lidR (version 3.2.3)

segment_shapes: Eigenvalues-based features at the point level

Description

Functions that compute, for each point, the eigenvalues of the covariance matrix of the neighbouring points. The eigenvalues are later used either to segment linear/planar points or to compute derived metrics (see Details).

Usage

segment_shapes(las, algorithm, attribute = "Shape", filter = NULL)
point_eigenvalues(las, k, r, xyz = FALSE, metrics = FALSE, filter = NULL)

Arguments

las

An object of class LAS or LAScatalog.

algorithm

An algorithm for shape detection. lidR has: shp_plane, shp_hplane and shp_line.

attribute

character. The name of the new column to add into the LAS object.

filter

formula of logical predicates. Enables the function to run only on points of interest in an optimized way. See the examples.

k, r

integer and numeric respectively for k-nearest neighbours and radius of the neighborhood sphere. If k is given and r is missing, computes with the knn, if r is given and k is missing computes with a sphere neighbourhood, if k and r are given computes with the knn and a limit on the search distance.

xyz

logical. Returns the XYZ coordinates of each points instead of IDs.

metrics

logical. Compute additional metrics such as curvature, linearity, planarity based on the eigenvalues.

Value

segment_shape

A LAS object with a new column named after the argument attribute that indicates those points that are part of a neighborhood that is approximately of the shape searched (TRUE) or not (FALSE).

point_eigenvalues

A data.frame like point_metrics

Details

All the functions documented here can be reproduced with point_metrics. However, point_metrics is a versatile and multipurpose function that is not as fast as is possible because it calls user-defined R code and that implies computational overheads. These functions are parallelized plain C++ versions of tools users can build with point_metrics and are consequently 10-times faster.

segment_shape

The points that meet a given criterion based on the eigenvalue are labelled as approximately coplanar/colinear or any other shape supported.

point_eigenvalues

Computes the eigenvalues of the covariance matrix and computes associated metrics following Lucas et al, 2019 (see references). It is equivalent to point_metrics(las, .stdshapemetrics) but much faster

References

Lucas, C., Bouten, W., Koma, Z., Kissling, W. D., & Seijmonsbergen, A. C. (2019). Identification of Linear Vegetation Elements in a Rural Landscape Using LiDAR Point Clouds. Remote Sensing, 11(3), 292.

Examples

# NOT RUN {
LASfile <- system.file("extdata", "Megaplot.laz", package="lidR")
las <- readLAS(LASfile, filter = "-keep_random_fraction 0.5")

# Use the eigenvalues to estimate if points are part of a local plan
las <- segment_shapes(las, shp_plane(k = 15), "Coplanar")
#plot(las, color = "Coplanar")

# Computes the eigenvalue of each point
M <- point_eigenvalues(las, k = 15)
M

# }
# NOT RUN {
# Drop ground point at runtime
las <- segment_shapes(las, shp_plane(k = 15), "Coplanar", filter = ~Classification != 2L)
#plot(las, color = "Coplanar")
# }
# NOT RUN {
# }