Automatic detection of limb prominences in 304 A EUV images

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

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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.

Item Type:Articles
Additional Information:The original publication is available at <a href=""></a>
Keywords:Corona, structures - prominences, quiescent - prominences, active
Glasgow Author(s) Enlighten ID:Labrosse, Dr Nicolas
Authors: Labrosse, N., Dalla, S., and Marshall, S.
Subjects:Q Science > QB Astronomy
Q Science > QC Physics
College/School:College of Science and Engineering > School of Physics and Astronomy
Research Group:Astronomy & Astrophysics
Journal Name:Solar Physics
Publisher:Springer Verlag
ISSN (Online):1573-093X
Published Online:13 January 2010
Copyright Holders:Copyright © 2010 Springer
First Published:First published in Solar Physics
Publisher Policy:Reproduced in accordance with the copyright policy of the publisher.

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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
465931Solar, stellar and cosmological plasmas: a synthesis of data, modelling and theory.Declan DiverScience & Technologies Facilities Council (STFC)ST/F002149/1Physics and Astronomy