Improvements on coronal hole detection in SDO/AIA images using supervised classification

TL;DR

This paper presents a machine learning approach combining segmentation and classification techniques to improve coronal hole detection in solar EUV images, achieving high accuracy and real-time applicability.

Contribution

The study introduces a supervised classification framework that enhances coronal hole detection accuracy over traditional methods using magnetic and shape features.

Findings

Linear SVM achieved the highest true skill statistic (~0.90).

Magnetic field data improved classification performance.

Approach is computationally efficient for real-time applications.

Abstract

We demonstrate the use of machine learning algorithms in combination with segmentation techniques in order to distinguish coronal holes and filaments in SDO/AIA EUV images of the Sun. Based on two coronal hole detection techniques (intensity-based thresholding, SPoCA), we prepared data sets of manually labeled coronal hole and filament channel regions present on the Sun during the time range 2011 - 2013. By mapping the extracted regions from EUV observations onto HMI line-of-sight magnetograms we also include their magnetic characteristics. We computed shape measures from the segmented binary maps as well as first order and second order texture statistics from the segmented regions in the EUV images and magnetograms. These attributes were used for data mining investigations to identify the most performant rule to differentiate between coronal holes and filament channels. We applied several classifiers, namely Support Vector Machine, Linear Support Vector Machine, Decision Tree, and Random Forest and found that all classification rules achieve good results in general, with linear SVM providing the best performances (with a true skill statistic of ~0.90). Additional information from magnetic field data systematically improves the performance across all four classifiers for the SPoCA detection. Since the calculation is inexpensive in computing time, this approach is well suited for applications on real-time data. This study demonstrates how a machine learning approach may help improve upon an unsupervised feature extraction method.