论文标题
基于归一化信息的新包装方法，用于降低尺寸和高光谱图像的分类
New wrapper method based on normalized mutual information for dimension reduction and classification of hyperspectral images
论文作者
论文摘要
特征选择是高光谱图像分类中最重要的问题之一。它包括从整个输入数据集中选择最有用的频段并丢弃嘈杂，多余和无关紧要的频段。在这种情况下，我们建议使用支持向量机（SVM）基于标准化的互信息（NMI）和错误概率（PE）提出一种新的包装器方法，以降低使用的高光谱图像的维度并提高分类效率。该实验已在由NASA的空降可见/红外成像光谱仪传感器（AVIRIS）捕获的两个具有挑战性的高光谱基准数据集上进行。已经计算了几个指标来评估所提出的算法的性能。获得的结果证明，与其他再现算法相比，我们的方法可以提高分类性能并提供准确的主题图。可以提高此方法以提高分类效率。关键字 - 功能选择，高光谱图像，分类，包装器，归一化互信息，支持向量机。
Feature selection is one of the most important problems in hyperspectral images classification. It consists to choose the most informative bands from the entire set of input datasets and discard the noisy, redundant and irrelevant ones. In this context, we propose a new wrapper method based on normalized mutual information (NMI) and error probability (PE) using support vector machine (SVM) to reduce the dimensionality of the used hyperspectral images and increase the classification efficiency. The experiments have been performed on two challenging hyperspectral benchmarks datasets captured by the NASA's Airborne Visible/Infrared Imaging Spectrometer Sensor (AVIRIS). Several metrics had been calculated to evaluate the performance of the proposed algorithm. The obtained results prove that our method can increase the classification performance and provide an accurate thematic map in comparison with other reproduced algorithms. This method may be improved for more classification efficiency. Keywords-Feature selection, hyperspectral images, classification, wrapper, normalized mutual information, support vector machine.
