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• W2155131749 abstract "Finding a subspace which consists of the most informative features for reliable hyperspectral image classification is a challenging task. Feature reduction is often achieved via feature selection and feature extraction techniques. In this letter, a hybrid approach which combines both treatments is proposed. Principal Component Analysis (PCA) is applied as a preprocessing step so that each of the new features is generated from the complete set of the original spectral bands. Feature selection is then performed effectively using a normalized Mutual Information (nMI) measure with two constraints to maximize general relevance and minimize redundancy in the selected subspace. The proposed algorithm (PCA-nMI) is tested on hyperspectral images and the experimental results show that the modifications give significant improvement in terms of classification accuracy." @default.
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• W2155131749 date "2014-02-01" @default.
• W2155131749 modified "2023-09-25" @default.
• W2155131749 title "Subspace Detection Using a Mutual Information Measure for Hyperspectral Image Classification" @default.
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• W2155131749 doi "https://doi.org/10.1109/lgrs.2013.2264471" @default.
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