Learning Sunspot Classification

  • Authors:

  • Trung Thanh Nguyen;Claire P. Willis;Derek J. Paddon;Sinh Hoa Nguyen;Hung Son Nguyen

  • Affiliations:

  • Department of Computer Science, University of Bath Bath BA2 7AY, United Kingdom;Department of Computer Science, University of Bath Bath BA2 7AY, United Kingdom;Department of Computer Science, University of Bath Bath BA2 7AY, United Kingdom;Polish-Japanese Institute of Information Technology Koszykowa 86, 02-008, Warsaw, Poland;Institute of Mathematics, Warsaw University, Banacha 2, Warsaw 02-095, Poland

  • Venue:
  • Fundamenta Informaticae - SPECIAL ISSUE ON CONCURRENCY SPECIFICATION AND PROGRAMMING (CS&P 2005) Ruciane-Nide, Poland, 28-30 September 2005
  • Year:
  • 2006

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Abstract

Sunspots are the subject of interest to many astronomers and solar physicists. Sunspot observation, analysis and classification form an important part of furthering the knowledge about the Sun. Sunspot classification is a manual and very labor intensive process that could be automated if successfully learned by a machine. This paper presents machine learning approaches to the problem of sunspot classification. The classification scheme attempted was the seven-class Modified Zurich scheme [18]. The data was obtained by processing NASA SOHO/MDI satellite images to extract individual sunspots and their attributes. A series of experiments were performed on the training dataset with an aim of learning sunspot classification and improving prediction accuracy. The experiments involved using decision trees, rough sets, hierarchical clustering and layered learning methods. Sunspots were characterized by their visual properties like size, shape, positions, and were manually classified by comparing extracted sunspots with corresponding active region maps (ARMaps) from the Mees Observatory at the Institute for Astronomy, University of Hawaii.