Solar Active Region Detection Using Deep Learning

Lin Quan,Long Xu,Ling Li,Huaning Wang,Xin Huang

doi:10.3390/electronics10182284

Abstract

Solar eruptive events could affect radio communication, global positioning systems, and some high-tech equipment in space. Active regions on the Sun are the main source regions of solar eruptive events. Therefore, the automatic detection of active regions is important not only for routine observation, but also for the solar activity forecast. At present, active regions are manually or automatically extracted by using traditional image processing techniques. Because active regions dynamically evolve, it is not easy to design a suitable feature extractor. In this paper, we first overview the commonly used methods for active region detection currently. Then, two representative object detection models, faster R-CNN and YOLO V3, are employed to learn the characteristics of active regions, and finally establish a deep learning–based detection model of active regions. The performance evaluation demonstrates that the high accuracy of active region detection is achieved by both the two models. In addition, YOLO V3 is 4% and 1% better than faster R-CNN in terms of true positive (TP) and true negative (TN) indexes, respectively; meanwhile, the former is eight times faster than the latter.

Highlights

A solar active region is an area with a strong magnetic field on the Sun
The performance of solar monitor active region tracking (SMART), automated solar activity prediction (ASAP), sunspot tracking and recognition algorithm (STARA) and SPoCA was analyzed and compared in [10]. They found that ASAP tends to detect very small sunspots, while STARA has a higher threshold for sunspot detection, and SMART and SPoCA detect more regions than the National Oceanic and Atmospheric Administration (NOAA) for the active regions
In region-based CNN (R-CNN), a convolutional neural network is proposed to learn the features from data

Summary

Introduction

A solar active region is an area with a strong magnetic field on the Sun. It is considered the major source region of solar eruptive events. Barra et al [8] proposed a fuzzy clustering algorithm (the spatial possibilistic clustering algorithm (SPoCA)) to automatically segment the full-disk solar images into coronal holes, quiet Sun and active regions, respectively. This unsupervised method can overcome the imprecision of the regions’ definition. The proposed detection methods are mainly based on the intensity threshold, morphological operations, region growing algorithms and clustering methods. In these methods, the pre-defined parameters should be determined [11]. It is difficult to settle on the optimal parameters

Methods

Results

Conclusion