Abstract: The auroral event is a physical phenomenon with rich information of texture, morphology and motion. Therefore, there is an urgent need to have a representation which captures these information simultaneously. Addressing to this problem, a three dimensional dynamic texture representation method based on oriented energy with binary coding is proposed for auroral event representation and automatic classification. At first, the local texture and oriented motion are described by the decomposition of energy into different orientations. Secondly, combining with the block partition strategy, the global morphology information is obtained as well. In order to obtain the statistical histogram, the technique of binary coding is applied for the fusion of energies with different orientations. Finally, the classifiers of nearest neighbor and support vector machines are used for classifying the auroral events from Chinese Arctic Yellow River Station. The classification results demonstrate that the proposed method achieves superior classification performance using nearest neighbor classifier compared with other two representative dynamic texture representation methods. The proposed method is specifically designed for auroral event representation, which is independent to the duration and captures the local texture, global morphology and motion simultaneously. It provides a feasible method for automatic classification of massive auroral events.

Key words: auroral event, supervised classification, oriented energies, dynamic texture, binary coding