Section: New Results

Spectral Clustering of Plant Units From 3D Point Clouds

• Participant: Dobrina Boltcheva

High-resolution terrestrial Light Detection And Ranging (tLiDAR), a 3-D remote sensing technique, has recently been applied for measuring the 3-D characteristics of vegetation from grass to forest plant species. The resulting data are known as a point cloud which shows the 3-D position of all the hits by the laser beam giving a raw sketch of the spatial distribution of plant elements in 3-D, but without explicit information on their geometry and connectivity. We have developed a new approach based on a delineation algorithm that clusters a point cloud into elementary plant units such as internodes, petioles and leaves. The algorithm creates a graph (points + edges) to recover plausible neighboring relationships between the points and embeds this graph in a spectral space in order to segment the point-cloud into meaningful elementary plant units. This work has been published in the International Journal of Remote Sensing [6].