Spatial texture based automatic classification of dayside aurora in all-sky images
Spatial texture based automatic classification of dayside aurora in all-sky images
复制标题
基于空间纹理的全天空影像日间极光自动分类
DOI：
10.1016/j.jastp.2010.01.011
复制
发表时间：
2010-04-01
影响因子：
1.9
通讯作者：
Yang, Huigen
中科院分区：
文献类型：
--
作者：
Wang, Qian;Liang, Jimin;Yang, Huigen
采用一种基于空间纹理的极光图像表示方法，综合了强度、形状和纹理特征，对全天极光图像进行了表征。局部二进制模式（LBP）算子和精心设计的块划分方案的组合实现了全局形状和局部纹理的能力。表示方法被用于自动识别的四个主要类别的离散昼侧极光使用2003年至2009年之间的观测在黄河站，新奥勒松，斯瓦尔巴特群岛。2003年对已标注数据的监督分类结果与科学家们同时考虑光谱和形态信息的标注结果一致。通过对2004-2009年的数据进行自动分类，得到了这4类极光的出现分布，证实了昼侧极光的多波长强度分布，并进一步提供了极光类型的形态学解释。(C)2010爱思唯尔有限公司版权所有。
A spatial texture based representation method including features of intensity, shape and texture, was utilized to characterize all-sky auroral images. The combination of the local binary pattern (LBP) operator and a delicately designed block partition scheme achieved both global shapes and local textures capabilities. The representation method was used in automatic recognition of four primary categories of discrete dayside aurora using observations between years 2003-2009 at the Yellow River Station, Ny-Alesund, Svalbard. The supervised classification results on labeled data in 2003 were in accordance with the labeling by scientists considering both spectral and morphological information. The occurrence distributions of the four categories were obtained through automatic classification of data between 2004-2009, which confirm the multiple-wavelength intensity distribution of dayside aurora, and further provide morphological interpretation of auroral types. (C) 2010 Elsevier Ltd. All rights reserved.