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Solar Physics

, Volume 283, Issue 2, pp 691–717 | Cite as

A Comparative Study of Clustering Methods for Active Region Detection in Solar EUV Images

  • C. Caballero
  • M. C. Aranda
Article

Abstract

The increase in the amount of solar data provided by new satellites makes it necessary to develop methods to automate the detection of solar features. Here we present a method for automatically detecting active regions in solar extreme ultraviolet (EUV) images using a series of steps. Initially, the bright regions in the image are segmented using seeded region growing. In a second phase these bright regions are clustered into active regions. Partition-based clustering (both hard and fuzzy) and hierarchical clustering are compared in this work. The aim of the clustering phase is to associate a group to each segmented region in order to reduce the total number of active regions. This facilitates the documentation or subsequent monitoring of these regions. We use two indicators to validate the partitioning: i) the number of detected clusters approximates the number of active regions reported by the National Oceanic and Atmospheric Administration (NOAA) and ii) the area that defines each cluster overlaps with the area of an active region of NOAA. Experiments have been performed on over 6000 images from SOHO/EIT (195 Å). The best results were obtained using hierarchical clustering. The method detects a set of active regions in an image of the solar corona that successfully matches the number of NOAA regions. We will use these regions to perform real-time monitoring and flare detection.

Keywords

Active region Clustering method Segmentation 

Notes

Acknowledgements

This work was funded by the project TIC07-02861 of the Junta de Andalucía (Spain).

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Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  1. 1.Department of Languages and Computer Science, Engineering SchoolUniversity of MalagaMálagaSpain

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