Automatic detection of limb prominences in 304 Å EUV images

N. Labrosse, S. Dalla, S. Marshall

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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.
Original languageEnglish
Pages (from-to)449-460
Number of pages11
JournalSolar Physics
Issue number2
Publication statusPublished - Apr 2010


  • corona
  • prominences
  • solar physics

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