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

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.

Original language	English
Title of host publication	Applications of Computational Intelligence - 2nd IEEE Colombian Conference, ColCACI 2019, Revised Selected Papers
Editors	Alvaro David Orjuela-Cañón, Juan Carlos Figueroa-García, Julián David Arias-Londoño
Publisher	Springer Paris
Pages	235-246
Number of pages	12
ISBN (Print)	9783030362102
DOIs	https://doi.org/10.1007/978-3-030-36211-9_19
Publication status	Published - 2019
Externally published	Yes
Event	2nd IEEE Colombian Conference on Applications in Computational Intelligence, ColCACI 2019 - Barranquilla, Colombia Duration: 5 Jun 2019 → 7 Jun 2019

Publication series

Name	Communications in Computer and Information Science
Volume	1096 CCIS
ISSN (Print)	1865-0929
ISSN (Electronic)	1865-0937

Conference

Conference	2nd IEEE Colombian Conference on Applications in Computational Intelligence, ColCACI 2019
Country	Colombia
City	Barranquilla
Period	5.6.19 → 7.6.19

Keywords

Astronomy and Astrophysics
Image processing
Neural network

ASJC Scopus subject areas

Computer Science(all)
Mathematics(all)

Access to Document

10.1007/978-3-030-36211-9_19

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Cite this

Camargo, T. O., Premebida, S. M., Pechebovicz, D., Soares, V. R., Martins, M., Baroncini, V., Siqueira, H., & Oliva, D. (2019). Solar Spots Classification Using Pre-processing and Deep Learning Image Techniques. In A. D. Orjuela-Cañón, J. C. Figueroa-García, & J. D. Arias-Londoño (Eds.), Applications of Computational Intelligence - 2nd IEEE Colombian Conference, ColCACI 2019, Revised Selected Papers (pp. 235-246). (Communications in Computer and Information Science; Vol. 1096 CCIS). Springer Paris. https://doi.org/10.1007/978-3-030-36211-9_19

Solar Spots Classification Using Pre-processing and Deep Learning Image Techniques. / Camargo, Thiago O.; Premebida, Sthefanie Monica; Pechebovicz, Denise; Soares, Vinicios R.; Martins, Marcella; Baroncini, Virginia; Siqueira, Hugo; Oliva, Diego.

Applications of Computational Intelligence - 2nd IEEE Colombian Conference, ColCACI 2019, Revised Selected Papers. ed. / Alvaro David Orjuela-Cañón; Juan Carlos Figueroa-García; Julián David Arias-Londoño. Springer Paris, 2019. p. 235-246 (Communications in Computer and Information Science; Vol. 1096 CCIS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Camargo, TO, Premebida, SM, Pechebovicz, D, Soares, VR, Martins, M, Baroncini, V, Siqueira, H & Oliva, D 2019, Solar Spots Classification Using Pre-processing and Deep Learning Image Techniques. in AD Orjuela-Cañón, JC Figueroa-García & JD Arias-Londoño (eds), Applications of Computational Intelligence - 2nd IEEE Colombian Conference, ColCACI 2019, Revised Selected Papers. Communications in Computer and Information Science, vol. 1096 CCIS, Springer Paris, pp. 235-246, 2nd IEEE Colombian Conference on Applications in Computational Intelligence, ColCACI 2019, Barranquilla, Colombia, 5.6.19. https://doi.org/10.1007/978-3-030-36211-9_19

Camargo TO, Premebida SM, Pechebovicz D, Soares VR, Martins M, Baroncini V et al. Solar Spots Classification Using Pre-processing and Deep Learning Image Techniques. In Orjuela-Cañón AD, Figueroa-García JC, Arias-Londoño JD, editors, Applications of Computational Intelligence - 2nd IEEE Colombian Conference, ColCACI 2019, Revised Selected Papers. Springer Paris. 2019. p. 235-246. (Communications in Computer and Information Science). https://doi.org/10.1007/978-3-030-36211-9_19

Camargo, Thiago O. ; Premebida, Sthefanie Monica ; Pechebovicz, Denise ; Soares, Vinicios R. ; Martins, Marcella ; Baroncini, Virginia ; Siqueira, Hugo ; Oliva, Diego. / Solar Spots Classification Using Pre-processing and Deep Learning Image Techniques. Applications of Computational Intelligence - 2nd IEEE Colombian Conference, ColCACI 2019, Revised Selected Papers. editor / Alvaro David Orjuela-Cañón ; Juan Carlos Figueroa-García ; Julián David Arias-Londoño. Springer Paris, 2019. pp. 235-246 (Communications in Computer and Information Science).

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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.",

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author = "Camargo, {Thiago O.} and Premebida, {Sthefanie Monica} and Denise Pechebovicz and Soares, {Vinicios R.} and Marcella Martins and Virginia Baroncini and Hugo Siqueira and Diego Oliva",

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AU - Camargo, Thiago O.

AU - Premebida, Sthefanie Monica

AU - Pechebovicz, Denise

AU - Soares, Vinicios R.

AU - Martins, Marcella

AU - Baroncini, Virginia

AU - Siqueira, Hugo

AU - Oliva, Diego

PY - 2019

Y1 - 2019

N2 - 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.

AB - 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.

KW - Astronomy and Astrophysics

KW - Image processing

KW - Neural network

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