Automatic Detection of Limb Prominences in 304 A EUV Images

TL;DR

This paper introduces an automated algorithm that detects solar limb prominences in EUV images using moments and SVM classification, achieving a 7% misclassification rate and enabling the creation of a prominence catalog.

Contribution

The novel algorithm combines moments and SVM for accurate prominence detection in EUV images, improving automation and reliability over previous methods.

Findings

Achieved 7% misclassification rate in prominence detection

Successfully discriminated between prominences, active regions, and quiet corona

Enabled creation of a comprehensive prominence catalog

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.