MIMR-DGSA: Unsupervised hyperspectral band selection based on information theory and a modified discrete gravitational search algorithm
MIMR-DGSA: Unsupervised hyperspectral band selection based on information theory and a modified discrete gravitational search algorithm
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DOI：
10.1016/j.inffus.2019.02.005
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发表时间：
2019-11-01
影响因子：
18.6
通讯作者：
Marshall, Stephen
中科院分区：
文献类型：
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作者：
Tschannerl, Julius;Ren, Jinchang;Marshall, Stephen
波段选择在高光谱数据分析中起着重要作用，因为它可以提高数据分析的性能，而不会丢失有关基础数据构成的信息。我们提出了一种用于频带选择的 MIMR-DGSA 算法，遵循最大信息最小冗余（MIMR）标准，最大化子集的各个特征所携带的信息，并最小化它们之间的冗余信息。子集是使用改进的离散引力搜索算法（DGSA）生成的，其中我们定义了特征子集的邻域概念。还开发了一种用于成对互信息计算的快速算法，该算法结合了高光谱波段的可变带宽，称为 VarBWFastMI。三个高光谱遥感数据集的分类结果表明，所提出的 MIMR-DGSA 的性能与采用克隆选择算法 (CSA) 的原始 MIMR 类似，但计算效率更高且更易于处理，因为它需要调整的参数更少。
Band selection plays an important role in hyperspectral data analysis as it can improve the performance of data analysis without losing information about the constitution of the underlying data. We propose a MIMR-DGSA algorithm for band selection by following the Maximum-Information-Minimum-Redundancy (MIMR) criterion that maximises the information carried by individual features of a subset and minimises redundant information between them. Subsets are generated with a modified Discrete Gravitational Search Algorithm (DGSA) where we definine a neighbourhood concept for feature subsets. A fast algorithm for pairwise mutual information calculation that incorporates variable bandwidths of hyperspectral bands called VarBWFastMI is also developed. Classification results on three hyperspectral remote sensing datasets show that the proposed MIMR-DGSA performs similar to the original MIMR with Clonal Selection Algorithm (CSA) but is computationally more efficient and easier to handle as it has fewer parameters for tuning.