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Techniques for Hyperspectral Images",
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@inproceedings{bb150153,
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Hyperspectral Image Classification",
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@inproceedings{bb150155,
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Hyperspectral Data: A Neural Network Based Approach",
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Classification of Hyperspectral Images",
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@inproceedings{bb150157,
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Classification",
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        PAGES = "1918-1923",
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@inproceedings{bb150158,
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@inproceedings{bb150159,
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Hyperspectral Image Classification",
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@inproceedings{bb150160,
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@inproceedings{bb150161,
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hyperspectral images",
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@inproceedings{bb150162,
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@inproceedings{bb150163,
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Measuring Spectra, One Sinusoid at a Time",
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@inproceedings{bb150164,
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@inproceedings{bb150165,
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@inproceedings{bb150166,
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for Hyperspectral Image Analysis",
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@inproceedings{bb150167,
        AUTHOR = "Myasnikov, E.",
        TITLE = "Exploiting Spatial Context in Nonlinear Mapping of Hyperspectral Image
Data",
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@inproceedings{bb150168,
        AUTHOR = "Myasnikov, E.",
        TITLE = "Nonlinear Mapping Based on Spectral Angle Preserving Principle for
Hyperspectral Image Analysis",
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@inproceedings{bb150169,
        AUTHOR = "Myasnikov, E.",
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@inproceedings{bb150170,
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of Hyperspectral Data",
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using Homogeneity feature and mutual information",
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A Semi-supervised Subspace Learning Algorithm",
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@article{bb150196,
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@article{bb150197,
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@article{bb150198,
        AUTHOR = "Liu, K.H. and Chen, S.Y. and Chien, H.C. and Lu, M.H.",
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Image Transmission",
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        YEAR = "2018",
        NUMBER = "3",
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@article{bb150199,
        AUTHOR = "Martinez Sotoca, J. and Pla, F. and Salvador Sanchez, J.",
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