Automatic detection of limb prominences in 304 A EUV images

N. Labrosse, S. Dalla, S. Marshall, N. Gray

Research output: Contribution to conferencePaper

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.
LanguageEnglish
Publication statusUnpublished - Apr 2009
EventRAS National Astronomy Meeting 2009 - London, UK
Duration: 19 Apr 200924 Apr 2009

Conference

ConferenceRAS National Astronomy Meeting 2009
CityLondon, UK
Period19/04/0924/04/09

Fingerprint

limb
image processing
corona
pixel
automatic detection
method
analysis

Keywords

  • corona
  • prominences
  • solar physics
  • sun

Cite this

Labrosse, N., Dalla, S., Marshall, S., & Gray, N. (2009). Automatic detection of limb prominences in 304 A EUV images. Paper presented at RAS National Astronomy Meeting 2009, London, UK, .
Labrosse, N. ; Dalla, S. ; Marshall, S. ; Gray, N. / Automatic detection of limb prominences in 304 A EUV images. Paper presented at RAS National Astronomy Meeting 2009, London, UK, .
@conference{014a78f958594bde94a9079d3cfe8ea5,
title = "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 {\AA} 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.",
keywords = "corona, prominences, solar physics, sun",
author = "N. Labrosse and S. Dalla and S. Marshall and N. Gray",
note = "Also published in 'Solar Physics' http://strathprints.strath.ac.uk/26452/; RAS National Astronomy Meeting 2009 ; Conference date: 19-04-2009 Through 24-04-2009",
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month = "4",
language = "English",

}

Labrosse, N, Dalla, S, Marshall, S & Gray, N 2009, 'Automatic detection of limb prominences in 304 A EUV images' Paper presented at RAS National Astronomy Meeting 2009, London, UK, 19/04/09 - 24/04/09, .

Automatic detection of limb prominences in 304 A EUV images. / Labrosse, N.; Dalla, S.; Marshall, S.; Gray, N.

2009. Paper presented at RAS National Astronomy Meeting 2009, London, UK, .

Research output: Contribution to conferencePaper

TY - CONF

T1 - Automatic detection of limb prominences in 304 A EUV images

AU - Labrosse, N.

AU - Dalla, S.

AU - Marshall, S.

AU - Gray, N.

N1 - Also published in 'Solar Physics' http://strathprints.strath.ac.uk/26452/

PY - 2009/4

Y1 - 2009/4

N2 - 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.

AB - 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.

KW - corona

KW - prominences

KW - solar physics

KW - sun

UR - http://www.ras.org.uk/events-and-meetings

UR - http://strathprints.strath.ac.uk/26452/

UR - http://www.jenam2009.eu/

M3 - Paper

ER -

Labrosse N, Dalla S, Marshall S, Gray N. Automatic detection of limb prominences in 304 A EUV images. 2009. Paper presented at RAS National Astronomy Meeting 2009, London, UK, .