TY - GEN
T1 - Solar Spots Classification Using Pre-processing and Deep Learning Image Techniques
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
UR - http://www.scopus.com/inward/record.url?scp=85078448005&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85078448005&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-36211-9_19
DO - 10.1007/978-3-030-36211-9_19
M3 - Conference contribution
AN - SCOPUS:85078448005
SN - 9783030362102
T3 - Communications in Computer and Information Science
SP - 235
EP - 246
BT - Applications of Computational Intelligence - 2nd IEEE Colombian Conference, ColCACI 2019, Revised Selected Papers
A2 - Orjuela-Cañón, Alvaro David
A2 - Figueroa-García, Juan Carlos
A2 - Arias-Londoño, Julián David
PB - Springer Paris
T2 - 2nd IEEE Colombian Conference on Applications in Computational Intelligence, ColCACI 2019
Y2 - 5 June 2019 through 7 June 2019
ER -