Accession Number : ADA621617


Title :   Automated Sunspot Detection and Classification Using SOHO/MDI Imagery


Descriptive Note : Master's thesis


Corporate Author : AIR FORCE INSTITUTE OF TECHNOLOGY WRIGHT-PATTERSON AFB OH GRADUATE SCHOOL OF ENGINEERING AND MANAGEMENT


Personal Author(s) : Howard, Samantha R


Full Text : https://apps.dtic.mil/dtic/tr/fulltext/u2/a621617.pdf


Report Date : Mar 2015


Pagination or Media Count : 90


Abstract : This research modifies and expands previous work by Spahr in 2014 to automatically identify and classify sunspot groups in satellite images. Data from the Solar and Heliospheric Observatory (SOHO) are analyzed to produce a database of sunspot information that is not biased by individual solar observers. Results of the algorithm on SOHO/MDI data correlate well with NOAA's reported data for region properties with R2 values greater than 0.75, but with a ratio of less than one. In particular, the results of analyzing SOHO data report less than 25% of the spots reported by NOAA. By considering a test case comparison with an SDO observation, resolution is likely the main factor in detection discrepancies. 15.


Descriptors :   *CLASSIFICATION , *SATELLITE IMAGERY , *SOLAR ACTIVITY , *SUNSPOTS , ALGORITHMS , DATA BASES , DETECTION , OBSERVATION , TEST AND EVALUATION , THESES


Subject Categories : Astrophysics


Distribution Statement : APPROVED FOR PUBLIC RELEASE