-4625
pISSN - 2464-4617
DOI - 10.24132/csrn.2021.3101.25
Subject(s) - segmentation , corona (planetary geology) , computer science , coronal loop , convolutional neural network , solar observatory , coronal plane , artificial intelligence , loop (graph theory) , coronal hole , image segmentation , computer vision , physics , coronal mass ejection , solar wind , mathematics , medicine , quantum mechanics , combinatorics , astrobiology , venus , magnetic field , radiology
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-Net architecture variants which enable segmentation of coronal loop structures of active regions from NASA’s Solar Dynamic Observatory (SDO) imagery. The effectiveness of the method is evaluated through experiments on both synthetic and real images, and the results show that the method segments the coronal loop structures accurately.