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Detection of Coronal Mass Ejections Using Multiple Features and Space–Time Continuity

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Abstract

Coronal Mass Ejections (CMEs) release tremendous amounts of energy in the solar system, which has an impact on satellites, power facilities and wireless transmission. To effectively detect a CME in Large Angle Spectrometric Coronagraph (LASCO) C2 images, we propose a novel algorithm to locate the suspected CME regions, using the Extreme Learning Machine (ELM) method and taking into account the features of the grayscale and the texture. Furthermore, space–time continuity is used in the detection algorithm to exclude the false CME regions. The algorithm includes three steps: i) define the feature vector which contains textural and grayscale features of a running difference image; ii) design the detection algorithm based on the ELM method according to the feature vector; iii) improve the detection accuracy rate by using the decision rule of the space–time continuum. Experimental results show the efficiency and the superiority of the proposed algorithm in the detection of CMEs compared with other traditional methods. In addition, our algorithm is insensitive to most noise.

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References

  • Berghmans, D., Foing, B.H., Fleck, B.: 2000, Automated detection of CMEs in LASCO data. In: Proceedings of the Soho Symposium on from Solar Min to Max: Half a Solar Cycle with Soho SP-508, 437.

    Google Scholar 

  • Boursier, Y., Lamy, P., Llebaria, A., Goudail, F., Robelus, S.: 2009, The Artemis catalog of lasco coronal mass ejections. Solar Phys. 257(1), 125.

    Article  ADS  Google Scholar 

  • Byrne, J.P., Morgan, H., Habbal, S.R., Gallagher, P.T.: 2012, Automatic detection and tracking of coronal mass ejections. II. Multiscale filtering of coronagraph images. Astrophys. J. 752(2), 145. http://stacks.iop.org/0004-637X/752/i=2/a=145 .

    Article  ADS  Google Scholar 

  • DeForest, C.E., Howard, T.A., McComas, D.J.: 2013, Tracking coronal features from the low corona to Earth: a quantitative analysis of the 2008 December 12 coronal mass ejection. Astrophys. J. 769(1), 43. DOI .

    Article  ADS  Google Scholar 

  • Floyd, O., Lamy, P., Boursier, Y., Llebaria, A.: 2013, Artemis II: a second-generation catalog of LASCO coronal mass ejections including mass and kinetic energy. Solar Phys. 288(1), 269.

    Article  ADS  Google Scholar 

  • Gallagher, P.T., Young, C.A., Byrne, J.P., James McAteer, R.T.: 2011, Coronal mass ejection detection using wavelets, curvelets and ridgelets: applications for space weather monitoring. Adv. Space Res. 47, 2118. DOI .

    Article  ADS  Google Scholar 

  • Gissot, S.F., Hochedez, J.-F., Dibos, F., Brajs̆a, R., Jacques, L., Berghmans, D., Zhukov, A., Clette, F., Wöhl, H., Antoine, J.-P.: 2003, Extracting the apparent motion from two successive eit images. In: Solar Variability as an Input to the Earth’s Environment 535, 853.

    Google Scholar 

  • Goussies, N.A., Stenborg, G., Vourlidas, A., Howard, R.: 2010a, Tracking of coronal white-light events by texture. Solar Phys. 262(2), 481. DOI .

    Article  ADS  Google Scholar 

  • Goussies, N.A., Mejail, M.E., Jacobo, J., Stenborg, G.: 2011b, Detection and tracking of coronal mass ejections based on supervised segmentation and level set. Phys. Rev. Lett. 31(6), 496. DOI .

    Google Scholar 

  • Haralick, R.M., Shanmugam, K.: 1973, Textural features for image classification. IEEE Trans. Syst. Man Cybern. SMC-3(6), 610.

    Article  Google Scholar 

  • Howard, R.A., Sheeley, N.R., Koomen, M.J., Michels, D.J.: 1985, Coronal mass ejections: 1979 – 1981. J. Geophys. Res. 90, 8173. DOI .

    Article  ADS  Google Scholar 

  • Howard, R., Moses, J., Vourlidas, A., Newmark, J., Socker, D., Plunkett, S., Korendyke, C., Cook, J., Hurley, A., Davila, J., et al.: 2008, Sun Earth connection coronal and heliospheric investigation (SECCHI). Space Sci. Rev. 136(1 – 4), 67.

    Article  ADS  Google Scholar 

  • Huang, G.B., Ding, X., Zhou, H.: 2010, Optimization method based extreme learning machine for classification. Neurocomputing 74(1 – 3), 155.

    Article  Google Scholar 

  • Huang, G.-B., Zhu, Q.-Y., Siew, C.-K.: 2006, Extreme learning machine: theory and applications. Neurocomputing 70, 489.

    Article  Google Scholar 

  • Hundhausen, A.J.: 1993, Sizes and locations of coronal mass ejections: SMM observations from 1980 and 1984 – 1989. J. Geophys. Res. 98, 13177. DOI .

    Article  ADS  Google Scholar 

  • Hurlburt, N., Jaffey, S.: 2015, Automated detection of solar eruptions. J. Space Weather Space Clim. 5(27), A39. DOI .

    Article  Google Scholar 

  • Kaiser, M.L., Kucera, T., Davila, J., Cyr, O.S., Guhathakurta, M., Christian, E.: 2008, The stereo mission: an introduction. In: The STEREO Mission, 5.

    Chapter  Google Scholar 

  • Morgan, H., Byrne, J.P., Habbal, S.R.: 2012, Automatically detecting and tracking coronal mass ejections. I. Separation of dynamic and quiescent components in coronagraph images. Astron. J. 752, 144. http://stacks.iop.org/0004-637X/752/i=2/a=144 .

    Article  ADS  Google Scholar 

  • Olmedo, O., Zhang, J., Wechsler, H., Poland, A., Borne, K.: 2008, Automatic detection and tracking of coronal mass ejections in coronagraph time series. Solar Phys. 248(2), 485. DOI .

    Article  ADS  Google Scholar 

  • Qu, M., Frank, Y.S., Jing, J., Wang, H.: 2006, Automatic detection and classification of coronal mass ejections. Solar Phys. 237(2), 419. DOI .

    Article  ADS  Google Scholar 

  • Robbrecht, E., Berghmans, D.: 2004, Automated recognition of coronal mass ejections (CMEs) in near-real-time data. Astron. Astrophys. 425(3), 1097. DOI .

    Article  ADS  Google Scholar 

  • Robbrecht, E., Berghmans, D.: 2006, A broad perspective on automated CME tracking: towards higher level space weather forecasting. In: AGU Geophys. Monograph Series 165, 33. DOI .

    Google Scholar 

  • Schapire, E.R., Freund, Y., Bartlett, P.: 1998, Boosting the margin: a new explanation for the effectiveness of voting methods. Ann. Stat. 26(05), 1651. DOI .

    Article  MathSciNet  MATH  Google Scholar 

  • Yashiro, S., Michalek, G., Gopalswamy, N.: 2008, A comparison of coronal mass ejections identified by manual and automatic methods. Ann. Geophys. 26(10), 3103. DOI . http://www.ann-geophys.net/26/3103/2008/ .

    Article  ADS  Google Scholar 

  • Yin, J., Yao, H., Lin, J., Yin, Y., Zhang, L., Liu, X., Feng, Z., Wang, X.: 2017, Coronal mass ejections detection using multiple features based ensemble learning. Neurocomputing 244, 123. DOI . http://www.sciencedirect.com/science/article/pii/S0925231217305258 .

    Article  Google Scholar 

  • Zhang, L., Yin, J.Q., Lin, J.B., Wang, X.F., Guo, J.: 2016, Detection of coronal mass ejections using AdaBoost on grayscale statistic features. New Astron. 48, 49. DOI .

    Article  ADS  Google Scholar 

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Acknowledgements

This work was supported partly by the National Natural Science Foundation of China (Grant Nos. 61203341, 61375084, 61640218, 61472163, and 61673192), the Fund for Outstanding Youth of Shandong Provincial High School (Grant No. ZR2016JL023), the Project of Shandong Province Higher Educational Science and Technology Program (Grant No. J16LN07) and the Foundation of University of Jinan (Grant No. XKY1513). The first two authors contributed equally to this work. We thank the anonymous referee and the copy editor for their helpful comments on the draft of this paper.

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Authors and Affiliations

  1. School of Information Science and Engineering, Shandong Provincial Key Laboratory of Network Based Intelligent Computing, University of Jinan, Jinan, 250022, China

    Ling Zhang, Zhi-quan Feng & Jin Zhou

  2. Automation School, Beijing University of Posts and Telecommunications, Beijing, 100876, China

    Jian-qin Yin

  3. Key Laboratory of Solar Activity, China Academy of Science, Beijing, 100012, China

    Jian-qin Yin & Jia-ben Lin

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  1. Ling Zhang
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  2. Jian-qin Yin
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  3. Jia-ben Lin
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  4. Zhi-quan Feng
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  5. Jin Zhou
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Correspondence to Jian-qin Yin.

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Zhang, L., Yin, Jq., Lin, Jb. et al. Detection of Coronal Mass Ejections Using Multiple Features and Space–Time Continuity. Sol Phys 292, 91 (2017). https://doi.org/10.1007/s11207-017-1107-2

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