论文标题
一种新型的过滤方法，用于使用归一化信息和支持向量机对高光谱远程感知的图像进行分类和分类
A Novel Filter Approach for Band Selection and Classification of Hyperspectral Remotely Sensed Images Using Normalized Mutual Information and Support Vector Machines
论文作者
论文摘要
在高光谱远程感知的图像HSI的分类中，乐队选择是一项艰巨的任务。这是由于其高光谱分辨率，许多类输出和有限数量的训练样本所致。为此，本文介绍了一种新的过滤方法，用于使用信息理论（归一化相互信息）和支持向量机SVM缩小尺寸和分类高光谱图像。此方法包括从输入数据集中选择最有用和相关的频段的最小子集，以提高分类效率。我们将提议的算法应用于由NASA的Aviris传感器在印第安纳州和美国萨利纳斯山谷（Salinas Valley）收集的两个著名基准数据集上。根据在该领域广泛使用的不同评估指标评估实验结果。与最新方法的比较证明，我们的方法可以在良好的时机中减少选定的频段，从而产生良好的性能。 关键字：缩小尺寸，高光谱图像，频段选择，归一化信息，分类，支持向量机器
Band selection is a great challenging task in the classification of hyperspectral remotely sensed images HSI. This is resulting from its high spectral resolution, the many class outputs and the limited number of training samples. For this purpose, this paper introduces a new filter approach for dimension reduction and classification of hyperspectral images using information theoretic (normalized mutual information) and support vector machines SVM. This method consists to select a minimal subset of the most informative and relevant bands from the input datasets for better classification efficiency. We applied our proposed algorithm on two well-known benchmark datasets gathered by the NASA's AVIRIS sensor over Indiana and Salinas valley in USA. The experimental results were assessed based on different evaluation metrics widely used in this area. The comparison with the state of the art methods proves that our method could produce good performance with reduced number of selected bands in a good timing. Keywords: Dimension reduction, Hyperspectral images, Band selection, Normalized mutual information, Classification, Support vector machines
