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Morphological feature extraction for statistical learning with applications to solar image data
File(s)
Author(s)
Stenning, DC
Lee, TCM
van Dyk, DA
Kashyap, VL
Sandell, J
Type
Journal Article
Abstract
Many areas of science are generating large volumes of digital image data. In order to take full advantage of the high-resolution and high-cadence images modern technology is producing, methods to automatically process and analyze large batches of such images are needed. This involves reducing complex images to simple representations such as binary sketches or numerical summaries that capture embedded scientific information. Using techniques derived from mathematical morphology, we demonstrate how to reduce solar images into simple ‘sketch’ representations and numerical summaries that can be used for statistical learning. We demonstrate our general techniques on two specific examples: classifying sunspot groups and recognizing coronal loop structures. Our methodology reproduces manual classifications at an overall rate of 90% on a set of 119 magnetogram and white light images of sunspot groups. We also show that our methodology is competitive with other automated algorithms at producing coronal loop tracings and demonstrate robustness through noise simulations.
Date Issued
2013-07-30
Citation
Statistical Analysis and Data Mining, 2013, 6 (4), pp.329-345
URI
DOI
ISSN
1932-1872
Publisher
Wiley
Start Page
329
End Page
345
Journal / Book Title
Statistical Analysis and Data Mining
Volume
6
Issue
4
Copyright Statement
Copyright © 2010 Wiley Periodicals, Inc., A Wiley Company. This is the peer reviewed version of the following article: Stenning, D. C., Lee, T. C. M., van Dyk, D. A., Kashyap, V., Sandell, J. and Young, C. A. (2013), Morphological feature extraction for statistical learning with applications to solar image data. Statistical Analy Data Mining, 6: 329–345, which has been published in final form at http://dx.doi.org/10.1002/sam.11200.  This article may be used for non-commercial purposes in accordance With Wiley Terms and Conditions for self-archiving.
License URL
Publication Status
Published