A Transfer Learning Based Approach for Sunspot Detection

EasyChair Preprint 10608

Abstract

Sunspots are known to be the most prominent feature of the solar photosphere. Solar activities play a vital role in Space weather which greatly affects the Earth's environment. The appearance of sunspots determines the solar activities and being observed from early eighteenth century. In this work, we have implemented a deep learning model which automatically detects sunspots from MDI and HMI image datasets. Proposed model uses Alexnet based deep convolutional networks to generate promising deep hierarchical features and proposed deep learning approach achieved excellent classification accuracies. Also, model has shown the improved result with MDI data set which is equal to 99.71%, 100%, 100%, and 100 for accuracy, precision, recall, and F-score respectively. This is to construct and build robust and reliable event recognition system to monitor solar activities which are crucial to understanding space weather and for physicists it is an aid for their research.

Keyphrases: CNN, Sunspot, VGG, deep learning

@booklet{EasyChair:10608,
  author    = {Channabasava Chola and J V Biabl Benifa and Abdullah Y Muaad and Md Belal Bin Heyat and Hanumanthappa Jayappa Davanagere and Mohammed Al-Sarem and Abdulrahman Alqaraf and Bouchaib Cherradi},
  title     = {A Transfer Learning Based Approach for Sunspot Detection},
  howpublished = {EasyChair Preprint 10608},
  year      = {EasyChair, 2023}}