Solar Image Analysis and Visualization

1. Front Matter

   Pages i-iv

   PDF

2. Preface: A Topical Issue on Solar Image Analysis and Visualization

   Pages 1-1

3. FESTIVAL: A Multiscale Visualization Tool for Solar Imaging Data

   • F. Auchère, E. Soubrié, K. Bocchialini, F. LeGall

   Pages 3-14

4. Visualization of Distributed Solar Data and Metadata with the Solar Weather Browser

   • B. Nicula, C. Marqué, D. Berghmans

   Pages 15-22

5. Widespread Occurrence of Trenching Patterns in the Granulation Field: Evidence for Roll Convection?

   • A. V. Getling, A. A. Buchnev

   Pages 23-35

6. Principal Components and Independent Component Analysis of Solar and Space Data

   • A. C. Cadavid, J. K. Lawrence, A. Ruzmaikin

   Pages 37-51

7. Automatic Recognition and Characterisation of Supergranular Cells from Photospheric Velocity Fields

   • H. E. Potts, D. A. Diver

   Pages 53-65

8. Automated McIntosh-Based Classification of Sunspot Groups Using MDI Images

   • T. Colak, R. Qahwaji

   Pages 67-86

9. Multifractal Properties of Evolving Active Regions

   • P. A. Conlon, P. T. Gallagher, R. T. J. McAteer, J. Ireland, C. A. Young, P. Kestener et al.

   Pages 87-99

10. Multiscale Analysis of Active Region Evolution

    • R. J. Hewett, P. T. Gallagher, R. T. J. McAteer, C. A. Young, J. Ireland, P. A. Conlon et al.

    Pages 101-112

11. A Comparison of Feature Classification Methods for Modeling Solar Irradiance Variation

    • H. P. Jones, G. A. Chapman, K. L. Harvey, J. M. Pap, D. G. Preminger, M. J. Turmon et al.

    Pages 113-127

12. The Observed Long- and Short-Term Phase Relation between the Toroidal and Poloidal Magnetic Fields in Cycle 23

    • S. Zharkov, E. Gavryuseva, V. Zharkova

    Pages 129-148

13. Comparison of Five Numerical Codes for Automated Tracing of Coronal Loops

    • Markus J. Aschwanden, Jong Kwan Lee, G. Allen Gary, Michael Smith, Bernd Inhester

    Pages 149-167

14. Segmentation of Loops from Coronal EUV Images

    • B. Inhester, L. Feng, T. Wiegelmann

    Pages 169-183

15. The Pixelised Wavelet Filtering Method to Study Waves and Oscillations in Time Sequences of Solar Atmospheric Images

    • R. A. Sych, V. M. Nakariakov

    Pages 185-198

16. A Time-Evolving 3D Method Dedicated o the Reconstruction of Solar Plumes and Results Using Extreme Ultraviolet Data

    • Nicolas Barbey, Frédéric Auchère, Thomas Rodet, Jean-Claude Vial

    Pages 199-213

17. Automatic Detection and Classification of Coronal Holes and Filaments Based on EUV and Magnetogram Observations of the Solar Disk

    • Isabelle F. Scholl, Shadia Rifai Habbal

    Pages 215-229

18. Spatial and Temporal Noise in Solar EUV Observations

    • V. Delouille, P. Chainais, J.-F. Hochedez

    Pages 231-245

19. Multiscale Edge Detection in the Corona

    • C. Alex Young, Peter T. Gallagher

    Pages 247-259

20. Automated Prediction of CMEs Using Machine Learning of CME – Flare Associations

    • R. Qahwaji, T. Colak, M. Al-Omari, S. Ipson

    Pages 261-273

The SECCHI A and B instrument suites (Howard et al. , 2006) onboard the two STEREO mission spacecraft (Kaiser, 2005) are each composed of: one Extreme Ultra-Violet Imager (EUVI), two white-light coronagraphs (COR1 and COR2), and two wide-angle heliospheric imagers (HI1 and HI2). Technical descriptions of EUVI, COR1 and the HIs can be found in Wuelser et al. (2004), Thompson et al. (2003), and De?se et al. (2003), respectively. The images produced by SECCHI represent a data visualization challenge: i) the images are 2048×2048 pixels (except for the HIs, which are usually binned onboard 2×2), thus the vast majority of computer displays are not able to display them at full frame and full r- olution, and ii) more importantly, the ?ve instruments of SECCHI A and B were designed to be able to track Coronal Mass Ejections from their onset (with EUVI) to their pro- gation in the heliosphere (with the HIs), which implies that a set of SECCHI images that covers the propagation of a CME from its initiation site to the Earth is composed of im- ?1 ages with very different spatial resolutions – from 1. 7 arcsecondspixel for EUVI to 2. 15 ?1 arcminutespixel for HI2, i. e. 75 times larger. A similar situation exists with the angular scales of the physical objects, since the size of a CME varies by orders of magnitude as it expands in the heliosphere.

• Corona
• Solar
• Sun
• Sunspot
• automated feature detection
• image enhancement
• machine learning and solar physics
• solar image processing
• three dimensional reconstruction of CMEs

• NASA Goddard Spaceflight Center, ADNET Systems, Inc., Greenbelt, USA

  J. Ireland, C. A. Young

Policies and ethics