Segmentation of SoHO/EIT Images using fuzzy clustering algorithms

Astronomy and Astrophysics – Astrophysics

Scientific paper

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7500 Solar Physics, Astrophysics, And Astronomy, 7509 Corona, 7511 Coronal Holes, 7536 Solar Activity Cycle (2162), 7538 Solar Irradiance

Scientific paper

The study of the variability of the solar corona and the monitoring of its traditional regions (Coronal Holes, Quiet Sun and Active Regions) are of great importance in astrophysics as well as in view of the Space Weather and Space Climate applications. In this presentation, I will propose a multi-channel unsupervised spatially- constrained fuzzy clustering algorithm that automatically segments EUV solar images into Coronal Holes, Quiet Sun and Active Regions. The use of Fuzzy logic allows to manage the various noises present in the images and the imprecision in the definition of the above mentioned regions. The process is fast and automatic. It is applied to SoHO-EIT images taken from January 1997 till May 2005, spanning thus almost a full solar cycle. Results in terms of areas and intensity estimations are consistent with previous knowledge. The method reveal the rotational and other mid-term periodicities in the extracted time series across solar cycle 23. Further, such an approach paves the way to bridging observations between spatially resolved data from imaging telescopes and time series from radiometers. Time series resulting form the segmentation of EUV coronal images can indeed provide an essential component in the process of reconstructing the solar spectrum.

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