stm.hough: Stem denoising algorithm: Hough Transform
In TreeLS: Terrestrial Point Cloud Processing of Forest Data

Description Usage Arguments LAS@data Special Fields Adapted Hough Transform References

This function is meant to be used inside stemPoints. It applies an adapted version of the Hough Transform for circle search. Mode details are given in the sections below.

stm.hough(
  h_step = 0.5,
  max_d = 0.5,
  h_base = c(1, 2.5),
  pixel_size = 0.025,
  min_density = 0.1,
  min_votes = 3
)

`h_step`	`numeric` - height interval to perform point filtering/assignment/classification.
`max_d`	`numeric` - largest tree diameter expected in the point cloud.
`h_base`	`numeric` vector of length 2 - tree base height interval to initiate circle search.
`pixel_size`	`numeric` - pixel side length to discretize the point cloud layers while performing the Hough Transform circle search.
`min_density`	`numeric` - between 0 and 1 - minimum point density within a pixel evaluated on the Hough Transform - i.e. only dense point clousters will undergo circle search.
`min_votes`	`integer` - Hough Transform parameter - minimum number of circle intersections over a pixel to assign it as a circle center candidate.

`LAS@data` Special Fields

Meaninful new fields in the output:

Stem: TRUE for stem points
Segment: stem segment number (from bottom to top and nested with TreeID)
Radius: approximate radius of the point's stem segment estimated by the Hough Transform - always a multiple of the pixel_size
Votes: votes received by the stem segment's center through the Hough Transform

The Hough Transform circle search algorithm used in TreeLS applies a constrained circle search on discretized point cloud layers. Tree-wise, the circle search is recursive, in which the search for circle parameters of a stem section is constrained to the feature space of the stem section underneath it. Initial estimates of the stem's feature space are performed on a baselise stem segment - i.e. a low height interval where a tree's bole is expected to be clearly visible in the point cloud. The algorithm is described in detail by Conto et al. (2017).

This adapted version of the algorithm is very robust against outliers, but not against forked or leaning stems.

Olofsson, K., Holmgren, J. & Olsson, H., 2014. Tree stem and height measurements using terrestrial laser scanning and the RANSAC algorithm. Remote Sensing, 6(5), pp.4323–4344.

Conto, T. et al., 2017. Performance of stem denoising and stem modelling algorithms on single tree point clouds from terrestrial laser scanning. Computers and Electronics in Agriculture, v. 143, p. 165-176.

TreeLS documentation built on Aug. 26, 2020, 5:14 p.m.