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An adaptive semantic dimensionality reduction approach for hyperspectral imagery classification

机译：高光谱图像分类的自适应语义降维方法

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Hyperspectral imagery (HSI) is widely used for several fields of remote sensing such as agriculture, land cover monitoring, and deforestation. However, the HSI classification is a challenge task due to the large number of spectral bands, unavailability of training samples, and the high correlation inter-bands. To address these challenges, we propose in this work a semantic reduction dimensionality approach based on the principal component analysis (PCA) and mutual information-based band selection (MI). Firstly, we project the original HSI using PCA to obtain a novel subspace with lower dimensions. Using the obtained components, a set of rules can be generated to find the relevant spectral bands based on score contribution coefficient. Moreover, the mutual information (MI) is used to select the spectral bands that contain a higher information based on the entropy criterion. We propose then to exploit the selected bands for HSI classification using SVM technique. Experiment results demonstrate that our proposed approach is effective and perform for HSI classification compared to other dimensionality reduction approaches.
机译：高光谱影像（HSI）被广泛用于遥感的多个领域，例如农业，土地覆盖监测和森林砍伐。但是，由于频谱带数量众多，训练样本不可用以及相关带间高度相关，HSI分类是一项艰巨的任务。为了解决这些挑战，我们在这项工作中提出了一种基于主成分分析（PCA）和基于互信息的频带选择（MI）的语义缩减维方法。首先，我们使用PCA投影原始的HSI，以获得具有较小尺寸的新颖子空间。使用获得的组件，可以生成一组规则，以基于得分贡献系数找到相关的光谱带。此外，互信息（MI）用于基于熵准则选择包含更高信息的光谱带。然后，我们建议使用SVM技术为HSI分类开发选定的波段。实验结果表明，与其他降维方法相比，我们提出的方法对于HSI分类是有效的。

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