Solar Event Classification Using Deep Convolutional Neural Networks

The recent advances in the field of neural networks, more specifically deep convolutional neural networks (CNN), have considerably improved the performance of computer vision and image recognition systems in domains such as medical imaging, object recognition, and scene characterization. In this work, we present the first attempt into bringing CNNs to the field of Solar Astronomy, with the application of solar event recognition. With the objective of advancing the state-of-the-art in the field, we compare the performance of multiple well established CNN architectures against the current methods of multiple solar event classification. To evaluate the effectiveness of deep learning in the solar image domain, we experimented with well-known architectures such as LeNet-5, CifarNet, AlexNet, and GoogLenet. We investigated the recognition of four solar event types using image regions extracted from the high-resolution full disk images of the Sun from the NASA's Solar Dynamics Observatory (SDO) mission. This work demonstrates the feasibility of using CNNs by obtaining improved results over the conventional pattern recognition methods used in the field.