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- W4281567285 abstract "Hyperspectral images (HSI) contain a wide range of information, the most prominent technology for observing the earth. However, using an original HSI high-dimensional datacube, the classification task faces significant challenges since it has a high computational cost. As a result, dimensionality reduction is indispensable. A dimension reduction method has been introduced in this paper, including feature extraction and feature selection to obtain feature subsets. Minimum Noise Fraction (MNF) is a popular feature extraction method for HSI, requiring a high computational capability. We propose a segmented MNF that divides the complete HSI into groups utilising normalised cross-cumulative residual entropy (nCCRE). An nCCRE-based feature selection is also employed to improve the quality of the chosen features using the max-relevancy min-redundancy measure. The support vector machine (SVM) classifier is used on two real HSI to evaluate the efficiency of the extracted subsets." @default.
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- W4281567285 date "2022-05-26" @default.
- W4281567285 modified "2023-10-18" @default.
- W4281567285 title "Spectral segmentation based dimension reduction for hyperspectral image classification" @default.
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- W4281567285 doi "https://doi.org/10.1080/14498596.2022.2074902" @default.
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