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• W2617168527 abstract "ABSTRACTA novel fusion-classification system is proposed for hyperspectral image classification. Firstly, spectral derivatives are used to capture salient spectral features for different land-cover classes and a Gabor filter is applied to extract useful spatial features at neighbouring locations. Then, two locality-preserving dimensionality reduction methods are employed to reduce the dimensionality of data and preserve the local structure of neighbouring samples in the original image, derivative-feature and Gabor-feature domains. Finally, the classification results from Gaussian-mixture-model classifiers are fused by a decision-fusion approach. We have compared the proposed system to several traditional and state-of-the-art methods on two benchmark classification data sets. In both cases, our system achieved improved accuracy than the current best performing methods. Especially in the case of classification for the Indian Pines data set, the proposed DG-locality-preserving nonnegative matrix factorizatio..." @default.
• W2617168527 created "2017-06-05" @default.
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• W2617168527 date "2017-01-01" @default.
• W2617168527 modified "2023-10-12" @default.
• W2617168527 title "Decision fusion for hyperspectral image classification based on multiple features and locality-preserving analysis" @default.
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• W2617168527 doi "https://doi.org/10.1080/22797254.2017.1299556" @default.
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