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ABSTRACT
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.
Acknowledgements
The authors would like to express their sincere gratitude to the Forestry Commission, the University of Grenoble Alpes and Inria for funding this work, as well as to Rémy Cumont for his participation in coding the segmentation algorithm and Dr Elisa Hétroy-Wheeler for proofreading the article.
Author contributions: E.C. and F.H.W. designed the research; F.H.W. designed and coded the segmentation algorithm; E.C. performed the real data acquisition and coded the hit/no-hit algorithm for point cloud simulations; D.B. filtered the point clouds; F.H.W. and E.C. analysed the results and wrote the article.
Disclosure statement
No potential conflict of interest was reported by the authors.
Supplementary material
Supplemental data for this article can be accessed here.