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Feature extraction plays an essential role in Hyperspectral image classification. Linear discriminant analysis (LDA) is a commonly used feature extraction (FE) method to resolve the Hughes phenomenon for classification. The Hughes phenomenon (also called the curse of dimensionality) is often encountered in classification when the dimensionality of the space grows and the size of the training set is...
Linear discriminant analysis (LDA) is a commonly used feature extraction (FE) method to resolve the Hughes phenomenon for classification. The Hughes phenomenon (also called the curse of dimensionality) is often encountered in classification when the dimensionality of the space grows and the size of the training set is fixed, especially in the small sampling size problem. Recent studies show that the...
In this study, a novel classifier ensemble method named adaptive feature extraction (AdaFE) with reject region is proposed for hyperspectral image. This new concept is deduced from the concepts of reject region and feature extraction. The main idea is adaptive in the sense that subsequent feature spaces are tweaked in favor of those reject regions by Gaussian or knn classifiers in the previous feature...
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