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Natural Numerical Networks on Directed Graphs in Satellite Image Classification

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Scale Space and Variational Methods in Computer Vision (SSVM 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14009))

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Abstract

Natural numerical networks on directed graphs as a new supervised deep learning PDE-based classification algorithm are proposed in this work. The Natural numerical network (NatNet) is based on a forward-backward diffusion model, where the points of the given clusters are attracted together by the forward diffusion, and in contrast, the backward diffusion repulses points of different clusters from each other. First, the network is trained on the labelled data to achieve the highest possible accuracy on the learning dataset. Then, the method is applied to the classification of Sentinel-2 satellite optical data to automatically identify the protected oak habitat in Western Slovakia due to its threatened status. To that goal, the relevancy map, one of the outputs of the Natural numerical network, is created efficiently; its construction is significantly speed up thanks to the new NatNet formulation on directed graphs.

Supported by grants APVV-19-0460, VEGA 1/0436/20 and ESA contract 4000140486/23/NL/SC/rp.

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Authors and Affiliations

  1. Department of Mathematics, Slovak University of Technology in Bratislava, Radlinskeho 11, Bratislava, 810 05, Slovakia

    Karol Mikula, Michal Kollár & Aneta A. Ožvat

  2. Algoritmy:SK s.r.o., Šulekova 6, 81106, Bratislava, Slovakia

    Karol Mikula, Michal Kollár & Aneta A. Ožvat

  3. Plant Science and Biodiversity Center, Slovak Academy of Sciences, Institute of Botany, Dubravska cesta 9, Bratislava, 845 23, Slovakia

    Mária Šibíková & Lucia Čahojová

Authors
  1. Karol Mikula
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  2. Michal Kollár
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  3. Aneta A. Ožvat
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  4. Mária Šibíková
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  5. Lucia Čahojová
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Corresponding author

Correspondence to Aneta A. Ožvat .

Editor information

Editors and Affiliations

  1. CNRS, Université Côte d'Azur, Sophia-Antipolis, France

    Luca Calatroni

  2. University of Insubria, Como, Italy

    Marco Donatelli

  3. University of Bologna, Bologna, Italy

    Serena Morigi

  4. University of Modena and Reggio Emilia, Modena, Italy

    Marco Prato

  5. University of Genova, Genova, Italy

    Matteo Santacesaria

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Mikula, K., Kollár, M., Ožvat, A.A., Šibíková, M., Čahojová, L. (2023). Natural Numerical Networks on Directed Graphs in Satellite Image Classification. In: Calatroni, L., Donatelli, M., Morigi, S., Prato, M., Santacesaria, M. (eds) Scale Space and Variational Methods in Computer Vision. SSVM 2023. Lecture Notes in Computer Science, vol 14009. Springer, Cham. https://doi.org/10.1007/978-3-031-31975-4_26

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  • DOI: https://doi.org/10.1007/978-3-031-31975-4_26

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-31974-7

  • Online ISBN: 978-3-031-31975-4

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