Active Region Detection and Verification With the Solar Feature Catalogue

Abstract

We describe the automated extraction of active regions (ARs) or plages from the European Grid of Solar Observations (EGSO) Solar Feature Catalogue using a region-growing technique. In this work, Hα and Ca II K3 solar images from the Meudon Observatory and EUV solar images from the SOHO/EIT instrument were used. For better detection accuracy, the statistical properties of each quarter of a full disk solar image are used to define local intensity thresholds for an initial segmentation that helps to define AR seeds. Median filtering and morphological operations are applied to the resulting binary image in order to remove noise and to merge broken regions. The centroids of each labelled region are used as seeds, from which a region-growing procedure starts. Statistics-based local thresholding is also applied to compute upper-and lower-threshold intensity values defining the spatial extents of the regions. The detection results obtained with the resulting automated thresholding and region-growing (ATRG) procedure are compared day-by-day with the synoptic maps manually generated by the Meudon Observatory and NOAA for 2 months in 2002 and more coarsely over a 5-year period. The moderate correlation found between our detection results and those produced manually on the other data sets reveals a need for a unified active region definition. As an application of the SFC for ARs we present the tracking of the active region AR NOAA 10484 during its appearance on the solar disk from 19-26 October 2003 and compare its intensity variations for Hα and Fe XII 195Å wavelengths.

Image Processing Techniques and Feature Recognition in Solar Physics

“…Some examples of such data manipulations are shown in Figure 6. Such mathematical-morphology methods have been applied to a number of automated tasks: to measure sunspot areas and active-region sizes in magnetograms McAteer et al, 2005), and white-light solar images (Curto, Blanca, and Martinez, 2008), to catalog active regions and filaments in Hα and Ca II images (Shih and Kowalski, 2003;Benkhalil et al, 2006;Fuller, Aboudarham, and Bentley, 2005;Bernasconi, Rust, and Hakim, 2005) and SOHO/EIT images (Scholl and Habbal, 2008), to identify the chirality and magnetic-field inversion in filaments (Bernasconi, Rust, and Hakim, 2005;Ipson et al, 2005Ipson et al, , 2009, to measure the magnetic-helicity injection of flaring active regions (LaBonte, Georgoulis, and Rust, 2007), for active-region identification and magnetic-field disambiguation (Georgoulis, Raouafi, and Henney, 2008), to deduce the magnetic tilts and charge separation in sunspot groups , to quantify the phase relation between toroidal (sunspot) and poloidal (background) magnetic field (Zharkov, Gavryuseva, and Zharkova, 2008), to quantify the role of the Wilson depression in sunspot detection (Watson et al, 2009), to off-limb detection of EUV prominences (Foullon and Verwichte, 2006), to detect sigmoids in full-disk soft X-ray images (Bernasconi and Georgoulis, 2009), or to detect coronal holes (Scholl and Habbal, 2008;Krista and Gallagher, 2009). …”

The SPoCA-suite: Software for extraction, characterization, and tracking of active regions and coronal holes on EUV images

“…Verbeeck et al (2011) provide a detailed comparison of outputs from four automatic detection algorithms that detect sunspots, magnetic, and coronal ARs using six weeks of SOHO-EIT data. At the chromospheric level, network and plage regions are separated using thresholding methods (Steinegger et al 1998;Worden et al 1999), which are possibly combined with region-growing techniques (Benkhalil et al 2006). Coronal ARs are segmented using either local thresholding, region-growing methods (Benkhalil et al 2006), supervised techniques (Dudok de Wit 2006;Colak & Qahwaji 2013), or unsupervised techniques (Barra et al 2009).…”

“…At the chromospheric level, network and plage regions are separated using thresholding methods (Steinegger et al 1998;Worden et al 1999), which are possibly combined with region-growing techniques (Benkhalil et al 2006). Coronal ARs are segmented using either local thresholding, region-growing methods (Benkhalil et al 2006), supervised techniques (Dudok de Wit 2006;Colak & Qahwaji 2013), or unsupervised techniques (Barra et al 2009). Revathy et al (2005) compares segmentation results of pixelwise fractal dimension of EIT images using thresholding, region-growing techniques, and supervised classification.…”

Automatic detection and tracking of filaments for a solar feature database

“…One of its work packages was devoted to solar feature recognition. The sizeable effort made in this field was productive and successful, and codes were developed to automatically detect filaments , sunspots , faculae (Benkhalil et al, 2006) and prominences.…”