论文标题
通过最小化标准化的互信息来选择和分类高光谱图像
Band selection and classification of hyperspectral images by minimizing normalized mutual information
论文作者
论文摘要
高光谱图像（HSI）分类是高技术遥感工具。主要目标是对区域的点进行分类。他的包含一个称为地面真相图（GT）的同一区域的一百多个双向措施，称为带（或简单的图像）。不幸的是，有些频段包含冗余信息，其他频段则受噪声的影响，并且特征的高维度使分类的准确性较低。所有这些频段对于某些应用都可能很重要，但是对于分类，其中一小部分是相关的。在本文中，我们使用共同信息（MI）选择相关的频段；以及归一化信息系数，以避免和控制冗余信息。这是一个功能选择方案和过滤策略。我们在HSI AVIRIS 92AV3C上建立了这项研究。这是有效性，也是控制冗余的快速计划。索引术语：高光谱图像，分类，特征选择，归一化互信息，冗余。
Hyperspectral images (HSI) classification is a high technical remote sensing tool. The main goal is to classify the point of a region. The HIS contains more than a hundred bidirectional measures, called bands (or simply images), of the same region called Ground Truth Map (GT). Unfortunately, some bands contain redundant information, others are affected by the noise, and the high dimensionalities of features make the accuracy of classification lower. All these bands can be important for some applications, but for the classification a small subset of these is relevant. In this paper we use mutual information (MI) to select the relevant bands; and the Normalized Mutual Information coefficient to avoid and control redundant ones. This is a feature selection scheme and a Filter strategy. We establish this study on HSI AVIRIS 92AV3C. This is effectiveness, and fast scheme to control redundancy. Index Terms: Hyperspectral images, Classification, Feature Selection, Normalized Mutual Information, Redundancy.
