Skip to Main content Skip to Navigation
Journal articles

Fast and robust segmentation of solar EUV images: algorithm and results for solar cycle 23

Abstract : Context. The study of the variability of the solar corona and the monitoring of coronal holes, quiet sun and active regions are of great importance in astrophysics as well as for space weather and space climate applications. Aims. In a previous work, we presented the spatial possibilistic clustering algorithm (SPoCA). This is a multi-channel unsupervised spatially-constrained fuzzy clustering method that automatically segments solar extreme ultraviolet (EUV) images into regions of interest. The results we reported on SoHO-EIT images taken from February 1997 to May 2005 were consistent with previous knowledge in terms of both areas and intensity estimations. However, they presented some artifacts due to the method itself. Methods. Herein, we propose a new algorithm, based on SPoCA, that removes these artifacts. We focus on two points: the definition of an optimal clustering with respect to the regions of interest, and the accurate definition of the cluster edges. We moreover propose methodological extensions to this method, and we illustrate these extensions with the automatic tracking of active regions. Results. The much improved algorithm can decompose the whole set of EIT solar images over the 23rd solar cycle into regions that can clearly be identified as quiet sun, coronal hole and active region. The variations of the parameters resulting from the segmentation, i.e. the area, mean intensity, and relative contribution to the solar irradiance, are consistent with previous results and thus validate the decomposition. Furthermore, we find indications for a small variation of the mean intensity of each region in correlation with the solar cycle. Conclusions. The method is generic enough to allow the introduction of other channels or data. New applications are now expected, e.g. related to SDO-AIA data.
Complete list of metadatas

Cited literature [27 references]  Display  Hide  Download
Contributor : Nathalie Pothier <>
Submitted on : Monday, January 11, 2016 - 8:56:54 AM
Last modification on : Wednesday, October 14, 2020 - 3:47:51 AM
Long-term archiving on: : Tuesday, April 12, 2016 - 11:05:07 AM


Publisher files allowed on an open archive


Distributed under a Creative Commons Attribution - NonCommercial - NoDerivatives 4.0 International License



V Barra, V Delouille, Matthieu Kretzschmar, Jean-François Hochedez. Fast and robust segmentation of solar EUV images: algorithm and results for solar cycle 23. Astronomy and Astrophysics - A&A, EDP Sciences, 2009, 505, pp.361-371. ⟨10.1051/0004-6361/200811416⟩. ⟨insu-01253569⟩



Record views


Files downloads