论文标题
一个基于三个变量的互助信息的新颖过滤器，用于降低维度的高光谱图像
A novel filter based on three variables mutual information for dimensionality reduction and classification of hyperspectral images
论文作者
论文摘要
高光谱图像（HSI）的高维度（HSI）包含数百多个频段（图像），该区域称为地面真相图，通常会为图像处理带来沉重的计算负担，并使学习过程变得复杂。实际上，去除无关，嘈杂和冗余带有助于提高分类精度。基于“共同信息”的频带选择过滤器是降低维度的常见技术。在本文中，介绍了根据评估过程的降低方法的分类。此外，开发了一种基于三个变量的新滤波器方法，以测量分类的频段相关性，不仅考虑频段相关性，还考虑频段相互作用。将所提出的方法与基于相互信息的再现过滤算法进行比较。 HSI AVIRIS 92AV3C的实验结果表明，所提出的方法非常有竞争力，有效且优于再现的滤波器策略性能。 关键字 - 高光谱图像，分类，频段选择，三个变量共同信息，信息增益。
The high dimensionality of hyperspectral images (HSI) that contains more than hundred bands (images) for the same region called Ground Truth Map, often imposes a heavy computational burden for image processing and complicates the learning process. In fact, the removal of irrelevant, noisy and redundant bands helps increase the classification accuracy. Band selection filter based on "Mutual Information" is a common technique for dimensionality reduction. In this paper, a categorization of dimensionality reduction methods according to the evaluation process is presented. Moreover, a new filter approach based on three variables mutual information is developed in order to measure band correlation for classification, it considers not only bands relevance but also bands interaction. The proposed approach is compared to a reproduced filter algorithm based on mutual information. Experimental results on HSI AVIRIS 92AV3C have shown that the proposed approach is very competitive, effective and outperforms the reproduced filter strategy performance. Keywords - Hyperspectral images, Classification, band Selection, Three variables Mutual Information, information gain.
