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)


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.


Image processing Astronomy and Astrophysics Neural network 


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© 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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