Automatic Classification of Ultraviolet Aurora Images Based on Texture and Shape Features

Abstract

Aurora is the typical ionosphere track generated by the interaction of solar wind and magnetosphere, and its detection is significant to study of space weather activity. Space-borne ultraviolet detectors, especially far ultraviolet band image detecting device, provide abundant detecting data. Based on the special morphology of ultraviolet aurora images, the combination of texture and shape features is utilized to extract the features of ultraviolet aurora images, and then a support vector machine (SVM) is employed to classify the auroras. The experiment based on ultraviolet aurora image data obtained by the Polar satellite shows the feasibility and effectiveness of our feature representation method.