Automatic classification of colour all-sky images
A recently developed machine vision routine detects aurora in the colour
image data with an accuracy higher than 90%. The method uses OpponentSIFT features coupled
with Support Vector Machines (SVM) classification. The training has been performed with
about 30000 manually labelled images from stations at Muonio and Kevo.
These animations include data from the station at Muonio on 13-14 November, 2012.
The exposure time was 4 seconds an the image cadence 10 seconds.
Wrongly classified images here mainly relate to very faint
aurora or partly cloudy periods of observations.
- Sample animation of Aurora (800 MB gif format)
as detected by the method.
- Animation of No Aurora (200 MB gif format)
as labelled by the method.
For more information of the method and test results, see: Rao et al.,
"Automatic auroral detection in colour all-sky camera images",
IEEE Selected Topics in Applied Earth Observations and Remote Sensing,
DOI : 10.1109/JSTARS.2014.2321433, 2014.
Data requests: Noora Partamies