Solar Physics

, Volume 283, Issue 1, pp 67–95 | Cite as

A Multi-wavelength Analysis of Active Regions and Sunspots by Comparison of Automatic Detection Algorithms

  • C. Verbeeck
  • P. A. Higgins
  • T. Colak
  • F. T. Watson
  • V. Delouille
  • B. Mampaey
  • R. Qahwaji
IMAGE PROCESSING IN THE PETABYTE ERA

Abstract

Since the Solar Dynamics Observatory (SDO) began recording ≈ 1 TB of data per day, there has been an increased need to automatically extract features and events for further analysis. Here we compare the overall detection performance, correlations between extracted properties, and usability for feature tracking of four solar feature-detection algorithms: the Solar Monitor Active Region Tracker (SMART) detects active regions in line-of-sight magnetograms; the Automated Solar Activity Prediction code (ASAP) detects sunspots and pores in white-light continuum images; the Sunspot Tracking And Recognition Algorithm (STARA) detects sunspots in white-light continuum images; the Spatial Possibilistic Clustering Algorithm (SPoCA) automatically segments solar EUV images into active regions (AR), coronal holes (CH), and quiet Sun (QS). One month of data from the Solar and Heliospheric Observatory (SOHO)/Michelson Doppler Imager (MDI) and SOHO/Extreme Ultraviolet Imaging Telescope (EIT) instruments during 12 May – 23 June 2003 is analysed. The overall detection performance of each algorithm is benchmarked against National Oceanic and Atmospheric Administration (NOAA) and Solar Influences Data Analysis Center (SIDC) catalogues using various feature properties such as total sunspot area, which shows good agreement, and the number of features detected, which shows poor agreement. Principal Component Analysis indicates a clear distinction between photospheric properties, which are highly correlated to the first component and account for 52.86% of variability in the data set, and coronal properties, which are moderately correlated to both the first and second principal components. Finally, case studies of NOAA 10377 and 10365 are conducted to determine algorithm stability for tracking the evolution of individual features. We find that magnetic flux and total sunspot area are the best indicators of active-region emergence. Additionally, for NOAA 10365, it is shown that the onset of flaring occurs during both periods of magnetic-flux emergence and complexity development.

Keywords

Active regions Magnetic fields Coronal structures Sunspots 

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References

  1. Abramenko, V.I., Longcope, D.W.: 2005, Distribution of the magnetic flux in elements of the magnetic field in active regions. Astrophys. J. 619, 1160 – 1166. doi:10.1086/426710. ADSCrossRefGoogle Scholar
  2. Ahmed, O., Qahwaji, R., Colak, T., Dudok de Wit, T., Ipson, S.: 2010, A new technique for the calculation and 3d visualisation of magnetic complexities on solar satellite images. Vis. Comput. 26, 385 – 395. doi:10.1007/s00371-010-0418-1. CrossRefGoogle Scholar
  3. Aschwanden, M.J.: 2010, Image processing techniques and feature recognition in solar physics. Solar Phys. 262, 235 – 275. doi:10.1007/s11207-009-9474-y. ADSCrossRefGoogle Scholar
  4. Barra, V., Delouille, V., Kretzschmar, M., Hochedez, J.: 2009, Fast and robust segmentation of solar EUV images: algorithm and results for solar cycle 23. Astron. Astrophys. 505, 361 – 371. doi:10.1051/0004-6361/200811416. ADSCrossRefGoogle Scholar
  5. Benkhalil, A., Zharkova, V.V., Zharkov, S., Ipson, S.: 2006, Active region detection and verification with the solar feature catalogue. Solar Phys. 235, 87 – 106. doi:10.1007/s11207-006-0023-7. ADSCrossRefGoogle Scholar
  6. Bentley, R.D., Aboudarham, J., Csillaghy, A., Jacquey, C., Hapgood, M.A., Messerotti, M., Gallagher, P., Bocchialini, K., Hurlburt, N.E., Roberts, D., Sanchez Duarte, L.: 2009, Addressing science use cases with HELIO. AGU Fall Meeting Abstracts, A6. Google Scholar
  7. Colak, T., Qahwaji, R.: 2008, Automated McIntosh-based classification of sunspot groups using MDI images. Solar Phys. 248, 277 – 296. doi:10.1007/s11207-007-9094-3. ADSCrossRefGoogle Scholar
  8. 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. doi:10.1029/2008SW000401. ADSCrossRefGoogle Scholar
  9. Colak, T., Ahmed, O.W., Qahwaji, R., Higgins, P.A.: 2010, Automated solar flare prediction: is it a myth? Presentation in Seventh European Space Weather Week. http://spaceweather.inf.brad.ac.uk/colak19nov.pdf.
  10. Colak, T., Qahwaji, R., Ipson, S., Ugail, H.: 2011, Representation of solar features in 3D for creating visual solar catalogues. Adv. Space Res. 47(12), 2092 – 2104. doi:10.1016/j.asr.2010.08.030. ADSCrossRefGoogle Scholar
  11. Conlon, P.A., Gallagher, P.T., McAteer, R.T.J., Ireland, J., Young, C.A., Kestener, P., Hewett, R.J., Maguire, K.: 2008, Multifractal properties of evolving active regions. Solar Phys. 248, 297 – 309. doi:10.1007/s11207-007-9074-7. ADSCrossRefGoogle Scholar
  12. Conlon, P.A., McAteer, R.T.J., Gallagher, P.T., Fennell, L.: 2010, Quantifying the evolving magnetic structure of active regions. Astrophys. J. 722, 577 – 585. doi:10.1088/0004-637X/722/1/577. ADSCrossRefGoogle Scholar
  13. Curto, J.J., Blanca, M., Martínez, E.: 2008, Automatic sunspots detection on full-disk solar images using mathematical morphology. Solar Phys. 250, 411 – 429. doi:10.1007/s11207-008-9224-6. ADSCrossRefGoogle Scholar
  14. Dalla, S., Fletcher, L., Walton, N.A.: 2008, Invisible sunspots and rate of solar magnetic flux emergence. Astrophys. J. Lett. 479, L1 – L4. doi:10.1051/0004-6361:20078800. ADSCrossRefGoogle Scholar
  15. DeForest, C.E., Hagenaar, H.J., Lamb, D.A., Parnell, C.E., Welsch, B.T.: 2007, Solar magnetic tracking. I. Software comparison and recommended practices. Astrophys. J. 666, 576 – 587. doi:10.1086/518994. ADSCrossRefGoogle Scholar
  16. Delaboudinière, J., Artzner, G.E., Brunaud, J., Gabriel, A.H., Hochedez, J.F., Millier, F., Song, X.Y., Au, B., Dere, K.P., Howard, R.A., Kreplin, R., Michels, D.J., Moses, J.D., Defise, J.M., Jamar, C., Rochus, P., Chauvineau, J.P., Marioge, J.P., Catura, R.C., Lemen, J.R., Shing, L., Stern, R.A., Gurman, J.B., Neupert, W.M., Maucherat, A., Clette, F., Cugnon, P., van Dessel, E.L.: 1995, EIT: Extreme-Ultraviolet Imaging Telescope for the SOHO mission. Solar Phys. 162, 291 – 312. doi:10.1007/BF00733432. ADSCrossRefGoogle Scholar
  17. Dougherty, E.R., Lotufo, R.A.: 2003, Hands-on Morphological Image Processing 130, SPIE Optical Engineering Press, Washington. CrossRefGoogle Scholar
  18. Dudok de Wit, T.D.: 2006, Fast segmentation of solar extreme ultraviolet images. Solar Phys. 239, 519 – 530. ADSCrossRefGoogle Scholar
  19. Dudok de Wit, T., Auchère, F.: 2007, Multispectral analysis of solar EUV images: linking temperature to morphology. Astron. Astrophys. 466, 347 – 355. doi:10.1051/0004-6361:20066764. ADSCrossRefGoogle Scholar
  20. Dun, J., Kurokawa, H., Ishii, T.T., Liu, Y., Zhang, H.: 2007, Evolution of magnetic nonpotentiality in NOAA AR 10486. Astrophys. J. 657, 577 – 591. doi:10.1086/510373. ADSCrossRefGoogle Scholar
  21. Falconer, D.A., Moore, R.L., Gary, G.A.: 2008, Magnetogram measures of total nonpotentiality for prediction of solar coronal mass ejections from active regions of any degree of magnetic complexity. Astrophys. J. 689, 1433 – 1442. doi:10.1086/591045. ADSCrossRefGoogle Scholar
  22. Fisher, G.H., Longcope, D.W., Metcalf, T.R., Pevtsov, A.A.: 1998, Coronal heating in active regions as a function of global magnetic variables. Astrophys. J. 508, 885 – 898. doi:10.1086/306435. ADSCrossRefGoogle Scholar
  23. Gallagher, P.T., Moon, Y., Wang, H.: 2002, Active-region monitoring and flare forecasting I. Data processing and first results. Solar Phys. 209, 171 – 183. doi:10.1023/A:1020950221179. ADSCrossRefGoogle Scholar
  24. Georgoulis, M.K., Rust, D.M.: 2007, Quantitative forecasting of major solar flares. Astrophys. J. Lett. 661, L109 – L112. doi:10.1086/518718. ADSCrossRefGoogle Scholar
  25. Habash Krause, L., Franz, A., Stevenson, A.: 2011, On the application of exploratory data analysis for characterization of space weather data sets. Adv. Space Res. 47, 2199 – 2209. doi:10.1016/j.asr.2011.03.017. ADSCrossRefGoogle Scholar
  26. Handy, B.N., Schrijver, C.J.: 2001, On the evolution of the solar photospheric and coronal magnetic field. Astrophys. J. 547, 1100 – 1108. doi:10.1086/318429. ADSCrossRefGoogle Scholar
  27. Hewett, R.J., Gallagher, P.T., McAteer, R.T.J., Young, C.A., Ireland, J., Conlon, P.A., Maguire, K.: 2008, Multiscale analysis of active region evolution. Solar Phys. 248, 311 – 322. doi:10.1007/s11207-007-9028-0. ADSCrossRefGoogle Scholar
  28. Higgins, P.A., Gallagher, P.T., McAteer, R.T.J., Bloomfield, D.S.: 2011, Solar magnetic feature detection and tracking for space weather monitoring. Adv. Space Res. 47, 2105 – 2117. doi:10.1016/j.asr.2010.06.024. ADSCrossRefGoogle Scholar
  29. Howard, R.F., Harvey, J.W., Forgach, S.: 1990, Solar surface velocity fields determined from small magnetic features. Solar Phys. 130, 295 – 311. doi:10.1007/BF00156795. ADSCrossRefGoogle Scholar
  30. Hurlburt, N., Cheung, M., Schrijver, C., Chang, L., Freeland, S., Green, S., Heck, C., Jaffey, A., Kobashi, A., Schiff, D., Serafin, J., Seguin, R., Slater, G., Somani, A., Timmons, R.: 2010, Heliophysics event knowledgebase for the Solar Dynamics Observatory (SDO) and beyond. Solar Phys. doi:10.1007/s11207-010-9624-2. Google Scholar
  31. Jiang, X.: 2011, Linear subspace learning-based dimensionality reduction. IEEE Signal Process. Mag. 28(2), 16 – 26. ADSCrossRefGoogle Scholar
  32. Jolliffe, I.T.: 2002, Principal Component Analysis, 2nd edn. Springer, New York. MATHGoogle Scholar
  33. Krishnapuram, R., Keller, J.M.: 1993, A possibilistic approach to clustering. IEEE Trans. Fuzzy Syst. 1, 98 – 110. CrossRefGoogle Scholar
  34. Krishnapuram, R., Keller, J.M.: 1996, The possibilistic C-means algorithm: insights and recommendations. IEEE Trans. Fuzzy Syst. 4, 385 – 393. CrossRefGoogle Scholar
  35. LaBonte, B.J., Georgoulis, M.K., Rust, D.M.: 2007, Survey of magnetic helicity injection in regions producing X-class flares. Astrophys. J. 671, 955 – 963. doi:10.1086/522682. ADSCrossRefGoogle Scholar
  36. Lefebvre, S., Rozelot, J.: 2004, A new method to detect active features at the solar limb. Solar Phys. 219, 25 – 37. doi:10.1023/B:SOLA.0000021818.97402.1e. ADSCrossRefGoogle Scholar
  37. Leka, K.D., Barnes, G.: 2007, Photospheric magnetic field properties of flaring versus flare-quiet active regions. IV. A statistically significant sample. Astrophys. J. 656, 1173 – 1186. doi:10.1086/510282. ADSCrossRefGoogle Scholar
  38. Lites, B.W., Low, B.C., Martinez Pillet, V., Seagraves, P., Skumanich, A., Frank, Z.A., Shine, R.A., Tsuneta, S.: 1995, The possible ascent of a closed magnetic system through the photosphere. Astrophys. J. 446, 877. doi:10.1086/175845. ADSCrossRefGoogle Scholar
  39. Liu, Y., Kurokawa, H.: 2004, On a surge: properties of an emerging flux region. Astrophys. J. 610, 1136 – 1147. doi:10.1086/421715. ADSCrossRefGoogle Scholar
  40. Liu, Y., Norton, A.A., Scherrer, P.H.: 2007, A note on saturation seen in the MDI/SOHO magnetograms. Solar Phys. 241, 185 – 193. doi:10.1007/s11207-007-0296-5. ADSCrossRefGoogle Scholar
  41. Martens, P.C.H., Attrill, G.D.R., Davey, A.R., Engell, A., Farid, S., Grigis, P.C., Kasper, J., Korreck, K., Saar, S.H., Savcheva, A., Su, Y., Testa, P., Wills-Davey, M., Bernasconi, P.N., Raouafi, N., Delouille, V.A., Hochedez, J.F., Cirtain, J.W., Deforest, C.E., Angryk, R.A., de Moortel, I., Wiegelmann, T., Georgoulis, M.K., McAteer, R.T.J., Timmons, R.P.: 2011, Computer vision for the Solar Dynamics Observatory (SDO). Solar Phys. doi:10.1007/s11207-010-9697-y. Google Scholar
  42. McAteer, R.T.J., Gallagher, P.T., Ireland, J., Young, C.A.: 2005, Automated boundary-extraction and region-growing techniques applied to solar magnetograms. Solar Phys. 228, 55 – 66. doi:10.1007/s11207-005-4075-x. ADSCrossRefGoogle Scholar
  43. Morita, S., McIntosh, S.W.: 2005, Genesis of AR NOAA10314. In: Sankarasubramanian, K., Penn, M., Pevtsov, A. (eds.) Large-Scale Structures and Their Role in Solar Activity CS-346. Astron. Soc. Pac., San Francisco, 317. Google Scholar
  44. Nguyen, S.H., Nguyen, T.T., Nguyen, H.S.: 2005, Rough set approach to sunspot classification problem. In: Slezak, D., Yao, J., Peters, J.F., Ziarko, W., Hu, X. (eds.) Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing, Lecture Notes in Computer Science 3642, Springer, Berlin, 263 – 272. CrossRefGoogle Scholar
  45. Parnell, C.E., DeForest, C.E., Hagenaar, H.J., Johnston, B.A., Lamb, D.A., Welsch, B.T.: 2009, A power-law distribution of solar magnetic fields over more than five decades in flux. Astrophys. J. 698, 75 – 82. doi:10.1088/0004-637X/698/1/75. ADSCrossRefGoogle Scholar
  46. Pérez-Suárez, D., Higgins, P.A., McAteer, R.T.J., Bloomfield, D.S., Gallagher, P.T.: 2011, Automated solar feature detection for space weather applications. In: Qahwaji, R., Green, R., Hines, E. (eds.) Applied Signal and Image Processing: Multidisciplinary Advancements, IGI Global, Hershey, 207 – 225. doi:10.4018/978-1-60960-477-6. CrossRefGoogle Scholar
  47. Qahwaji, R., Colak, T.: 2006, Hybrid imaging and neural networks techniques for processing solar images. Int. J. Comput. Appl. 13(1), 9–16. Google Scholar
  48. Sarro, L.M., Berihuete, A.: 2011, Statistical techniques for the detection and analysis of solar explosive events. Astron. Astrophys. 528, A62. doi:10.1051/0004-6361/201014894. ADSCrossRefGoogle Scholar
  49. Scherrer, P.H., Bogart, R.S., Bush, R.I., Hoeksema, J.T., Kosovichev, A.G., Schou, J., Rosenberg, W., Springer, L., Tarbell, T.D., Title, A., Wolfson, C.J., Zayer, I.: MDI Engineering Team: 1995, The solar oscillations investigation–Michelson Doppler Imager. Solar Phys. 162, 129–188. doi:10.1007/BF00733429. ADSCrossRefGoogle Scholar
  50. Schrijver, C.J.: 1987, Solar active regions – radiative intensities and large-scale parameters of the magnetic field. Astron. Astrophys. 180, 241 – 252. ADSGoogle Scholar
  51. Schrijver, C.J.: 2007, A characteristic magnetic field pattern associated with all major solar flares and its use in flare forecasting. Astrophys. J. Lett. 655, L117 – L120. doi:10.1086/511857. ADSCrossRefGoogle Scholar
  52. SIDC-Team: 2003, The international sunspot number. Monthly Report on the International Sunspot Number, Online Catalogue. http://www.sidc.be/sunspot-data/dailyssn.php.
  53. Subramanian, P., Dere, K.P.: 2001, Source regions of coronal mass ejections. Astrophys. J. 561, 372 – 395. doi:10.1086/323213. ADSCrossRefGoogle Scholar
  54. 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. doi:10.1007/s11207-009-9420-z. ADSCrossRefGoogle Scholar
  55. Welsch, B.T., Longcope, D.W.: 2003, Magnetic helicity injection by horizontal flows in the quiet Sun. I. Mutual-helicity flux. Astrophys. J. 588, 620 – 629. doi:10.1086/368408. ADSCrossRefGoogle Scholar
  56. Zharkov, S., Zharkova, V., Ipson, S., Benkhalil, A.: 2004, Automated recognition of sunspots on the SOHO/MDI white light solar images. In: Knowledge-Based Intelligent Information and Engineering Systems, Lecture Notes in Computer Science 3215, Springer, Berlin, 446 – 452. CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2011

Authors and Affiliations

  • C. Verbeeck
    • 1
  • P. A. Higgins
    • 2
  • T. Colak
    • 3
  • F. T. Watson
    • 4
  • V. Delouille
    • 1
  • B. Mampaey
    • 1
  • R. Qahwaji
    • 3
  1. 1.Royal Observatory of BelgiumBrusselsBelgium
  2. 2.Trinity College DublinDublinIreland
  3. 3.University of BradfordBradfordUK
  4. 4.University of GlasgowGlasgowUK

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