Improvements on coronal hole detection in SDO/AIA images using supervised classification

Improvements on coronal hole detection in SDO/AIA images using supervised classification

We demonstrate the use of machine learning algorithms in combination with segmentation techniques in order to distinguish coronal holes and filaments in SDO/AIA EUV images of the Sun. Based on two coronal hole detection techniques (intensity-based thresholding, SPoCA), we prepared datasets of manual...

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Bibliographic Details
Main Authors: Reiss Martin A., Hofmeister Stefan J., De Visscher Ruben, Temmer Manuela, Veronig Astrid M., Delouille Véronique, Mampaey Benjamin, Ahammer Helmut
Format: Article
Language:English
Published: EDP Sciences 2015-01-01
Series:Journal of Space Weather and Space Climate
Subjects:
Solar wind
Coronal holes
Filament channels
Feature extraction
Supervised Classification
Textural features
Online Access:http://dx.doi.org/10.1051/swsc/2015025

Internet

http://dx.doi.org/10.1051/swsc/2015025

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