EIT Images

Solar Image Processing


2007-2011. Royal Observatory of Belgium (Véronique Delouille)


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. We proposed two ways of performing such a study :

  • a multi-channel unsupervised spatially-constrained fuzzy clustering algorithm that automatically segments EUV solar images into Coronal Holes, Quiet Sun and Active Regions. Fuzzy logic allows to manage the various noises present in the images and the imprecision in the definition of the above regions. The process is fast and automatic. It is applied to SoHO-EIT images taken from January 1997 till May 2005, i.e. along 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.
  • a fusion approach that allows to aggregate (17.1 nm, 19.5 nm) data stemming from the solar EIT instrument onboard the SoHO mission, and that is flexible enough to allow the integration of other type of information. The method is based on both a spatially constrained possibilistic clustering algorithm and a context dependent fusion operator. It aggregates the complementary and redundant information coming from the input sources. The results obtained on a 9-year dataset are consistent with those found in the solar physics literature. Unlike previous algorithms used in solar physics, our method has the ability to add further heterogeneous sources and sensors (e.g. human knowledge, images in other bandpasses, ratio of images) to the process, in order to postpone the decision step (here the segmentation of structures of interest) until sufficient information is available.

10 years of solar activity analysis


. Image-to-image translation model to generate magnetogram out of EUV images. In Proc of Machine Learning in Heliophysics, 2019.

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. Fast and Robust Segmentation of Solar EUV Images: Towards Real Time Use in the Age of SDO. In Bulletin of the American Astronomical Society, American Astronomical Society Meeting Abstracts #216, 2010.

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. Fast and robust segmentation of solar EUV images : algorithm and results for solar cycle 23. In Astronomy and Astrophysics, 505, 361-371, 2009.


. Segmentation of Extreme Ultraviolet Solar Images via Multichannel Unsupervised Fuzzy Clustering. In Advances in Space Research, 42 : pp 917–925, 2008.

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. Synergies between solar UV radiometry and imaging. COSPAR, 2006.

Project bibtex

. Segmentation of EIT Images using a fuzzy clustering algorithm : a preliminary study. In Proc of European SPM-11, Leuven, 2005.

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