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. CaballeroEmail author
  • M. C. Aranda


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.


Active region Clustering method Segmentation 



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


  1. Aboudarham, J., Scholl, I., Fuller, N., Fouesneau, M., Galametz, M., Gonon, F., Maire, A., Leroy, Y.: 2008, Automatic detection and tracking of filaments for a solar feature database. Ann. Geophys. 26, 243 – 248. ADSCrossRefGoogle Scholar
  2. Adams, R., Bischof, L.: 1994, Seeded region growing. IEEE Trans. Pattern Anal. Mach. Intell. 16, 641 – 647. CrossRefGoogle Scholar
  3. Alonso Moral, J.M.: 2007, Interpretable fuzzy systems modeling with cooperation between expert and induced knowledge. Ph.D. thesis, Universidad Politécnica de Madrid. Google Scholar
  4. Anderberg, M.: 1973, Cluster Analysis for Applications, Academic Press, New York, 395. zbMATHGoogle Scholar
  5. Aranda, M.C., Caballero, C.: 2010, Automatic detection of active region on EUV solar images using fuzzy clustering. In: Hüllermeier, E., Kruse, R., Hoffmann, F. (eds.) Computational Intelligence for Knowledge-Based Systems Design, Lecture Notes in Computer Science 6178, Springer, Berlin, 69 – 78. CrossRefGoogle Scholar
  6. Arthur, D., Vassilvitskii, S.: 2007, k-means++: the advantages of careful seeding. In: Gabow, H. (ed.) Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms, Society for Industrial and Applied Mathematics, Philadelphia, 1027 – 1035. Google Scholar
  7. Babuska, R., der Venn, P.J.V., Kaymak, U.: 2002, Improved variance estimation for Gustafson Kessel clustering. In: Proceedings of the 2002 IEEE International Conference on Fuzzy Systems, 1081 – 1085. Google Scholar
  8. Barra, V., Delouille, V., Hochedez, J.-F.: 2008, Segmentation of extreme ultraviolet solar images via multichannel fuzzy clustering. Adv. Space Res. 42, 917 – 925. ADSCrossRefGoogle Scholar
  9. Barra, V., Delouille, V., Kretzschmar, M., Hochedez, J.F.: 2009, Fast and robust segmentation of solar EUV images: Algorithm and results for solar cycle 23. Astron. Astrophys. 505, 361 – 371. ADSCrossRefGoogle Scholar
  10. Benkhalil, A., Zharkova, V., Ipson, S., Zharkov, S.: 2006, Active region detection and verification with the solar feature catalogue. Solar Phys. 235, 87 – 106. ADSCrossRefGoogle Scholar
  11. Bezdek, J.C.: 1981, Pattern Recognition with Fuzzy Objective Function Algorithms, Plenum Press, New York, 256. zbMATHCrossRefGoogle Scholar
  12. Bezdek, J.C., Dunn, J.C.: 1975, Optimal fuzzy partitions: A heuristic for estimating the parameters in a mixture of normal distribution. IEEE Trans. Comput. 24, 835 – 838. zbMATHCrossRefGoogle Scholar
  13. Bezdek, J.C., Ehrlich, R., Full, W.: 1984, FCM: Fuzzy c-means algorithm. Comput. Geosci. 10, 191 – 203. ADSCrossRefGoogle Scholar
  14. Chou, C., Su, M., Lai, E.: 2004, A new cluster validity measure and its application to image compression. Pattern Anal. Appl. 7, 205 – 220. MathSciNetCrossRefGoogle Scholar
  15. Colak, T., Qahwaji, R.: 2008, Automated McIntosh-based classification of sunspot groups using MDI images. Solar Phys. 248, 277 – 296. ADSCrossRefGoogle Scholar
  16. Colak, T., Qahwaji, R.: 2009, Automated solar activity prediction: A hybrid computer platform using machine learning and solar imaging for automated prediction of solar flares. Space Weather 7, S06001. ADSCrossRefGoogle Scholar
  17. Davies, D.L., Bouldin, D.W.: 1979, A cluster separation measure. IEEE Trans. Pattern Anal. Mach. Intell. 1, 224 – 227. CrossRefGoogle Scholar
  18. Delaboudiniére, J.-P., Artzner, G.E., Brunaud, J., Gabriel, A.H., Hochedez, J.F., Millier, F., et al.: 1995, EIT: Extreme-ultraviolet Imaging Telescope for the SOHO mission. Solar Phys. 162, 291 – 312. ADSCrossRefGoogle Scholar
  19. Dunn, J.C.: 1974, Well-separated clusters and optimal fuzzy partitions. J. Cybern. 4, 95 – 104. MathSciNetCrossRefGoogle Scholar
  20. Fukuyama, Y., Sugeno, M.: 1989, A new method of choosing the number of clusters for the fuzzy c-means method. In: Proceedings of Fifth Fuzzy System Symposium, 247 – 250. Google Scholar
  21. Fuller, N., Aboudarham, J., Bentley, R.: 2005, Filament recognition and image cleaning on Meudon Hα spectroheliograms. Solar Phys. 227, 61 – 73. ADSCrossRefGoogle Scholar
  22. Gath, I., Geva, A.B.: 1989, Unsupervised optimal fuzzy clustering. IEEE Trans. Pattern Anal. Mach. Intell. 11, 773 – 781. CrossRefGoogle Scholar
  23. Ghaemi, R., Sulaiman, N., Ibrahim, H., Mustapha, N.: 2009, A survey: Clustering ensembles techniques. Proc. World Acad. Sci., Eng. Technol. 38, 644 – 653. Google Scholar
  24. Gustafson, D.E., Kessel, W.C.: 1978, Fuzzy clustering with a fuzzy covariance matrix. In: IEEE Conference on Decision and Control including the 17th Symposium on Adaptive Processes, 761 – 766. CrossRefGoogle Scholar
  25. Halkidi, M., Batistakis, Y., Varzigiannis, M.: 2002a, Cluster validity methods part I. ACM SIGMOD Rec. 31, 40 – 45. CrossRefGoogle Scholar
  26. Halkidi, M., Batistakis, Y., Varzigiannis, M.: 2002b, Cluster validity methods part II. ACM SIGMOD Rec. 31, 19 – 27. CrossRefGoogle Scholar
  27. Hartigan, J.: 1975, Clustering Algorithms, Wiley, New York, 351. zbMATHGoogle Scholar
  28. Higgins, P., Gallagher, P., McAteer, R., Bloomfield, D.: 2010, Solar magnetic feature detection and tracking for space weather monitoring. Adv. Space Res. 47, 2105 – 2117. ADSCrossRefGoogle Scholar
  29. Jain, A., Dubes, R.: 1988, Algorithms for Clustering Data, Prentice Hall, Englewood Cliffs, 320. zbMATHGoogle Scholar
  30. Joshi, A., Srivastava, N., Mathew, S.: 2010, Automated detection of filaments and their disappearance using full-disc Hα images. Solar Phys. 262, 425 – 436. ADSCrossRefGoogle Scholar
  31. Kaufman, L., Rousseeuw, P.J.: 1987, Clustering by means of medois. Technical Report, Vrije Universiteit. Google Scholar
  32. Kaufman, L., Rousseeuw, P.J.: 1990, Finding Groups in Data: An Introduction to Cluster Analysis, Wiley-Interscience, New York, 342. CrossRefGoogle Scholar
  33. Krista, L., Gallagher, P.: 2009, Automated coronal hole detection using local intensity thresholding techniques. Solar Phys. 256, 87 – 100. ADSCrossRefGoogle Scholar
  34. Macqueen, J.B.: 1967, Some methods for classification and analysis of multivariate observations. In: Le Cam, L.M., Neyman, J. (eds.) Proc. Fifth Berkeley Symp. Mathematical Statistics and Probability 1, Univ. California Press, Berkeley, 281 – 297. Google Scholar
  35. McAteer, R., Gallagher, P., Ireland, J., Young, C.: 2005, Automated boundary-extraction and region-growing techniques applied to solar magnetograms. Solar Phys. 228, 55 – 66. ADSCrossRefGoogle Scholar
  36. Nguyen, T., Willis, C., Paddon, D., Nguyen, H.: 2006, A hybrid system for learning sunspot recognition and classification. In: Proceedings of the 2006 International Conference on Hybrid Information Technology 2, Washington, 257 – 264. CrossRefGoogle Scholar
  37. Nieniewski, M.: 2004, Extraction of diffuse objects from images by means of watershed and region merging: example of solar images. IEEE Trans. Syst. Man Cybern., Part B, Cybern. 34, 796 – 801. CrossRefGoogle Scholar
  38. Otsu, N.: 1979, A threshold selection method from grey level histograms. IEEE Trans. Syst. Man Cybern. 9, 62 – 66. CrossRefGoogle Scholar
  39. Pesnel, W.D., Thompson, B.J., Chamberlin, P.C.: 2012, The solar dynamics observatory (SDO). Solar Phys. 275, 3 – 15. ADSCrossRefGoogle Scholar
  40. Qahwaji, R., Colak, T.: 2006, Automatic detection and verification of solar features. Int. J. Imaging Syst. Technol. 4, 199 – 210. Google Scholar
  41. Ridpath, I.: 2012, A Dictionary of Astronomy, 2nd edn., Oxford Univ. Press, New York. Google Scholar
  42. Robbrecht, E., Berghmans, D., van der Linden, R.: 2006, Objective CME detection over the solar cycle: A first attempt. Adv. Space Res. 38, 475 – 479. ADSCrossRefGoogle Scholar
  43. Scherrer, P.H., Bogart, R.S., Bush, R.I., Hoeksema, J.T., Kosovichev, A.G., Schou, J., et al.: 1995, The Solar Oscillation Investigation – Michelson-Doppler Imager. Solar Phys. 162, 129 – 188. ADSCrossRefGoogle Scholar
  44. Sharma, S.: 1996, Applied Multivariate Techniques, Wiley, New York, 225. Google Scholar
  45. Steinhaus, H.: 1956, Sur la division des corp materiels en parties. Bull. Acad. Pol. Sci 1, 801 – 804. Google Scholar
  46. Sych, R., Nakariakov, V., Karlicky, M., Afinogentov, S.: 2009, Relationship between wave processes in sunspots and quasi-periodic pulsations in active region flares. Astron. Astrophys. 505, 791 – 799. ADSCrossRefGoogle Scholar
  47. Tibshirani, R., Walter, G., Hastie, T.: 2001, Estimating the number of cluster in a dataset via the gap statistic. J. Roy. Stat. Soc. B 32, 411 – 423. CrossRefGoogle Scholar
  48. Verbeeck, C., Higgins, T., Colak, T., Watson, T., Delouille, V., Mapaey, B., Qahwaji, R.: 2011, A multi-wavelength analysis of active regions and sunspots by comparison of automatic detection algorithms. Solar Phys. 283, 67–95. ADSCrossRefGoogle Scholar
  49. Veronig, A., Steinegger, M., Otruba, W., Hanslmeier, A., Messerotti, M., Temmer, M., Gonzi, S., Brunner, G.: 2000, Automatic image processing in the frame of a solar flare alerting system. Hvar Obs. Bull. 24, 195 – 205. ADSGoogle Scholar
  50. Watson, F., Fletcher, L., Dalla, S., Marshall, S.: 2009, Modelling the longitudinal asymmetry in sunspot emergence: The role of the Wilson depression. Solar Phys. 260, 5 – 19. ADSCrossRefGoogle Scholar
  51. Wu, K.-L., Yang, M.-S.: 2005, A cluster validity index for fuzzy clustering. Pattern Recognit. Lett. 26, 1275 – 1291. CrossRefGoogle Scholar
  52. Xie, X.L., Beni, G.: 1991, A validity measure for fuzzy clustering. IEEE Trans. Pattern Anal. Mach. Intell. 13, 841 – 846. CrossRefGoogle Scholar
  53. Xu, R., Wunsch, D.: 2008, Clustering, Wiley-IEEE Press, New York, 368. CrossRefGoogle Scholar
  54. Yeung, K.Y., Haynor, D.R., Ruzzo, W.L.: 2001, Validating clustering for gene expression data. Bioinformatics 17, 309 – 318. CrossRefGoogle Scholar
  55. Young, C., Gallagher, P.: 2008, Multiscale edge detection in the corona. Solar Phys. 248, 457 – 469. ADSCrossRefGoogle Scholar
  56. Zharkov, S., Zharkova, V.: 2011, Statistical properties of Hα flares in relation to sunspots and active regions in the cycle 23. J. Atmos. Solar-Terr. Phys. 73, 264 – 270. ADSCrossRefGoogle Scholar
  57. Zharkov, S., Zharkova, V., Ipson, S., Benkhalil, A.: 2004, Automated recognition of sunspots on the SOHO/MDI white light solar images. In: Negoita, M.G., Howlett, R.J., Jain, L.C. (eds.) Knowledge-Based Intelligent Information and Engineering Systems, Springer, Berlin, 446 – 452. CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

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

Personalised recommendations