Abstract: The sun spots is the major solar activity which represent the suns magnetic activity and prominent in the solar feature. This study present a new hybrid model for segmentation and further classification of sunspots from solar images obtained from Michelson Doppler Imager (MDI). Sunspots are regions on the solar surface that look black because they are cooler than the surrounding photosphere. The sunspots are regions where strong magnetic fields emerge from the solar interior and where major eruptive events occur. These energetic events can cause power outages, interrupt telecommunication and navigation services and pose hazards to astronauts. In this study, we present a new hybrid method to detect and extract sunspot features. Based on the efficient classifiers the sunspots classification is done. The input is a sequence of MDI images and the output is detection and classification of solar events. Initially, processing is done to enhance and remove noise from the image. Finally, we analyze the result with the globally accepted solar indices like international sunspot number and different standard data sources are used.
T.I. Manish, D. Murugan, K. Rajalakshmi and T. Ganesh Kumar, 2014. Detection of Sunspot Using Improved Swarm Based Region Growing. Asian Journal of Information Technology, 13: 782-786.