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• W2890945279 abstract "In this paper, we propose a segmented principal component analysis (SPCA) and Gaussian pyramid decomposition based multiscale feature fusion method for the classification of hyperspectral images. First, considering the band-to-band cross correlations of objects, the SPCA method is utilized for the spectral dimension reduction of the hyperspectral image. Then, the dimension-reduced image is decomposed into several Gaussian pyramids to extract the multiscale features. Next, the SPCA method is performed again to compute the fused SPCA based Gaussian pyramid features (SPCA-GPs). Finally, the performance of the SPCA-GPs is evaluated using the support vector machine classifier. Experiments performed on three widely used hyperspectral images show that the proposed SPCA-GPs method outperforms several compared classification methods in terms of classification accuracies and computational cost." @default.
• W2890945279 created "2018-09-27" @default.
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• W2890945279 date "2018-09-01" @default.
• W2890945279 modified "2023-10-14" @default.
• W2890945279 title "Gaussian Pyramid Based Multiscale Feature Fusion for Hyperspectral Image Classification" @default.
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• W2890945279 doi "https://doi.org/10.1109/jstars.2018.2856741" @default.
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