stm.hough: Stem denoising algorithm: Hough Transform

In tiagodc/TreeLS: Terrestrial Point Cloud Processing of Forest Data

Stem denoising algorithm: Hough Transform

Description

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.

Usage

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
)

Arguments

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

Adapted 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.

References

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.

tiagodc/TreeLS documentation built on June 29, 2023, 7:40 p.m.