Classification of Aurora Series Image Based on Eigenvalue-scaling Kernel Fisher Discriminant Analysis and Klt

Aurora is the typical ionosphere track generated by the interaction of solar wind and magnetosphere. This paper proposed a method based on eigenvalue-scaling kernel fisher discriminant analysis and Karhunen-Loeve Transform (KLT) to take advantage of interband correlation between aurora images to detect the change of aurora in serial time. Conventional classification algorithms are incapable of attaining the desired classification accuracy, and the feature of redundancy of images in a close time sequence is not considered. To solve the problems, we first apply KLT to reduce the spectral redundancies and wavelet transform to extract eigenvalues of the spatial domain. Then we employ eigenvalue-scaling kernel fisher discriminant analysis which is a modified kernel fisher discriminant analysis to realize the desired classification accuracy. Experiments carry out on time series image pointed out the effectiveness of the presented technique, which results in an increase of the classification accuracy with respect to conventional algorithms.