Solar Physics

, Volume 262, Issue 2, pp 449–460 | Cite as

Automatic Detection of Limb Prominences in 304 Å EUV Images

  • N. LabrosseEmail author
  • S. Dalla
  • S. Marshall
Solar Image Processing and Analysis


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.


Corona, structures Prominences, quiescent Prominences, active 


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© Springer Science+Business Media B.V. 2010

Authors and Affiliations

  1. 1.Department of Physics and AstronomyUniversity of GlasgowGlasgowUK
  2. 2.Jeremiah Horrocks Institute for Astrophysics and SupercomputingUniversity of Central LancashirePrestonUK
  3. 3.Department of Electronic and Electrical EngineeringUniversity of StrathclydeGlasgowUK

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