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Embedding Spatial Context into Spectral Angle Based Nonlinear Mapping for Hyperspectral Image Analysis

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Book cover Computer Vision and Graphics (ICCVG 2018)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11114))

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Abstract

Due to the high dimensionality and redundancy of hyperspectral images, an important step in analyzing such images is to reduce the dimensionality. In this paper, we propose and study the dimensionality reduction technique, which is based on the approximation of spectral angle mapper (SAM) measures by Euclidean distances. The key feature of the proposed method is the integration of spatial information into the dissimilarity measure. The experiments performed on the open hyperspectral datasets showed that the developed method can be used in the analysis of hyperspectral images.

The reported study was funded by RFBR according to the research project no. 18-07-01312-a.

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Author information

Authors and Affiliations

  1. Samara University, Moskovskoe Shosse 34A, Samara, 443086, Russia

    Evgeny Myasnikov

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  1. Evgeny Myasnikov
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Correspondence to Evgeny Myasnikov .

Editor information

Editors and Affiliations

  1. Faculty of Applied Informatics and Mathematics, Warsaw University of Life Sciences, Warsaw, Poland

    Leszek J. Chmielewski

  2. Faculty of Applied Informatics and Mathematics, Warsaw University of Life Sciences, Warsaw, Poland

    Ryszard Kozera

  3. Faculty of Applied Informatics and Mathematics, Warsaw University of Life Sciences, Warsaw, Poland

    Arkadiusz Orłowski

  4. Institute of Computer Science, Silesian University of Technology, Gliwice, Poland

    Konrad Wojciechowski

  5. Technion, Israel Institute of Technology, Haifa, Israel

    Alfred M. Bruckstein

  6. University of Groningen, Groningen, The Netherlands

    Nicolai Petkov

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Myasnikov, E. (2018). Embedding Spatial Context into Spectral Angle Based Nonlinear Mapping for Hyperspectral Image Analysis. In: Chmielewski, L., Kozera, R., Orłowski, A., Wojciechowski, K., Bruckstein, A., Petkov, N. (eds) Computer Vision and Graphics. ICCVG 2018. Lecture Notes in Computer Science(), vol 11114. Springer, Cham. https://doi.org/10.1007/978-3-030-00692-1_23

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  • DOI: https://doi.org/10.1007/978-3-030-00692-1_23

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-00691-4

  • Online ISBN: 978-3-030-00692-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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