Maximum relevance, minimum redundancy band selection based on neighborhood rough set for hyperspectral data classification

文献类型： 外文期刊

第一作者： Liu, Yao

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

作者机构：

关键词： hyperspectral imaging;rough set;band selection;maximal relevance;minimal redundancy

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 12 期

页码：

收录情况： SCI

摘要： Band selection is considered to be an important processing step in handling hyperspectral data. In this work, we selected informative bands according to the maximal relevance minimal redundancy (MRMR) criterion based on neighborhood mutual information. Two measures MRMR difference and MRMR quotient were defined and a forward greedy search for band selection was constructed. The performance of the proposed algorithm, along with a comparison with other methods (neighborhood dependency measure based algorithm, genetic algorithm and uninformative variable elimination algorithm), was studied using the classification accuracy of extreme learning machine (ELM) and random forests (RF) classifiers on soybeans' hyperspectral datasets. The results show that the proposed MRMR algorithm leads to promising improvement in band selection and classification accuracy.

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