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Multiple support vector machines (SVMs) with random subspaces [1]-[5] have been performing excellently for hyperspectral image classification to reduce the correlation between features and avoid the Hughes phenomena. In most random subspace methods, features were randomly selected without replacement from the original feature set according to uniform distribution [6]. However, in general, SVM with...
For hyperspectral image classification, feature extraction is a crucial pre-process for avoiding the Hughes phenomena. Some feature extraction methods such as linear discriminant analysis (LDA), nonparametric weighted feature extraction (NWFE), and their kernel versions, generalized discriminant analysis (GDA) and kernel nonparametric weighted feature extraction method (KNWFE) have been shown that...
Many research shows that we will encounter the Highes phenomenon when dealing with the high-dimensional data classification problem. In addition, non-linear support vector machine (SVM) has been shown that it can conquer the problem efficiently. However, the SVM is a black-box model based on the whole features and does not provide the feature importance or “good” feature subset for classification...
Most studies showed that most hyperspectral image classification encountered the Hughes phenomenon due to the redundant features, especially in the small sample size problem. Feature extraction method such as linear discriminant analysis (LDA), nonparametric weighted feature extraction (NWFE) is a preprocessing step before classification and used to combine and reduce the original features into a...
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