Description Usage Arguments Eigen Decomposition of Point Neighborhoods Radius Estimation Through Normal Vectors Intersection References
This function is meant to be used inside stemPoints
. It filters points based on their voxel geometries (check fastPointMetrics
) and assign them to stem patches if reaching a voxel with enough votes.
1 2 3 4 5 6 7 8 9  stm.eigen.voxel(
h_step = 0.5,
max_curvature = 0.1,
max_verticality = 10,
voxel_spacing = 0.025,
max_d = 0.5,
votes_weight = 0.2,
v3d = FALSE
)

h_step 

max_curvature 

max_verticality 

voxel_spacing 

max_d 

votes_weight 

v3d 

Point filtering/classification methods that rely on eigen decomposition rely on shape indices calculated for point neighborhoods (knn or voxel). To derive these shape indices, eigen decomposition is performed on the XYZ columns of a point cloud patch. Metrics related to object curvature are calculated upon ratios of the resulting eigen values, and metrics related to object orientation are caltulated from approximate normals obtained from the eigen vectors.
For instance, a point neighborhood that belongs to a perfect flat surface will have all of its variance explained by the first two eigen values, and none explained by the third eigen value. The 'normal' of such surface, i.e. the vector oriented in the direction orthogonal to the surface, is therefore represented by the third eigenvector.
Methods for both tree mapping and stem segmentation use those metrics, so in order
to speed up the workflow one might apply fastPointMetrics
to the point
cloud before other methods. The advantages of this approach are that users
can parameterize the point neighborhoods themselves when calculating their metrics.
Those calculations won't be performed again internally in the tree mapping or stem
denoising methods, reducing the overall processing time.
stemPoints
methods that filter points based on eigen decomposition
metrics (knn or voxel) provide a rough estimation of stem segments radii
by splitting every stem segment into a local voxel space and counting
the number of times that point normals intersect on every voxel (votes).
Every stem point then has a radius assigned to it, corresponding to the distance
between the point and the voxel with most votes its normal intersects. The average of
all points' radii in a stem segment is the segment's radius. For approximately straight
vertical stem segments, the voting can be done in 2D (pixels).
The point normals of this method are extracted from the eigen vectors calculated by
fastPointMetrics
. On top of the point metrics used for stem point filtering, the following
fields are also added to the LAS
object:
Votes
: number of normal vector intersections crossing the point's normal at its estimated center
VotesWeight
: ratio of (votes count) / (highest votes count) per TreeID
Radius
: estimated stem segment radius
This method was inspired by the denoising algorithm developed by Olofsson & Holmgren (2016), but it is not an exact reproduction of their work.
Liang, X. et al., 2012. Automatic stem mapping using singlescan terrestrial laser scanning. IEEE Transactions on Geoscience and Remote Sensing, 50(2), pp.661–670.
Olofsson, K. & Holmgren, J., 2016. Single Tree Stem Profile Detection Using Terrestrial Laser Scanner Data, Flatness Saliency Features and Curvature Properties. Forests, 7, 207.
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