Diurnal auroral occurrence statistics obtained via machine vision

Modern ground-based digital auroral All-Sky Imager (ASI) networks capture millions of images annually. Machine vision techniques are widely utilised in the retrieval of images from large data bases. Clearly, they can play an important scientific role in dealing with data from auroral ASI networks, facilitating both efficient searches and statistical studies. Furthermore, the development of automated techniques for identifying specific types of aurora opens up the potential of ASI control software that would change instrument operation in response to evolving geophysical conditions. In this paper, we describe machine vision techniques that we have developed for use on large auroral image data sets. We present the results of application of these techniques to a 350 000 image subset of the CANOPUS Gillam ASI in the years 1993–1998. In particular, we obtain occurrence statistics for auroral arcs, patches, and Omega-bands. These results agree with those of previous manual auroral surveys.