论文标题
高光谱图像分类和使用同质性特征和相互信息的降低尺寸降低
Hyperspectral images classification and Dimensionality Reduction using Homogeneity feature and mutual information
论文作者
论文摘要
高光谱图像（HSI）包含数百个称为地面真相（GT）的区域的频段。频段是并并列频率的，但其中一些是嘈杂的测量或不包含信息的。对于分类，频段的选择显着影响分类结果，实际上，使用相关频带的子集，这些结果可以比使用所有频段获得的结果更好，从而减少HSI的维度。在本文中，根据生成过程进行了降低方法的分类。此外，我们基于共同信息（MI）复制了一种算法，以通过特征选择来降低维度，并使用相互信息和同质性引入算法。这两个模式是过滤策略。最后，为了验证这一点，我们考虑了案例研究Aviris HSI 92AV3C。 关键字：高光谱图像；分类;特征选择；相互信息；同质性
The Hyperspectral image (HSI) contains several hundred bands of the same region called the Ground Truth (GT). The bands are taken in juxtaposed frequencies, but some of them are noisily measured or contain no information. For the classification, the selection of bands, affects significantly the results of classification, in fact, using a subset of relevant bands, these results can be better than those obtained using all bands, from which the need to reduce the dimensionality of the HSI. In this paper, a categorization of dimensionality reduction methods, according to the generation process, is presented. Furthermore, we reproduce an algorithm based on mutual information (MI) to reduce dimensionality by features selection and we introduce an algorithm using mutual information and homogeneity. The two schemas are a filter strategy. Finally, to validate this, we consider the case study AVIRIS HSI 92AV3C. Keywords: Hyperspectrale images; classification; features selection; mutual information; homogeneity
