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.Comment: in press for SWS
