Automatic Detection of Limb Prominences in 304 A EUV Images
Abstract
A new algorithm for automatic detection of prominences on the solar limb in 304 A 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 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.
Keywords
Cite
@article{arxiv.0912.1099,
title = {Automatic Detection of Limb Prominences in 304 A EUV Images},
author = {Nicolas Labrosse and Silvia Dalla and Steve Marshall},
journal= {arXiv preprint arXiv:0912.1099},
year = {2011}
}
Comments
13 pages, 6 figures (4 coloured)