Extraction of Active Regions and Coronal Holes from EUV Images Using the Unsupervised Segmentation Method in the Bayesian Framework

Abstract

The solar corona is the origin of very dynamic events that are mostly produced in active regions (AR) and coronal holes (CH). The exact location of these large-scale features can be determined by applying image-processing approaches to extreme-ultraviolet (EUV) data.We here investigate the problem of segmentation of solar EUV images into ARs, CHs, and quiet-Sun (QS) images in a firm Bayesian way. On the basis of Bayes' rule, we need to obtain both prior and likelihood models. To find the prior model of an image, we used a Potts model in non-local mode. To construct the likelihood model, we combined a mixture of a Markov-Gauss model and non-local means. After estimating labels and hyperparameters with the Gibbs estimator, cellular learning automata were employed to determine the label of each pixel.We applied the proposed method to a Solar Dynamics Observatory/ Atmospheric Imaging Assembly (SDO/AIA) dataset recorded during 2011 and found that the mean value of the filling factor of ARs is 0.032 and 0.057 for CHs. The power-law exponents of the size distribution of ARs and CHs were obtained to be -1.597 and -1.508, respectively, with the maximum likelihood estimator method. When we compare the filling factors of our method with a manual selection approach and the SPoCA algorithm, they are highly compatible.

Segmentation of Coronal Features to Understand the Solar EUV and UV Irradiance Variability III. Inclusion and Analysis of Bright Points

“…Images from the Solar Dynamics Observatory (SDO; Pesnell, Thompson, and Chamberlin, 2012) are used from the Atmospheric Imaging Assembly (AIA; Boerner et al, 2012) and the Helioseismic and Magnetic Imager (HMI; Hoeksema et al, 2014). Data from the PROBA2 spacecraft are used from the Large Yield Radiometer (LYRA; Dominique et al, 2013) and the Sun Watcher with Active Pixel System detector and Image Processing telescope (SWAP; Berghmans et al, 2006;Seaton et al, 2013).…”

“…Several bright point detection algorithms implemented in long-term statistical analysis have been described, using Yohkoh/SXT (Nakakubo and Hara, 2000;Sattarov et al, 2002), SOHO/EIT (McIntosh and Gurman, 2005;Dudok de Wit, 2006), or SDO/AIA (Dorotovic et al, 2018;Shahamatnia et al, 2016;Alipour and Safari, 2015;Arish et al, 2016) data as input. McIntosh and Gurman (2005) discussed the need to apply an intensity background threshold to their BP detection algorithm, to counterweight the different intensity changes in EUV images both over the latitudinal range as well as the solar cycle changes.…”

Characteristics of EUV Solar Coronal Bright Points Using Time Series Analyses

“…Alipour, Safari (2015) developed an automatic detection for CBPs at 193 ÅSDO/AIA images based on feeding invariant image moments to the support vector machine (proposed by Javaherian et al (2014)). An extended overview of solar image processing techniques employed for supervised and unsupervised automated feature detection of small and large events can be found in Aschwanden (2010), Arish et al (2016), andJavaherian et al (2017).…”

Complex Network for Solar Active Regions

“…It is believed that solar flares and coronal mass ejections are direct results of the changes in the topology and structure of the ARs' magnetic field (Priest & Forbes 2002;Aschwanden 2005). Because of the important role of ARs in solar activity, numerous attempts have been made to study the statistical properties of ARs (Künzel 1959;Howard 1989Howard , 2000Sammis et al 2000;Leka & Barnes 2003;Georgoulis & Rust 2007;Schrijver 2007;Falconer et al 2008;Mason & Hoeksema 2010;Falconer et al 2011;Georgoulis et al 2012;Abramenko 2015;Arish et al 2016; Barnes et al 2016;Raboonik et al 2017). Zhang et al (2010) studied both the statistical (e.g.…”