Coronal loop detection from solar images

Authors:
Nurcan Durak;Olfa Nasraoui;Joan Schmelz
Affiliations:
Knowledge Discovery & Web Mining Lab, Department of Computer Engineering and Computer Science, University of Louisville, Louisville, KY 40292, USA;Knowledge Discovery & Web Mining Lab, Department of Computer Engineering and Computer Science, University of Louisville, Louisville, KY 40292, USA;Solar Physic Lab, Department of Physics, University of Memphis, Memphis, TN 38152, USA
Venue:
Pattern Recognition
Year:
2009

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Abstract

In this paper, we make an overview of a methodology for the automatic retrieval of images with coronal loops from the solar image data captured by the extreme-ultraviolet imaging telescope (EIT) onboard the spacecraft SOHO (Solar and Heliospheric Observatory). Our image retrieval system provides relevant data to astrophysicists who need such data to study the coronal heating problem. As part of building this system, we investigated various image preprocessing techniques, image based features, and classifiers to automatically detect coronal loops and to indicate their locations on the images. Despite many challenges related to the coronal loop characteristic, we obtained promising results, namely, 78% precision and 80% recall in loop retrieval.