HR: 0830h
AN: SM41B-0575    [PDF]
TI: Automated Classification of Auroral Images
AU: * Syrjaesuo, M
EM: mikko@phys.ucalgary.ca
AF: Institute for Space Research, University of Calgary, University Drive 2500 N.W., Calgary, AB T2N 1N4 Canada
AU: Donovan, E F
EM: eric@phys.ucalgary.ca
AF: Institute for Space Research, University of Calgary, University Drive 2500 N.W., Calgary, AB T2N 1N4 Canada
AU: Kauristie, K
EM: Kirsti.Kauristie@fmi.fi
AF: Finnish Meteorological Institute, Geophysical Research, Vuorikatu 15 A (3rd floor), Helsinki, 00100 Finland
AB: Modern ground-based auroral All-Sky Imager (ASI) networks capture millions of images annually. While case studies still play an important role in auroral research, there are important reasons for developing automated analysis tools for use on large numbers of images, which is required for objective statistical studies. Future programmes such as NASA's THEMIS (Time History of Events and Macroscale Interactions during Substorms), which will produce about 84 million auroral images annually, can not fully utilise the data without automated tools. We have developed computer vision techniques for automatic classification of auroral images in terms of the type of aurora they contain. In this paper, we discuss our approach in formulating a numerical representation of the contents of each image, and the results of the application of these techniques to roughly 500,000 images from CANOPUS and MIRACLE. We also discuss possibilities for incorporating these techniques in multi-instrument statistical studies in which extensive ASI data sets will be combined with other obsevations, like solar wind or ground-based magnetometer data.
DE: 2409 Current systems (2708)
DE: 2455 Particle precipitation
DE: 2494 Instruments and techniques
DE: 2704 Auroral phenomena (2407)
DE: 9820 Techniques applicable in three or more fields
SC: SPA - Magnetospheric Physics [SM]
MN: 2003 Fall Meeting