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In the fields of remote sensing image processing, hyperspectral image (HSI) classification is a hot topic, which involves computer graphics, statistics, matrix theory and other disciplines. In this study, we prefer to use machine learning classification algorithm (supervised learning or unsupervised learning) to predict under the context of HSI classification. Recently, sparse representation based...
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...
In the kernel methods, it is very important to choose a proper kernel function to avoid overlapping data. Based this fact, in this paper we mainly utilize a unified kernel optimization framework on the hyperspectral image classification to augment the margin between different classes, and under the kernel optimization framework, to employ the Fisher discriminant criteria formulated in a pairwise manner...
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