Delouille, Veronique ; Hofmeister, Stefan ; Reiss, Martin ; Temmer, Manuela ; Mampaey, Benjamin

Poster presented at SDO Science workshop, Ghent, Belgium on 2018-10-28

Abstract: We demonstrate the use of machine learning algorithms in combination with segmentation techniques in order to distinguish coronal holes and filaments in solar EUV images. We used the Spatial Possibilistic Clustering Algorithm (SPoCA) to prepare data sets of manually labeled coronal hole and filament channel regions present on the Sun during the time range 2010-2016. By mapping the extracted regions from EUV observations onto HMI line-of-sight magnetograms we also include their magnetic characteristics. We computed average latitude, area, 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, taking into account the imbalance in our dataset which contains one filament channel for 15 coronal holes. We tested classifiers such as Support Vector Machine, Linear Support Vector Machine, Decision Tree, k-Nearest Neighbors, as well as ensemble classifier based on Decision Trees. Best performance in terms of True Skill Statistics are obtained with cost-sensitive learning, Support Vector Machine classifiers, and when HMI attributes are included in the dataset.