Hyperspectral band selection based on consistency-measure of neighborhood rough set theory

文献类型： 外文期刊

第一作者： Liu, Yao

作者： Liu, Yao;Xie, Hong;Wang, Liguo;Liu, Yao;Tan, Kezhu;Chen, Yuehua;Xu, Zhen

作者机构：

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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