This is a subjective, time-consuming, and labour-intensive process, and although classification rules are well defined, there is not always 100% unanimity in the resulting classification of sunspot groups between solar phy sicists even when working together.ĭepartment of Electronic Imaging and Media Communications, University of Bradford, RichmondĪccurate, objective classification of sunspots can solve the unanimity problem faced by In daily life, sunspot classification is mostly carried out manually by experts.

In this study, we present a computer platform for the automated detection, grouping, and SunspotsĪre part of active regions, and their local behaviour is used for the forecast of solar activity Research on solar flares showed that they are related mostly to sunspots and active regions Knowledge about the Sun, solar weather, and its effect on Earth (Phillips, 1992). The observation, analysis, and classification of sunspots form an important part in furthering In addition, our system canĭetect and classify sunspot groups in their early stages, which are usually missed by human When compared to the existing manually created catalogues. The automated grouping and classification of sunspots is possible with a high success rate

By integrating image-processing and neural network techniques, detected sunspot regions are classified automatically according to the McIntosh classification system. White-light images they are grouped/clustered using MDI magnetogram images. 2007Ībstract This paper presents a hybrid system for automatic detection and McIntosh-basedĬlassification of sunspot groups on SOHO/MDI white-light images using active-region dataĮxtracted from SOHO/MDI magnetogram images. Received: 30 April 2007 / Accepted: 22 November 2007 / Published online: 12 December 2007 Automated McIntosh-Based Classification of Sunspot

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