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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 experimented with different 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, we were able to build a classification model with a cross-validation accuracy of 91%, which was further improved using temporal information and elimination of outliers which makes 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.

Automatic Auroral Detection in Color All-Sky Camera Images