Supervised classification of solar features using prior information | Journal of Space Weather and Space Climate

Statistical Challenges in Solar Information Processing

Open Access

Issue		J. Space Weather Space Clim. Volume 5, 2015 Statistical Challenges in Solar Information Processing


Article Number		A34
Number of page(s)		12
DOI		https://doi.org/10.1051/swsc/2015033
Published online		27 October 2015

Aschwanden, M.J. Image processing techniques and feature recognition in solar physics. Sol. Phys., 262, 235–275, 2010, DOI: 10.1007/s11207-009-9474-y. [Google Scholar]
Barra, V., V. Delouille, and J. Hochedez. Segmentation of extreme ultraviolet solar images using a multispectral data fusion process, in IEEE International Conference on Fuzzy Systems, 1–6, 2007, DOI: 10.1109/FUZZY.2007.4295367. [Google Scholar]
Barra, V., V. Delouille, M. Kretzschmar, and J.F. Hochedez. Fast and robust segmentation of solar EUV images: algorithm and results for solar cycle 23. A&A, 505, 361–371, 2009, DOI: 10.1051/0004-6361/200811416. [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
Benkhalil, A., V. Zharkova, S. Zharkov, and S. Ipson. Automatic identification of active regions (Plages) in the full-disk solar images using local thresholding and region growing techniques, in Proceedings of the AISB'03 Symposium, Aberystwyth, 11 April 2003, 66–73, 2003. [Google Scholar]
Benkhalil, A., V.V. Zharkova, S. Zharkov, and S. Ipson. Active region detection and verification with the solar feature catalogue. Sol. Phys., 235, 87–106, 2006, DOI: 10.1007/s11207-006-0023-7. [Google Scholar]
Bloch, I. Information combination operators for data fusion: a comparative review with classification. IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans, 26 (1), 52–67, 1996, DOI: 10.1109/3468.477860. [CrossRef] [Google Scholar]
Caballero, C., and M.C. Aranda. A comparative study of clustering methods for active region detection in solar EUV images. Sol. Phys., 283, 691–717, 2013, DOI: 10.1007/s11207-013-0239-2. [Google Scholar]
Colak, T., and R. Qahwaji. Automated McIntosh-based classification of sunspot groups using MDI images. Sol. Phys., 248, 277–296, 2008, DOI: 10.1007/s11207-007-9094-3. [Google Scholar]
Colak, T., and R. Qahwaji. Prediction of EVE/ESP irradiance from SDO/AIA images using Fuzzy image processing and machine learning. Sol. Phys., 283, 143–156, 2013, DOI: 10.1007/s11207-011-9880-9. [NASA ADS] [CrossRef] [Google Scholar]
Curto, J.J., M. Blanca, and E. Martínez. Automatic sunspots detection on full-disk solar images using mathematical morphology. Sol. Phys., 250, 411–429, 2008, DOI: 10.1007/s11207-008-9224-6. [Google Scholar]
de Toma, G. Evolution of coronal holes and implications for high-speed solar wind during the minimum between cycles 23 and 24. Sol. Phys., 274, 195–217, 2011, DOI: 10.1007/s11207-010-9677-2. [NASA ADS] [CrossRef] [Google Scholar]
Dempster, A.P., N.M. Laird, and D.B. Rubin. Maximum likelihood from incomplete data via the EM algorithm. J. R. Stat. Soc.: Ser. B, 39, 1–38, 1977. [Google Scholar]
Dudok de Wit, T. Fast segmentation of solar extreme ultraviolet images. Sol. Phys., 239, 519–530, 2006. [Google Scholar]
Haberreiter, M., V. Delouille, B. Mampaey, C. Verbeeck, G. Del Zanna, and S. Wieman. Reconstruction of the solar EUV irradiance from 1996 to 2010 based on SOHO/EIT images. J. Space Weather Space Clim., 4 (27), A30, 2014, DOI: 10.1051/swsc/2014027. [CrossRef] [EDP Sciences] [Google Scholar]
Harvey, K.L., and O.R. White. Magnetic and radiative variability of solar surface structures. I. Image decomposition and magnetic-intensity mapping. Astrophys. J., 515, 812–831, 1999, DOI: 10.1086/307035. [Google Scholar]
Higgins, P.A., P.T. Gallagher, R.T.J. McAteer, and D.S. Bloomfield. Solar magnetic feature detection and tracking for space weather monitoring. Adv. Space Res., 47, 2105–2117, 2011, DOI: 10.1016/j.asr.2010.06.024. [NASA ADS] [CrossRef] [Google Scholar]
Hurlburt, N., M. Cheung, C. Schrijver, L. Chang, S. Freeland, et al. Heliophysics event knowledgebase for the Solar Dynamics Observatory (SDO) and beyond. Sol. Phys., 275 (1–2), 67–78, 2012, DOI: 10.1007/s11207-010-9624-2. [Google Scholar]
Kraaikamp, E., and C. Verbeeck. Solar Demon – an approach to detecting flares, dimmings, and EUV waves on SDO/AIA images. J. Space Weather Space Clim., 5, A18, 2015, DOI: 10.1051/swsc/2015019. [CrossRef] [EDP Sciences] [Google Scholar]
Krieger, A.S., A.F. Timothy, and E.C. Roelof. A coronal hole and its identification as the source of a high velocity solar wind stream. Sol. Phys., 29, 505–525, 1973, DOI: 10.1007/BF00150828. [NASA ADS] [CrossRef] [Google Scholar]
Krista, L.D., and P.T. Gallagher. Automated coronal hole detection using local intensity thresholding techniques. Sol. Phys., 256, 87–100, 2009, DOI: 10.1007/s11207-009-9357-2. [Google Scholar]
Kullback, S. Information theory and statistics, New York: John Wiley, 1959. [Google Scholar]
Lemen, J.R., A.M. Title, D.J. Akin, P.F. Boerner, and the AIA team. 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] [Google Scholar]
Lowder, C., J. Qiu, R. Leamon, and Y. Liu. Measurements of EUV coronal holes and open magnetic flux. Astrophys. J., 783 (2), 142, 2014, DOI: 10.1088/0004-637X/783/2/142. [CrossRef] [Google Scholar]
Martens, P.C.H., G.D.R. Attrill, A.R. Davey, A. Engell, S. Farid, et al. Computer vision for the Solar Dynamics Observatory (SDO). Sol. Phys., 275, 79–113, 2012, DOI: 10.1007/s11207-010-9697-y. [NASA ADS] [CrossRef] [Google Scholar]
McAteer, R.T.J., P.T. Gallagher, J. Ireland, and C.A. Young. Automated boundary-extraction and region-growing techniques applied to solar magnetograms. Sol. Phys., 228, 55–66, 2005, DOI: 10.1007/s11207-005-4075-x. [NASA ADS] [CrossRef] [Google Scholar]
Pettauer, T., and P. Brandt. On novel methods to determine areas of sunspots from photoheliograms. Sol. Phys., 175, 197–203, 1997. [NASA ADS] [CrossRef] [Google Scholar]
Preminger, D., S. Walton, and G. Chapman. Solar feature identification using contrasts and contiguity. Sol. Phys., 171, 303–330, 1997. [NASA ADS] [CrossRef] [Google Scholar]
Reiss, M., M. Temmer, R. Rotter, S. Hofmeister, and A. Veronig. Identification of coronal holes and lament channels in SDO/AIA 193A images via geometrical classification methods. Cent. Eur. Astrophys. Bull., 1, 95–104, 2014. [Google Scholar]
Reiss, M.A., S.J. Hofmeister, R. De Visscher, M. Temmer, A.M. Veronig, V. Delouille, B. Mampaey, and H. Ahammer. Improvements on coronal hole detection in SDO/AIA images using supervised classification. Journal of Space Weather and Space Climate, 5, A23, 2015, DOI: 10.1051/swsc/2015025. [CrossRef] [EDP Sciences] [Google Scholar]
Richards, J. Remote sensing digital image analysis, Springer-Verlag, Berlin, ISBN: 0471056693, 1999. [Google Scholar]
Rigler, E.J., S.M. Hill, A.A. Reinard, and R.A. Steenburgh. Solar thematic maps for space weather operations. Space Weather, 10, S08009, 2012, DOI: 10.1029/2012SW000780. [CrossRef] [Google Scholar]
Scholl, I.F., and S.R. Habbal. Automatic detection and classification of coronal holes and filaments based on EUV and magnetogram observations of the solar disk. Sol. Phys., 248, 425–439, 2008, DOI: 10.1007/s11207-007-9075-6. [NASA ADS] [CrossRef] [MathSciNet] [PubMed] [Google Scholar]
Scott, D.W. Multivariate density estimation: theory, practice, and visualization (Wiley Series in probability and statistics), 2 edn. Wiley, ISBN: 978-0-471-69755-8, 2015. [Google Scholar]
Seaton, D.B., A. De Groof, P. Shearer, D. Berghmans, and B. Nicula. SWAP observations of the long-term, large-scale evolution of the extreme-ultraviolet solar corona. Astrophys. J., 777, 72, 2013, DOI: 10.1088/0004-637X/777/1/72. [Google Scholar]
Steinegger, M., J. Bonet, M. Vazquez, and A. Jimenez. On the intensity thresholds of the network and plage regions. Sol. Phys., 177, 279–286, 1998. [NASA ADS] [CrossRef] [Google Scholar]
Turmon, M., J.M. Pap, and S. Mukhtar. Statistical pattern recognition for labeling solar active regions: application to SOHO/MDI imagery. Astrophys. J., 568, 396–407, 2002, DOI: 10.1086/338681. [Google Scholar]
Verbanac, G., B. Vršnak, S. Živković, T. Hojsak, A.M. Veronig, and M. Temmer. Solar wind high-speed streams and related geomagnetic activity in the declining phase of solar cycle 23. A&A, 533, A49, 2011, DOI: 10.1051/0004-6361/201116615. [Google Scholar]
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] [Google Scholar]
Verbeeck, C., P.A. Higgins, T. Colak, F.T. Watson, V. Delouille, B. Mampaey, and R. Qahwaji. A multi-wavelength analysis of active regions and sunspots by comparison of automatic detection algorithms. Sol. Phys., 283, 67–95, 2013, DOI: 10.1007/s11207-011-9859-6. [NASA ADS] [CrossRef] [Google Scholar]
Watson, F., L. Fletcher, S. Dalla, and S. Marshall. Modelling the longitudinal asymmetry in sunspot emergence: the role of the Wilson depression. Sol. Phys., 260, 5–19, 2009, DOI: 10.1007/s11207-009-9420-z. [NASA ADS] [CrossRef] [Google Scholar]
Worden, J., T. Woods, W. Neupert, and J. Delaboudiniere. Evolution of chromospheric structures: how chromospheric structures contribute to the solar He II 30.4 nanometer irradiance and variability. Astrophys. J., 511, 965–975, 1999. [Google Scholar]
Yeates, A.R., D.H. Mackay, and A.A. van Ballegooijen. Evolution and distribution of current helicity in full-sun simulations. Astrophys. J., 680, L165–L168, 2008, DOI: 10.1086/590057. [CrossRef] [Google Scholar]
Zhang, H., J.E. Fritts, and S.A. Goldman. Image segmentation evaluation: a survey of unsupervised methods. Comput. Vis. Image Underst., 110 (2), 260–280, 2008, DOI: 10.1016/j.cviu.2007.08.003. [Google Scholar]
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, 377–397, 2005, DOI: 10.1007/s11207-005-5005-7. [NASA ADS] [CrossRef] [Google Scholar]

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