A novel aurora pattern recognition method using X-gray level aura matrices and support vector machines effectively classified dayside aurora images compared to basic gray level aura matrices.
The proposed X-GLAMs method effectively classifies dayside aurora patterns, which is significant for studying space weather activity.
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. This paper proposes a novel aurora pattern recognition method based on static image classification of day-side aurora. In the feature extraction phase, X-gray level aura matrices (X-GLAMs) are designed to extract the feature of the original aurora images. For classification, models of texture classes are learned using support vector machine (SVM), then a given texture of aurora image can be classified into one of the pre-learned classes. It compares two sets of features: X-GLAMs and basic gray level aura matrices (BGLAMs), both of which are based on different windows on the real aurora image database from Chinese Arctic Yellow River Station. The experimental results illustrate the effectiveness of the proposed dayside aurora classification algorithm.
Wang et al. (Mon,) conducted a other in Aurora pattern recognition. X-gray level aura matrices (X-GLAMs) and SVM vs. Basic gray level aura matrices (BGLAMs) was evaluated on Aurora image classification. A novel aurora pattern recognition method using X-gray level aura matrices and support vector machines effectively classified dayside aurora images compared to basic gray level aura matrices.