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Solar Spots Classification Using Pre-processing and Deep Learning Image Techniques

  • Thiago O. Camargo
  • Sthefanie Monica Premebida
  • Denise Pechebovicz
  • Vinicios R. Soares
  • Marcella MartinsEmail author
  • Virginia Baroncini
  • Hugo Siqueira
  • Diego Oliva
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1096)

Abstract

Machine learning techniques and image processing have been successfully applied in many research fields. Astronomy and Astrophysics are some of these areas. In this work, we apply machine learning techniques in a new approach to classify and characterize solar spots which appear on the solar photosphere which express intense magnetic fields, and these magnetic fields present significant effects on Earth. In our experiments we consider images from Helioseismic and Magnetic Imager (HMI) in IntensitygramFlat format. We apply pre-processing techniques to recognize and count the groups of sunspots for further classification. Besides, we investigate the performance of the CNN AlexNet layer input in comparison with the Radial Basis Function Network (RBF) using different levels and combining both networks approaches. The results show that when the CNN uses the RBF to identify and classify sunspots from image processing, its performance is higher than when only CNN is used.

Keywords

Image processing Astronomy and Astrophysics Neural network 

References

  1. 1.
    Giovanelli, R.G.: The relations between eruptions and sunspots. Astrophys. J. 89, 555 (1939)CrossRefGoogle Scholar
  2. 2.
    Siscoe, G.: The space-weather enterprise: past, present, and future. J. Atmos. Sol.-Terr. Phys. 62(14), 1223–1232 (2000)CrossRefGoogle Scholar
  3. 3.
    Schwenn, R., Dal Lago, A., Huttunen, E., Gonzalez, W.D.: The association of coronal mass ejections with their effects near the earth. Ann. Geophys. 23(3), 1033–1059 (2005)CrossRefGoogle Scholar
  4. 4.
    Hoeksema, J.T., et al.: The helioseismic and magnetic imager (HMI) vector magnetic field pipeline: overview and performance. Sol. Phys. 289(9), 3483–3530 (2014)CrossRefGoogle Scholar
  5. 5.
    Damião, G.: Estudo da atividade solar no passado em função da radiação cósmica (2014)Google Scholar
  6. 6.
    Maluf, P.P.P.: O numero de manchas solares, indice da atividade do sol medido nos ultimos 50 anos. Rev. Bras. de Ensino de Física 25, 157–163 (2003)CrossRefGoogle Scholar
  7. 7.
    Hathaway, D.H.: The solar dynamo. NASA Technical report NASA-TM-111102, NAS 1.15:111102 (1994)Google Scholar
  8. 8.
    Clette, F., Berghmans, D., Vanlommel, P., Van der Linden, R.A., Koeckelenbergh, A., Wauters, L.: From the wolf number to the international sunspot index: 25 years of SIDC. Adv. Space Res. 40(7), 919–928 (2007)CrossRefGoogle Scholar
  9. 9.
    Han, J., Pei, J., Kamber, M.: Data mining: concepts and techniques. In: Data Mining: Concepts and Techniques, pp. 83–120. Elsevier (2000)Google Scholar
  10. 10.
    Smola, A., Vishwanathan, S.: Introduction to Machine Learning, vol. 32, p. 34. Cambridge University, Cambridge (2008)Google Scholar
  11. 11.
    Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)Google Scholar
  12. 12.
    Wang, L., Wang, K., Li, R.: Unsupervised feature selection based on spectral regression from manifold learning for facial expression recognition. IET Comput. Vis. 9(5), 655–662 (2015)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Chapelle, O., Scholkopf, B., Zien, A.: Semi-supervised learning (chapelle, o. et al., eds.; 2006) [book reviews]. IEEE Trans. Neural Netw. 20(3), 542–542 (2009)CrossRefGoogle Scholar
  14. 14.
    Sutton, R.S., Barto, A.G., et al.: Introduction to Reinforcement Learning, vol. 135. MIT Press, Cambridge (1998)zbMATHGoogle Scholar
  15. 15.
    Howard, R.A.: Dynamic programming and Markov processes (1960)Google Scholar
  16. 16.
    Sarwar, F., Grin, A., Periasamy, P., Portas, K., Law, J.: Detecting and counting sheep with a convolutional neural network. In: 2018 15th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6. Nov 2018Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Federal University of Technology - Paraná - Ponta Grossa - (UTFPR-PG)Ponta GrossaBrazil
  2. 2.Universidad de Guadalajara, CUCEIGuadalajaraMexico

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