Learning Sunspot Classification

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 compari...

• Type: Journal
• Year: 2006
• Where: FUIN
• Authors: Trung Thanh Nguyen, Claire P. Willis, Derek J. Paddon, Sinh Hoa Nguyen, Hung Son Nguyen
• Keywords: FUIN 2006, Sunspot Classification, Sunspot Observation, Sunspots