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Morphological feature extraction for statistical learning with applications to solar image data
Author(s) -
Stenning David C.,
Lee Thomas C. M.,
van Dyk David A.,
Kashyap Vinay,
Sandell Julia,
Young C. Alex
Publication year - 2013
Publication title -
statistical analysis and data mining: the asa data science journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.381
H-Index - 33
eISSN - 1932-1872
pISSN - 1932-1864
DOI - 10.1002/sam.11200
Subject(s) - computer science , artificial intelligence , robustness (evolution) , sketch , pattern recognition (psychology) , feature extraction , data mining , machine learning , algorithm , biochemistry , chemistry , gene
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. © 2013 Wiley Periodicals, Inc. Statistical Analysis and Data Mining, 2013