An Aurora Image Classification Method based on Compressive Sensing and Distributed WKNN

Reasonable Aurora classification is particularly important for studying the relationship between aurora phenomena and the process of magnetosphere dynamics. With the development of computer science, image processing and pattern recognition technology, new approaches for Aurora classification are springing up. In this paper, we extract the LBP feature of images and use the distributed weighted KNN based on optimal discriminant dictionary for sparse representation as the classification method to discriminate the shape of aurora. The proposed method combines compressed sensing approaches and distributed computing technology, improving the accuracy and effectiveness of the existed sparse representation methods. The experimental results show that the proposed method significantly enhances the power of discrimination of aurora features, and consequently improve the accuracy and effectiveness of the classification of aurora images.