J. Space Weather Space Clim.
Volume 6, 2016
Statistical Challenges in Solar Information Processing
Article Number A22
Number of page(s) 18
Published online 04 May 2016
  • Aydin, B., D. Kempton, V. Akkineni, R. Angryk, and K. Pillai. Mining spatiotemporal co-occurrence patterns in solar datasets. Astron. Comput., 13, 136–144, 2015, DOI: 10.1016/j.ascom.2015.10.003. [CrossRef]
  • Banda, J.M., R. Angryk. Selection of image parameters as the first step towards creating a CBIR system for the solar dynamics observatory. In Digital Image Computing: Techniques and Applications (DICTA) 2010 International Conference on, IEEE, 528–534, 2010, DOI: 10.1109/DICTA.2010.94.
  • Banda, J.M., M.A. Schuh, R.A. Angryk, K.G. Pillai, and P. McInerney. Big data new frontiers: mining, search and management of massive repositories of solar image data and solar events. In New Trends in Databases and Information Systems, Springer International Publishing, Cham, 151–158, 2014, DOI: 10.1007/978-3-319-01863-8_17. [CrossRef]
  • Bernasconi, P.N., D.M. Rust, and D. Hakim. Advanced automated solar filament detection and characterization code: description, performance, and results. Sol. Phys., 228 (1–2), 97–117, 2005, DOI: 10.1007/s11207-005-2766-y. [NASA ADS] [CrossRef]
  • Council. N. R. Severe Space Weather Events – understanding societal and economic impacts: a workshop report. The National Academies Press, 2008, DOI: 10.17226/12507.
  • Hurlburt, N., M. Cheung, C. Schrijver, L. Chang, S. Freeland, et al. Heliophysics event knowledgebase for the Solar Dynamics Observatory (SDO) and beyond. In The Solar Dynamics Observatory, Springer, 67–78, 2012, DOI: 10.1007/978-1-4614-3673-7_5.
  • Kempton, D., and R. Angryk. Tracking solar events through iterative refinement. Astron. Comput., 13, 124–135, 2015, DOI: 10.1016/j.ascom.2015. [CrossRef]
  • Kempton, D., K. Pillai, and R. Angryk. Iterative refinement of multiple targets tracking of solar events. In 2014 IEEE International Conference on Big Data (Big Data), 36–44, 2014, DOI: 10.1109/BigData.2014.7004402.
  • Lemen, J., A. Title, D. Akin, P. Boerner, C. Chou, et al. The Atmospheric Imaging Assembly (AIA) on the Solar Dynamics Observatory (SDO). Sol. Phys., 275, 17–40, 2012, DOI: 10.1007/s11207-011-9776-8. [NASA ADS] [CrossRef]
  • Martens, P., G. Attrill, A. Davey, A. Engell, S. Farid, et al. Computer Vision for the Solar Dynamics Observatory (SDO). In: P. Chamberlin, W.D. Pesnell, and B. Thompson, Editors. The Solar Dynamics Observatory, Springer, 79–113, ISBN: 978-1-4614-3672-0, 2012, DOI: 10.1007/978-1-4614-3673-7_6.
  • Pesnell, W., B. Thompson, and P. Chamberlin. The Solar Dynamics Observatory (SDO). Sol. Phys., 275, 3–15, 2012, DOI: 10.1007/s11207-011-9841-3. [NASA ADS] [CrossRef]
  • Pevtsov, A.A., K. Balasubramaniam, and J.W. Rogers. Chirality of chromospheric filaments. Astrophys. J., 595 (1), 500, 2003, DOI: 10.1086/377339. [NASA ADS] [CrossRef]
  • Pillai, K.G., R.A. Angryk, J.M. Banda, T. Wylie, and M.A. Schuh. Spatiotemporal co-occurrence rules. In New Trends in Databases and Information Systems, Springer International Publishing, Cham, 27–35, 2014, DOI: 10.1007/978-3-319-01863-8_3. [CrossRef]
  • Scherrer, P., J. Schou, R. Bush, A. Kosovichev, R. Bogart, et al. The Helioseismic and Magnetic Imager (HMI) Investigation for the Solar Dynamics Observatory (SDO). Sol. Phys., 275, 207–227, 2012, DOI: 10.1007/s11207-011-9834-2. [NASA ADS] [CrossRef]
  • Schuh, M., R. Angryk, K.G. Pillai, J. Banda, and P. Martens. A large scale solar image dataset with labeled event regions. In Proc. International Conference on Image Processing (ICIP), 4349–4353, 2013, DOI: 10.1109/ICIP.2013.6738896.
  • Schuh, M., J. Banda, P. Bernasconi, R. Angryk, and P. Martens. A comparative evaluation of automated solar filament detection. Sol. Phys., 289 (7), 2503–2524, 2014, DOI: 10.1007/s11207-014-0495-9. [CrossRef]
  • Schuh, M., J. Banda, T. Wylie, P. McInerney, K.G. Pillai, and R. Angryk. On visualization techniques for solar data mining. Astron. Comput., 10, 32–42, 2015, DOI: 10.1016/j.ascom.2014.12.003. [CrossRef]
  • Schuh, M.A., and R.A. Angryk. Massive labeled solar image data benchmarks for automated feature recognition. In 2014 IEEE International Conference on Big Data (Big Data), IEEE, 53–60, 2014, DOI: 10.1109/BigData.2014.7004404.
  • Schuh, M.A., R.A. Angryk, and P.C. Martens. Supporting data: a large-scale dataset of solar event reports from automated feature recognition modules, 2016, DOI: 10.5281/zenodo.48187.
  • Thompson, W. Coordinate systems for solar image data. A&A, 449 (2), 791–803, 2006, DOI: 10.1051/0004-6361:20054262.3. [NASA ADS] [CrossRef] [EDP Sciences]
  • Verbeeck, C., V. Delouille, B. Mampaey, and R. De Visscher. The SPoCA-suite Software for extraction, characterization, and tracking of active regions and coronal holes on EUV images. A&A, 561, A29, 2014, DOI: 10.1051/0004-6361/201321243. [NASA ADS] [CrossRef] [EDP Sciences]
  • Withbroe, G.L. Living With a Star. In AAS/Solar Physics Division Meeting #31, vol. 32 of Bulletin of the American Astronomical Society, 839, 2000, DOI: 10.1029/GM125p0045.
  • Zharkov, S., V.V. Zharkova, and S.S. Ipson. Statistical properties of sunspots in 1996–2004: I. Detection, North-South asymmetry and area distribution. Sol. Phys., 228 (1), 377–397, 2005, DOI: 10.1007/s11207-005-5005-7. [NASA ADS] [CrossRef]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.