Auroral arc is the brightest and most obvious kind of aurora. Detecting auroral arc contour from all-sky imagery (ASI) images is not a trivial problem. The location, shape, and intensity of the arc vary depending on the factors such as the date, time of the day, etc; in addition, the intensity of both the auroral arc itself and the background area differs a lot in an image, which make the segmentation of auroral arc difficult. In this study, three image segmenting algorithms, including adaptive fuzzy thresholding, integrating spatial fuzzy clustering with level set methods, and region growing are applied to a selected set of ASI arc images to examine their effectiveness in auroral arc boundary detection. The results from the three techniques are presented, and the merits and demerits of each method are discussed.
['Qiuju Yang']
Comparison of Three Segmentation Techniques for Auroral Arc Detection