Based on the fuzzy local information c-means （FLICM） clustering algorithm, a new method is developed for extracting the equatorward and poleward boundaries of the auroral oval from images acquired by the Ultraviolet...Based on the fuzzy local information c-means （FLICM） clustering algorithm, a new method is developed for extracting the equatorward and poleward boundaries of the auroral oval from images acquired by the Ultraviolet Imager （UVI） aboard the POLAR satellite. First, the method iteratively segments the UVI image with the FLICM clustering algorithm, according to an integrity criterion for the segmented auroral oval. Then, possible gaps in the extracted auroral oval are filled, based on prior knowledge of its shape. To evaluate the method objectively, the extracted boundaries are compared with the precipitating electron boundaries determined from DMSP satellite precipitation particle data. The evaluation results demonstrate that the proposed method generates more accurate auroral boundaries than traditional methods.展开更多
基金supported by the National Natural Science Foundation of China (Grant nos.60872154,41031064,40904041,40974103)the National High Technology Research and Development Program of China (Grant no.2008AA121703)the Ocean Public Welfare Scientific Research Project, State Oceanic Administration of China (Grant no.201005017)
文摘Based on the fuzzy local information c-means （FLICM） clustering algorithm, a new method is developed for extracting the equatorward and poleward boundaries of the auroral oval from images acquired by the Ultraviolet Imager （UVI） aboard the POLAR satellite. First, the method iteratively segments the UVI image with the FLICM clustering algorithm, according to an integrity criterion for the segmented auroral oval. Then, possible gaps in the extracted auroral oval are filled, based on prior knowledge of its shape. To evaluate the method objectively, the extracted boundaries are compared with the precipitating electron boundaries determined from DMSP satellite precipitation particle data. The evaluation results demonstrate that the proposed method generates more accurate auroral boundaries than traditional methods.
