论文标题
基于归一化信息的算法和启发式算法，用于降低维度和分类高光谱图像
An Algorithm and Heuristic based on Normalized Mutual Information for Dimensionality Reduction and Classification of Hyperspectral images
论文作者
论文摘要
在特征分类域中，数据的选择会广泛影响结果。高光谱图像（HSI）是同一区域的一组超过一百多个双向度量（称为频段）（称为地面真相图：GT）。 HSI是在一组n矢量上建模的。因此，我们有n个特征（或属性）表达C物质测量值的n个向量（称为类）。有问题的是，投资所有可能的子集在原则上是不可能的。因此，我们必须在n之间找到k矢量，例如相关和无冗余。为了对物质进行分类。在这里，我们介绍了一种基于归一化信息的算法，以选择相关和无冗余频带，这是提高HSI分类精度所必需的。 关键字：特征选择，归一化互信息，高光谱图像，分类，冗余。
In the feature classification domain, the choice of data affects widely the results. The Hyperspectral image (HSI), is a set of more than a hundred bidirectional measures (called bands), of the same region (called ground truth map: GT). The HSI is modelized at a set of N vectors. So we have N features (or attributes) expressing N vectors of measures for C substances (called classes). The problematic is that it's pratically impossible to investgate all possible subsets. So we must find K vectors among N, such as relevant and no redundant ones; in order to classify substances. Here we introduce an algorithm based on Normalized Mutual Information to select relevant and no redundant bands, necessary to increase classification accuracy of HSI. Keywords: Feature Selection, Normalized Mutual information, Hyperspectral images, Classification, Redundancy.
