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Quantization of Hyperspectral Image Manifold Using Probabilistic Distances

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Geometric Science of Information (GSI 2015)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9389))

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Abstract

A technique of spatial-spectral quantization of hyperspectral images is introduced. Thus a quantized hyperspectral image is just summarized by K spectra which represent the spatial and spectral structures of the image. The proposed technique is based on \(\alpha \)-connected components on a region adjacency graph. The main ingredient is a dissimilarity metric. In order to choose the metric that best fit the hyperspectral data manifold, a comparison of different probabilistic dissimilarity measures is achieved.

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Author information

Authors and Affiliations

  1. MINES ParisTech, CMM-Centre de Morphologie Mathématique, PSL-Research University, Paris, France

    Gianni Franchi & Jesús Angulo

Authors
  1. Gianni Franchi
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  2. Jesús Angulo
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Corresponding author

Correspondence to Jesús Angulo .

Editor information

Editors and Affiliations

  1. Bâtiment Alan Turing, CS35003, École Polytechnique, Palaiseau, France

    Frank Nielsen

  2. Thales Land\& Air Systems, Limours, France

    Frédéric Barbaresco

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Franchi, G., Angulo, J. (2015). Quantization of Hyperspectral Image Manifold Using Probabilistic Distances. In: Nielsen, F., Barbaresco, F. (eds) Geometric Science of Information. GSI 2015. Lecture Notes in Computer Science(), vol 9389. Springer, Cham. https://doi.org/10.1007/978-3-319-25040-3_44

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  • DOI: https://doi.org/10.1007/978-3-319-25040-3_44

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-25039-7

  • Online ISBN: 978-3-319-25040-3

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