Dynamic aurora sequence recognition using Volume Local Directional Pattern with local and global features

Aurora event consists of the spatial structure and temporal evolution of aurora luminosity, which attributes to the effects of the solar wind-magnetosphere interaction and the physics of the magnetosphere-ionosphere interaction. Dynamic aurora event provides a meaningful projection of effects from plasma processes of outer space and also reveals some certain physical phenomenon and principle. Aurora sequence recognition is one of the key procedure in the analysis of dynamic aurora event. Lots of effective features for static aurora image classification are proposed. If these features for static image classification are utilized to recognize the dynamic aurora sequence, it will result in higher computational complexity. The dynamic features of aurora sequence are seldom proposed due to its complexity. To this end, this paper proposes an efficient aurora sequence descriptor which combines local and global spatial information with temporal location information, which is called as Volume Local Directional Patterns. The ring-section spatial pyramid partition structure is used in the VLDP code image which is coded by Volume Local Directional Patterns to obtain the local spatial feature. After combining the global feature of VLDP code image, the final RSPLDP feature is obtained. Finally, the STSC (self-tuning spectral clustering) method is used to classify the aurora sequence. The experimental results on the dataset which is captured from All-sky Imager (ASI) at the Chinese Yellow River Station demonstrate the effectiveness of the proposed classification scheme.