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Segmentation of tree seedling point clouds into elementary units

Identifieur interne : 000108 ( Main/Exploration ); précédent : 000107; suivant : 000109

Segmentation of tree seedling point clouds into elementary units

Auteurs : Franck Hétroy-Wheeler [France] ; Eric Casella [Royaume-Uni] ; Dobrina Boltcheva [France]

Source :

RBID : Hal:hal-01285419

English descriptors

Abstract

This paper describes a new semi-automatic method to cluster TLS data into meaningful sets of points to extract plant components. The approach is designed for small plants with distinguishable branches and leaves, such as tree seedlings. It first creates a graph by connecting each point to its most relevant neighbours, then embeds the graph into a spectral space, and finally segments the embedding into clusters of points. The process can then be iterated on each cluster separately. The main idea underlying the approach is that the spectral embedding of the graph aligns the points along the shape's principal directions. A quantitative evaluation of the segmentation accuracy, as well as of leaf area estimates, is provided on a poplar seedling mock-up. It shows that the segmentation is robust with false positive and false negative rates around 1%. Qualitative results on four contrasting plant species with three different scan resolution levels each are also shown.

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Le document en format XML

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<country>France</country>
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<settlement type="city">Nancy</settlement>
<settlement type="city">Metz</settlement>
<region type="region" nuts="2">Grand Est</region>
<region type="old region" nuts="2">Lorraine (région)</region>
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<title level="j">International Journal of Remote Sensing</title>
<idno type="ISSN">0143-1161</idno>
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<date type="datePub">2016</date>
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<term> 3D point cloud</term>
<term> segmentation</term>
<term> spectral clustering</term>
<term> tLiDAR</term>
<term> tree seedling</term>
<term>terrestrial laser scanning</term>
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<div type="abstract" xml:lang="en">This paper describes a new semi-automatic method to cluster TLS data into meaningful sets of points to extract plant components. The approach is designed for small plants with distinguishable branches and leaves, such as tree seedlings. It first creates a graph by connecting each point to its most relevant neighbours, then embeds the graph into a spectral space, and finally segments the embedding into clusters of points. The process can then be iterated on each cluster separately. The main idea underlying the approach is that the spectral embedding of the graph aligns the points along the shape's principal directions. A quantitative evaluation of the segmentation accuracy, as well as of leaf area estimates, is provided on a poplar seedling mock-up. It shows that the segmentation is robust with false positive and false negative rates around 1%. Qualitative results on four contrasting plant species with three different scan resolution levels each are also shown.</div>
</front>
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<country>
<li>France</li>
<li>Royaume-Uni</li>
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<li>Auvergne-Rhône-Alpes</li>
<li>Grand Est</li>
<li>Lorraine (région)</li>
<li>Rhône-Alpes</li>
</region>
<settlement>
<li>Grenoble</li>
<li>Metz</li>
<li>Nancy</li>
</settlement>
<orgName>
<li>Université Joseph Fourier</li>
<li>Université Pierre-Mendès-France</li>
<li>Université de Grenoble</li>
<li>Université de Lorraine</li>
</orgName>
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<name sortKey="Hetroy Wheeler, Franck" sort="Hetroy Wheeler, Franck" uniqKey="Hetroy Wheeler F" first="Franck" last="Hétroy-Wheeler">Franck Hétroy-Wheeler</name>
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<country name="Royaume-Uni">
<noRegion>
<name sortKey="Casella, Eric" sort="Casella, Eric" uniqKey="Casella E" first="Eric" last="Casella">Eric Casella</name>
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</country>
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