Auroral Sequence Representation and Classification Using Hidden Markov Models

The naturally occurring aurora phenomenon is a dynamically evolving process. Taking temporal information into consideration, the auroral image sequence analysis is more reasonable and desirable than using static images only. However, the enormous richness of space structures and temporal variations make automatic auroral sequence analysis a particularly challenging task. In this paper, a hidden Markov model (HMM) based representation method including features of spatial texture and dynamic evolution is presented to characterize auroral image sequences captured by all-sky imagers (ASIs). The uniform local binary patterns are employed to describe the 2-D space structures of ASI images. HMM is feasible to characterize the doubly stochastic process involved in the auroral evolution-measurable polar light activities and hidden dynamic plasma processes. We present an affine log-likelihood normalization technique to manage the sequences with different lengths. The proposed method is used in the automatic recognition of four primary categories of ASI auroral observations between the years 2003 and 2009 at the Yellow River Station, Ny-Ålesund, Svalbard. The supervised classification results on manually labeled data in 2003 demonstrate the effectiveness of the proposed technique. Compared with frame-based classification, the higher accuracies and the lower rejection rates show the advantages of the sequence-based method. The occurrence distributions of the four aurora categories were obtained through automatic classification of data gathered from 2004 to 2009. Their agreement with the multiple-wavelength intensity distribution of the dayside aurora and the conclusions made from the frame-based method further illustrate the validity of our method on auroral representation and classification.