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dc.contributor.authorColak, Tufan*
dc.contributor.authorQahwaji, Rami S.R.*
dc.date.accessioned2009-12-14T15:50:27Z
dc.date.available2009-12-14T15:50:27Z
dc.date.issued16/12/2007
dc.identifier.citationColak T and Qahwaji RSR (2007) Automated McIntosh-Based Classification of Sunspot Groups Using MDI Images. Solar Physics. 248(2): 277-296.en
dc.identifier.urihttp://hdl.handle.net/10454/4091
dc.descriptionyesen
dc.description.abstractThis paper presents a hybrid system for automatic detection and McIntosh-based classification of sunspot groups on SOHO/MDI white-light images using active-region data extracted from SOHO/MDI magnetogram images. After sunspots are detected from MDI white-light images they are grouped/clustered using MDI magnetogram images. By integrating image-processing and neural network techniques, detected sunspot regions are classified automatically according to the McIntosh classification system. Our results show that the automated grouping and classification of sunspots is possible with a high success rate when compared to the existing manually created catalogues. In addition, our system can detect and classify sunspot groups in their early stages, which are usually missed by human observers.en
dc.description.sponsorshipEPSRCen
dc.language.isoenen
dc.publisherSpringeren
dc.rights© 2007 Springer. Reproduced in accordance with the publisher's self-archiving policy. Original publication is available at http://www.springerlink.comen
dc.subjectSolar Imaging; Space weather; McIntosh-based classification; Sunspot groups; Active-region data; SOHO/MDI magnetogram images; image-processing;en
dc.titleAutomated McIntosh-Based Classification of Sunspot Groups Using MDI Imagesen
dc.status.refereedyesen
dc.typeArticleen
dc.type.versionAccepted Manuscripten
refterms.dateFOA2018-07-18T19:39:19Z
dc.relation.urlhttp://www.springerlink.com/content/15g5u9p7346202h3/?p=eb5d493b67e243b481b72415e2851fe3&pi=0


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