Hyperspectral band selection based on consistency-measure of neighborhood rough set theory
文献类型： 外文期刊
作者： Liu, Yao 1 ; Xie, Hong 1 ; Tan, Kezhu 2 ; Chen, Yuehua 2 ; Xu, Zhen 3 ; Wang, Liguo 1 ;
作者机构： 1.Harbin Engn Univ, Coll Informat & Commun Engn, Harbin 150001, Peoples R China
2.Northeast Agr Univ, Coll Elect & Informat, Harbin 150030, Peoples R China
3.Heilongjiang Acad Agr Sci, Harbin 150086, Peoples R China
关键词： hyperspectral imaging;consistency-measure;neighborhood rough set;band selection
期刊名称：MEASUREMENT SCIENCE AND TECHNOLOGY （ 影响因子：2.046； 五年影响因子：2.11 ）
ISSN： 0957-0233
年卷期： 2016 年 27 卷 5 期
页码：
收录情况： SCI
摘要： Band selection is a well-known approach for reducing dimensionality in hyperspectral imaging. In this paper, a band selection method based on consistency-measure of neighborhood rough set theory (CMNRS) was proposed to select informative bands from hyperspectral images. A decision-making information system was established by the reflection spectrum of soybeans' hyperspectral data between 400 nm and 1000 nm wavelengths. The neighborhood consistency-measure, which reflects not only the size of the decision positive region, but also the sample distribution in the boundary region, was used as the evaluation function of band significance. The optimal band subset was selected by a forward greedy search algorithm. A post-pruning strategy was employed to overcome the over-fitting problem and find the minimum subset. To assess the effectiveness of the proposed band selection technique, two classification models (extreme learning machine (ELM) and random forests (RF)) were built. The experimental results showed that the proposed algorithm can effectively select key bands and obtain satisfactory classification accuracy.
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