Owing to the highly dimensional nature of hyperspectral imaging datasets, waveband selection has become an important step in processing. In this work, we propose a novel weighted maximum relevance minimum redundancy waveband selection algorithm. The relative importance between redundancy and relevance is better balanced by introducing a weight coefficient. The mutual information between wavebands and target classes and wavebands based on the neighbourhood rough set theory were calculated using the proposed algorithm. Using the forward greedy search algorithm, the wavebands with maximum relevance to target classes and minimum redundancy to previously selected wavebands were selected. In the classification of soybean hyperspectral imaging datasets, weighted maximum relevance minimum redundancy algorithms with an equal weight and an unequal weight both performed well in terms of classification accuracy. The classification performances of the extreme learning machine classifiers are...

中文翻译：

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基于加权最大相关最小冗余的高光谱波段选择算法及其稳定性分析

由于高光谱成像数据集的高度维度性质，波段选择已成为处理中的重要步骤。在这项工作中，我们提出了一种新颖的加权最大相关最小冗余频带选择算法。通过引入权重系数，可以更好地平衡冗余和相关性之间的相对重要性。利用所提出的算法，基于邻域粗糙集理论，计算了波段与目标类别和波段之间的互信息。使用前向贪婪搜索算法，选择与目标类别最大相关，对先前选择的波段具有最小冗余的波段。在大豆高光谱成像数据集的分类中，权重相等和权重不相等的加权最大相关最小冗余算法在分类精度方面均表现良好。极限学习机分类器的分类性能为...