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Supervised classification of solar features using prior information

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De Visscher, Ruben Delouille, Véronique Dupont, Pierre [UCL] Deledalle, Charles-Alban

Context: The Sun as seen by Extreme Ultraviolet (EUV) telescopes exhibits a variety of large-scale structures. Of particular interest for space-weather applications is the extraction of active regions (AR) and coronal holes (CH). The next generation of GOESR satellites will provide continuous monitoring of the solar corona in six EUV bandpasses that are similar to the ones provided by the SDO-AIA EUV telescope since May 2010. Supervised segmentations of EUV images that are consistent with manual segmentations by for example space-weather forecasters help in extracting useful information from the raw data. Aims: We present a supervised segmentation method that is based on the Maximum A Posteriori rule. Our method allows integrating both manually segmented images as well as other type of information. It is applied on SDO-AIA images to segment them into AR, CH, and the remaining Quiet Sun (QS) part. Methods: A Bayesian classifier is applied on training masks provided by the user. The noise structure in EUV images is nontrivial, and this suggests the use of a non-parametric kernel density estimator to fit the intensity distribution within each class. Under the Naive Bayes assumption we can add information such as latitude distribution and total coverage of each class in a consistent manner. Those information can be prescribed by an expert or estimated with an Expectation-Maximization algorithm. Results: The segmentation masks are in line with the training masks given as input and show consistency over time. Introduction of additional information besides pixel intensity improves upon the quality of the final segmentation. Conclusions: Such a tool can aid in building automated segmentations that are consistent with some ground truth’ defined by the users.

Document type Article de périodique (Journal article) – Article de recherche
Access type Accès libre
Publication date 2015
Language Anglais
Journal information "Journal of Space Weather and Space Climate" - Vol. 5, no.A34, p. 1-12 (2015)
Peer reviewed yes
Publisher EDP Sciences (Les Ulis)
issn 2115-7251
e-issn 2115-7251
Publication status Publié
Affiliations UCL - SST/ICTM/INGI - Pôle en ingénierie informatique
Royal Observatory of Belgium - Solar Influences Data Analysis Center
Université de Bordeaux - Institut de Mathématiques
Keywords Solar image processing ; Corona ; Supervised Classification
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Bibliographic reference De Visscher, Ruben ; Delouille, Véronique ; Dupont, Pierre ; Deledalle, Charles-Alban. Supervised classification of solar features using prior information. In: Journal of Space Weather and Space Climate, Vol. 5, no.A34, p. 1-12 (2015)
Permanent URL http://hdl.handle.net/2078.1/166547