Segmentation of tree seedling point clouds into elementary units

This article describes a new semi-automatic method to cluster terrestrial laser scanning (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 conn...

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Bibliographic Details
Published in:International journal of remote sensing 2016-07, Vol.37 (13), p.2881-2907
Main Authors: Hétroy-Wheeler, Franck, Casella, Eric, Boltcheva, Dobrina
Format: Article
Language:English
Subjects:
Computer Science
Computer Vision and Pattern Recognition
Environmental Sciences
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Summary:This article describes a new semi-automatic method to cluster terrestrial laser scanning (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 (LA) estimates, is provided on a poplar seedling mock-up. It shows that the segmentation is robust with false-positive and false-negative rates of around 1%. Qualitative results on four contrasting plant species with three different scan resolution levels each are also shown.
ISSN:0143-1161
1366-5901
DOI:10.1080/01431161.2016.1190988