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Sunspot Pattern Classification using PCA and Neural Networks (Poster)The sunspot classification scheme presented in this paper is considered as a 2-D classification problem on archived datasets, and is not a real-time system. As a first step, it mirrors the Zuerich/McIntosh historical classification system and reproduces classification of sunspot patterns based on preprocessing and neural net training datasets. Ultimately, the project intends to move from more rudimentary schemes, to develop spatial-temporal-spectral classes derived by correlating spatial and temporal variations in various wavelengths to the brightness fluctuation spectrum of the sun in those wavelengths. Once the approach is generalized, then the focus will naturally move from a 2-D to an n-D classification, where "n" includes time and frequency. Here, the 2-D perspective refers both to the actual SOH0 Michelson Doppler Imager (MDI) images that are processed, but also refers to the fact that a 2-D matrix is created from each image during preprocessing. The 2-D matrix is the result of running Principal Component Analysis (PCA) over the selected dataset images, and the resulting matrices and their eigenvalues are the objects that are stored in a database, classified, and compared. These matrices are indexed according to the standard McIntosh classification scheme.
Document ID
20050238985
Acquisition Source
Ames Research Center
Document Type
Preprint (Draft being sent to journal)
Authors
Rajkumar, T.
(NASA Ames Research Center Moffett Field, CA, United States)
Thompson, D. E.
(NASA Ames Research Center Moffett Field, CA, United States)
Slater, G. L.
(Lockheed Martin Corp. United States)
Date Acquired
August 23, 2013
Publication Date
January 1, 2005
Subject Category
Astrophysics
Meeting Information
Meeting: Space Weather Week Conference
Location: Bloomfield, CO
Country: United States
Start Date: April 4, 2005
End Date: April 8, 2005
Distribution Limits
Public
Copyright
Public Use Permitted.
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