Issue |
J. Space Weather Space Clim.
Volume 5, 2015
Statistical Challenges in Solar Information Processing
|
|
---|---|---|
Article Number | A34 | |
Number of page(s) | 12 | |
DOI | https://doi.org/10.1051/swsc/2015033 | |
Published online | 27 October 2015 |
Technical Article
Supervised classification of solar features using prior information
1
Royal Observatory of Belgium, 1180
Brussels, Belgium
2
Université catholique de Louvain – ICTEAM, 1348
Louvain-la-Neuve, Belgium
3
Université Bordeaux 1, 33405
Talence, France
* Corresponding author: ruben.devisscher@observatory.be
Received:
11
June
2014
Accepted:
27
August
2015
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 GOES-R 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 non-trivial, 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.
Key words: Solar image processing / Corona / Statistics and probability / Classification
© R. De Visscher et al., Published by EDP Sciences 2015
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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