Automatic auroral detection in color all-sky camera images

Jayasimha Rao, Noora Partamies, Olga Amariutei, Mikko Syrjäsuo, Koen E A Van De Sande

Research output: Contribution to journalArticleScientificpeer-review

12 Citations (Scopus)


Every winter, the all-sky cameras (ASCs) in the MIRACLE network take images of the night sky at regular intervals of 10-20 s. This amounts to millions of images that not only need to be pruned, but there is also a need for efficient auroral activity detection techniques. In this paper, we describe a method for performing automated classification of ASC images into three mutually exclusive classes: aurora, no aurora, and cloudy. This not only reduces the amount of data to be processed, but also facilitates in building statistical models linking the magnetic fluctuations and auroral activity helping us to get a step closer to forecasting auroral activity. We experimentedwithdifferent feature extraction techniques coupled with Support Vector Machines classification. Color variants of Scale Invariant Feature Transform (SIFT) features, specifically Opponent SIFT features, were found to perform better than other feature extraction techniques. With Opponent SIFT features,wewere able to build a classification model with a cross-validation accuracy of 91%, which was further improved using temporal information and elimination of outlierswhichmakes it accurate enough for operational data pruning purposes. Since the problem is essentially similar to scene detection, local point description features perform better than global- and texture-based feature descriptors.

Original languageEnglish
Article number6817533
Pages (from-to)4717-4725
Number of pages9
JournalIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Issue number12
Publication statusPublished - 1 Dec 2014
MoE publication typeA1 Journal article-refereed


  • Aurora
  • Classification
  • Scene detection
  • Vision


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