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Comparing different thresholding algorithms for segmenting auroras

机译:比较用于分割极光的不同阈值算法

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摘要

Extracting aurora oval boundary from spacecraft UV imagery is not a trivial problem. The distinction between aurora and background varies depending on the factors such as the date, time of the day, and satellite position. Thresholding technique is a well-known technique for detecting aurora boundary from satellite imagery. In this study, three distinct thresholding algorithms, mixture modeling, fuzzy sets and entropy thresholding were applied to a selected set of UV images measured on board Polar satellite to examine their effectiveness in aurora boundary detection. Two thresholding approaches were taken: global thresholding and adaptive thresholding. As expected, adaptive thresholding approach showed better results. In addition to these algorithms, another new algorithm (edge-based) was examined using adaptive approach. This thresholding algorithm detects aurora oval by identifying the boundary transition between aurora and background. The results from these different algorithms are presented.
机译:从航天器的紫外图像中提取极光椭圆形边界并不是一个小问题。极光和背景之间的区别因日期,一天中的时间和卫星位置等因素而异。阈值技术是一种用于从卫星图像中检测极光边界的众所周知的技术。在这项研究中,将三种不同的阈值算法,混合模型,模糊集和熵阈值应用于在Polar卫星上测量的一组选定的UV图像,以检查其在极光边界检测中的有效性。采取了两种阈值方法:全局阈值和自适应阈值。不出所料,自适应阈值方法显示了更好的结果。除了这些算法之外,还使用自适应方法研究了另一种新算法(基于边缘的算法)。该阈值算法通过识别极光和背景之间的边界过渡来检测极光椭圆。给出了这些不同算法的结果。

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