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International Conference on Multimedia Modeling

MMM 2021: MultiMedia Modeling pp 307–317Cite as

Canopy Height Estimation from Spaceborne Imagery Using Convolutional Encoder-Decoder

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Part of the Lecture Notes in Computer Science book series (LNISA,volume 12573)

Abstract

The recent advances in multimedia modeling with deep learning methods have significantly affected remote sensing applications, such as canopy height mapping. Estimating canopy height maps in large-scale is an important step towards sustainable ecosystem management. Apart from the standard height estimation method using LiDAR data, other airborne measurement techniques, such as very high-resolution passive airborne imaging, have also shown to provide accurate estimations. However, those methods suffer from high cost and cannot be used at large-scale nor frequently. In our study, we adopt a neural network architecture to estimate pixel-wise canopy height from cost-effective spaceborne imagery. A deep convolutional encoder-decoder network, based on the SegNet architecture together with skip connections, is trained to embed the multi-spectral pixels of a Sentinel-2 input image to height values via end-to-end learned texture features. Experimental results in a study area of 942 \(\mathrm{km}^2\) yield similar or better estimation accuracy resolution in comparison with a method based on costly airborne images as well as with another state-of-the-art deep learning approach based on spaceborne images.

Keywords

  • Canopy height estimation
  • Deep learning
  • Convolutional encoder-decoder
  • Sentinel-2

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  • DOI: 10.1007/978-3-030-67835-7_26
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Notes

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    For example, Samaria’s Data Cube is a tool for satellite data users to monitor and evaluate land resources and land change. http://datacube.iti.gr/.

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Acknowledgements

This study has been partially funded and supported by the European Union’s Horizon 2020 innovation program under Grant Agreement No. 820852, e-shape (https://e-shape.eu/). LIDAR data was granted by the Cross-border cooperation programme Czech Republic–Bavaria Free State ETC goal 2014–2020, the Interreg V project No. 99 “Přeshranični mapovani lesnich ekosystemů – cesta ke společnemu managementu NP Šumava a NP Bavorsky les /Grenzuberschreitende Kartierung der Waldokosysteme – Weg zum gemeinsamen Management in NP Sumava und NP Bayerischen Wald”. We acknowledge the support of the “Data Pool Initiative for the Bohemian Forest Ecosystem” data-sharing initiative of the Bavarian Forest National Park.

Author information

Authors and Affiliations

  1. Multimedia Understanding Group, Electrical and Computer Engineering Department, Aristotle University of Thessaloniki, 54124, Thessaloniki, Greece

    Leonidas Alagialoglou & Anastasios Delopoulos

  2. Information Technologies Institute, Centre for Research and Technology Hellas (CERTH), Charilaou-Thermi Rd. 6th km, 57001, Thessaloniki, Greece

    Ioannis Manakos

  3. Department of Nature Protection and Research, Bavarian Forest National Park, Freyunger Str. 2, 94481, Grafenau, Germany

    Marco Heurich

  4. Šumava National Park, Sušická 339, 34192, Kašperské Hory, Czech Republic

    Jaroslav Červenka

Authors
  1. Leonidas Alagialoglou
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  2. Ioannis Manakos
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  3. Marco Heurich
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  4. Jaroslav Červenka
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  5. Anastasios Delopoulos
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Corresponding author

Correspondence to Leonidas Alagialoglou .

Editor information

Editors and Affiliations

  1. Charles University, Prague, Czech Republic

    Jakub Lokoč

  2. Charles University, Prague, Czech Republic

    Prof. Tomáš Skopal

  3. Klagenfurt University, Klagenfurt, Austria

    Prof. Dr. Klaus Schoeffmann

  4. CERTH-ITI, Thessaloniki, Greece

    Vasileios Mezaris

  5. Renmin University of China, Beijing, China

    Dr. Xirong Li

  6. CERTH-ITI, Thessaloniki, Greece

    Dr. Stefanos Vrochidis

  7. Queen Mary University of London, London, UK

    Dr. Ioannis Patras

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Alagialoglou, L., Manakos, I., Heurich, M., Červenka, J., Delopoulos, A. (2021). Canopy Height Estimation from Spaceborne Imagery Using Convolutional Encoder-Decoder. In: , et al. MultiMedia Modeling. MMM 2021. Lecture Notes in Computer Science(), vol 12573. Springer, Cham. https://doi.org/10.1007/978-3-030-67835-7_26

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  • DOI: https://doi.org/10.1007/978-3-030-67835-7_26

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