Dayside aurora classification via BIFs-based sparse representation using manifold learning

• Main Authors: Han, B, Zhao, X, Tao, D, Li, X, Hu, Z, Hu, H
• Format: Article in Journal/Newspaper
• Language: unknown
• Published: 2014
• Subjects: Numerical & Computational Mathematics; Arctic; Gist
• Online Access: http://hdl.handle.net/10453/33453

© 2013, Taylor & Francis. Aurora is the typical ionosphere track generated by the interaction of solar wind and magnetosphere, whose modality and variation are significant to the study of space weather activity A new aurora classification algorithm based on biologically inspired features (BIFs) and discriminative locality alignment (DLA) is proposed in this paper First, an aurora image is represented by the BIFs, which combines the C1 units from the hierarchical model of object recognition in cortex and the gist features from the saliency map; then, the manifold learning method called DLA is used to obtain the effective sparse representation for auroras based on BIFs; finally, classification results using support vector machine and nearest neighbour with three sets of features: the C1 unit features, the gist features and the BIFs illustrate the effectiveness and robustness of our method on the real aurora image database from Chinese Arctic Yellow River Station.