The solar corona is the origin of very dynamic events, mostly produced in active regions (AR) and coronal holes (CH). Determining the exact location of these large-scale features can be done by image processing approaches applied to extreme ultraviolet data. This paper tackles the problem of segmentation of solar EUV images into ARs, CHs, and QS (quiet Sun) 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 use a Potts model in non-local mode. To construct the likelihood model, we combine a mixture of Markov-Gauss model and non-local means. After estimating labels and hyperparameters by using Gibbs estimator, cellular learning automata are employed to find the label of each pixel. By applying the proposed method on a Solar Dynamics Observatory/ Atmospheric Imaging Assembly (SDO/AIA) dataset recorded during the year 2011, the mean value of the filling factor of ARs is 0.032, and for CHs is 0.057. By using the maximum likelihood estimator method, the power-law exponents of the size distribution of ARs and CHs are obtained -1.597 and -1.508, respectively. Comparing filling factors of our method with a manual selection approach and the SPoCA algorithm shows great compatibility.
کلید واژگان :Sun: corona . Sun: activity . Sun: EUV radiation . Techniques: image processing
ارزش ریالی : 600000 ریال
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