VoxR (version 1.0.0)

distance_clustering: Clustering of non connected objects in a point cloud.

Description

Clustering objects with non common points: two points located within a user defined distance from each other are considered as the parts of a unique object. This function is well suited to be applied to the outputs of the substract_point_clouds function.

Clustering objects with non common points: two points located within a user defined distance from each other are considered as the parts of a unique object. This function is well suited to be applied to the outputs of the substract_point_clouds function.

Usage

distance_clustering(data, d_clust, method, C_size, message)
distance_clustering(data, d_clust, method, C_size, message)

Arguments

data

a data.frame or data.table containing the x, y, z, ... coordinates of a point cloud or voxel cloud.

d_clust

numeric. The distance required to consider two points as being part of two different clusters. Default = 0.02.

method

character. The algorithm to use for clustering. Can be either "D_mat" or "Iter", see details. Default = "D_mat".

C_size

(optional) numeric. If method = "Iter", sets the maximal size of a cluster (in distance unit).

message

logical. If FALSE, messages are disabled. Default = TRUE.

Value

The input data with an additionnal field containing the cluster ID.

The input data with an additionnal field containing the cluster ID.

Details

If method == "D_mat" the clustering process is based on building a matrix distance. This is time efficient but use a lot of memory. If method == "Iter" a slower but memory efficient iterative process is used. In some cases, D_clust can help to speed up the process.

References

Lecigne, B., Delagrange, S., & Messier, C. (2018). Exploring trees in three dimensions: VoxR, a novel voxel-based R package dedicated to analysing the complex arrangement of tree crowns. Annals of botany, 121(4), 589-601.

Examples

# NOT RUN {
#- import datasets
t0=data.table::fread(system.file("extdata", "Tree_t0.asc", package="VoxR"))
t1=data.table::fread(system.file("extdata", "Tree_t1.asc", package="VoxR"))

#- keep only the tree crown
t0 = t0[z>=0,]
t1 = t1[z>=0,]

#- substract t0 to t1 with the hull method
diff = VoxR::substract_point_clouds(t0 = t0,t1 = t1, method = "hull")

#- clustering the difference between t0 and t1
clust = VoxR::distance_clustering(diff,d_clust = 0.03)

#- plot the result (NOTE that colors are redundant)
rgl::open3d()
rgl::plot3d(clust,col=clust$cluster,add=TRUE)
#- import datasets
t0=data.table::fread(system.file("extdata", "Tree_t0.asc", package="VoxR"))
t1=data.table::fread(system.file("extdata", "Tree_t1.asc", package="VoxR"))

#- keep only the tree crown
t0 = t0[z>=0,]
t1 = t1[z>=0,]

#- substract t0 to t1 with the hull method
diff = VoxR::substract_point_clouds(t0 = t0,t1 = t1, method = "hull")

#- clustering the difference between t0 and t1 with the matrix distance based method
clust = VoxR::distance_clustering(diff,d_clust = 0.03)

#- plot the result (NOTE that colors are redundant)
rgl::open3d()
rgl::plot3d(clust,col=clust$cluster,add=TRUE)
# }
# NOT RUN {
#- clustering the difference between t0 and t1 with the iterative method
clust = VoxR::distance_clustering(diff,d_clust = 0.03,method = "Iter")

#- plot the result (NOTE that colors are redundant)
rgl::open3d()
rgl::plot3d(clust,col=clust$cluster,add=TRUE)

#- clustering the difference between t0 and t1 with the iterative method with maximum object size
clust = VoxR::distance_clustering(diff,d_clust = 0.03,method = "Iter",C_size = 1)

#- plot the result (NOTE that colors are redundant)
rgl::open3d()
rgl::plot3d(clust,col=clust$cluster,add=TRUE)
# }