lidR (version 3.0.4)

shape_detection: Algorithms for shape detection of the local point neighborhood

Description

These functions are made to be used in lasdetectshape. They implement algorithms for local neighborhood shape estimation.

Usage

shp_plane(th1 = 25, th2 = 6, k = 8)
shp_hplane(th1 = 25, th2 = 6, th3 = 0.98, k = 8)
shp_line(th1 = 10, k = 8)

Arguments

th1, th2, th3

numeric. Threshold values (see details)

k

integer. Number of neighbours used to estimate the neighborhood.

Details

In the following, $a1,a2,a3$ denote the eigenvalues of the covariance matrix of the neighbouring points in ascending order. $th1,th2,th3$ denote a set of threshold values. Points are labelled TRUE if they meet the following criteria. FALSE otherwise.

shp_plane

Detection of plans based on criteria defined by Limberger & Oliveira (2015) (see references). A point is labelled TRUE if the neighborhood is approximately planar, that is: $$a2>(th1\ast a1)and(th2\ast a2)>a3$$

shp_hplane

The same as 'plane' but with an extra test on the orientation of the Z vector of the principal components to test the horizontality of the surface. $$a2>(th1\ast a1)and(th2\ast a2)>a3and|Z|>th3$$ In theory |Z| should be exactly equal to 1. In practice 0.98 or 0.99 should be fine

shp_line

Detection of lines inspired by the Limberger & Oliveira (2015) criterion. A point is labelled TRUE if the neighborhood is approximately linear, that is: $$th1\ast a2<a3andth1\ast a1<a3$$

References

Limberger, F. A., & Oliveira, M. M. (2015). Real-time detection of planar regions in unorganized point clouds. Pattern Recognition, 48(6), 2043<U+2013>2053. https://doi.org/10.1016/j.patcog.2014.12.020