Abstract
The dynamically evolving process of aurora is closely related to complex and energetic plasma processes of the outer magnetosphere, so aurora image sequences often have complex underlying structures. In this paper, we present a novel aurora sequences classification and aurora events detection method based hidden conditional random fields (HCRF) employing spatial texture features. Firstly, divided uniform local binary patterns (uLBP) are extracted as the spatial texture features; then HCRF model is built for the spatial texture features of aurora sequences; at last, the model is applied in automatic classification and detection for four primary categories of dayside auroral observations. The supervised classification results on labeled data demonstrate the effectiveness of our method. The occurrence distributions of four categories from automatic detection confirm the multiple-wavelength intensity distribution of dayside aurora, and further illustrate the validity of our method.
This research was supported in part by the National Nature Science Foundation, P.R. China. (No. 61571353, 61172118, 61471202), Jiangsu Province Universities Natural Science Research Key Grant Project (No. 13KJA510004), Natural Science Foundation of Jiangsu Province (BK20130867), and a Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions(Information and Communication Engineering).
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Xu, B., Chen, C., Gan, Z., Liu, B. (2016). Aurora Sequences Classification and Aurora Events Detection Based on Hidden Conditional Random Fields. In: Tan, T., Li, X., Chen, X., Zhou, J., Yang, J., Cheng, H. (eds) Pattern Recognition. CCPR 2016. Communications in Computer and Information Science, vol 663. Springer, Singapore. https://doi.org/10.1007/978-981-10-3005-5_33
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DOI: https://doi.org/10.1007/978-981-10-3005-5_33
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