Automatic detection of limb prominences in 304 Å EUV images

Tools

Labrosse, N. and Dalla, S. and Marshall, S. (2010) Automatic detection of limb prominences in 304 Å EUV images. Solar Physics, 262 (2). pp. 449-460. ISSN 0038-0938 (http://dx.doi.org/10.1007/s11207-009-9492-9)

[thumbnail of strathprints020201.pdf]
Preview
 PDF. Filename: strathprints020201.pdf
Accepted Author Manuscript
Download (981kB)| Preview

Abstract

A new algorithm for automatic detection of prominences on the solar limb in 304 Å EUV images is presented, and results of its application to SOHO/EIT data discussed. The detection is based on the method of moments combined with a classifier analysis aimed at discriminating between limb prominences, active regions, and the quiet corona. This classifier analysis is based on a Support Vector Machine (SVM). Using a set of 12 moments of the radial intensity profiles, the algorithm performs well in discriminating between the above three categories of limb structures, with a misclassification rate of 7%. Pixels detected as belonging to a prominence are then used as the starting point to reconstruct the whole prominence by morphological image-processing techniques. It is planned that a catalogue of limb prominences identified in SOHO and STEREO data using this method will be made publicly available to the scientific community.

ORCID iDs

Labrosse, N., Dalla, S. and Marshall, S. ORCID logoORCID: https://orcid.org/0000-0001-7079-5628;
CORE (COnnecting REpositories)