Classification of Tree Species and Standing Dead Trees with Lidar Point Clouds Using Two Deep Neural Networks: PointCNN and 3DmFV-Net View Full Text


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Article Info

DATE

2022-03-30

AUTHORS

Maximilian Hell, Melanie Brandmeier, Sebastian Briechle, Peter Krzystek

ABSTRACT

Knowledge about tree species distribution is important for forest management and for modeling and protecting biodiversity in forests. Methods based on images are inherently limited to the forest canopy. Airborne lidar data provide information about the trees’ geometric structure, as well as trees beneath the upper canopy layer. In this paper, the potential of two deep learning architectures (PointCNN, 3DmFV-Net) for classification of four different tree classes is evaluated using a lidar dataset acquired at the Bavarian Forest National Park (BFNP) in a leaf-on situation with a maximum point density of about 80 pts/m2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$^{2}$$\end{document}. Especially in the case of BFNP, dead wood plays a key role in forest biodiversity. Thus, the presented approaches are applied to the combined classification of living and dead trees. A total of 2721 single trees were delineated in advance using a normalized cut segmentation. The trees were manually labeled into four tree classes (coniferous, deciduous, standing dead tree with crown, and snag). Moreover, a multispectral orthophoto provided additional features, namely the Normalized Difference Vegetation Index. PointCNN with 3D points, laser intensity, and multispectral features resulted in a test accuracy of up to 87.0%. This highlights the potential of deep learning on point clouds in forestry. In contrast, 3DmFV-Net achieved a test accuracy of 73.2% for the same dataset using only the 3D coordinates of the laser points. The results show that the data fusion of lidar and multispectral data is invaluable for differentiation of the tree classes. Classification accuracy increases by up to 16.3% points when adding features generated from the multispectral orthophoto. More... »

PAGES

103-121

References to SciGraph publications

  • 2015-05-27. Deep learning in NATURE
  • 2010. Improving the Fisher Kernel for Large-Scale Image Classification in COMPUTER VISION – ECCV 2010
  • 1998. Efficient BackProp in NEURAL NETWORKS: TRICKS OF THE TRADE
  • 2013-06-12. Image Classification with the Fisher Vector: Theory and Practice in INTERNATIONAL JOURNAL OF COMPUTER VISION
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    URI

    http://scigraph.springernature.com/pub.10.1007/s41064-022-00200-4

    DOI

    http://dx.doi.org/10.1007/s41064-022-00200-4

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    202 rdf:type schema:Organization
    203 grid-institutes:grid.449775.c schema:alternateName Faculty of Plastics Engineering and Surveying, FHWS University of Applied Sciences Würzburg-Schweinfurt, Würzburg, Germany
    204 schema:name Esri Deutschland GmbH, Kranzberg, Germany
    205 Faculty of Plastics Engineering and Surveying, FHWS University of Applied Sciences Würzburg-Schweinfurt, Würzburg, Germany
    206 rdf:type schema:Organization
     




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