This function is meant to be used inside treeMap. It applies an adapted version of the Hough Transform for circle search. Mode details are given in the sections below.
map.hough(hmin = 1, hmax = 3, hstep = 0.5, pixel_size = 0.025,
max_radius = 0.25, min_density = 0.1, min_votes = 3)numeric - height thresholds within the point cloud in which circle search will be performed.
numeric - height interval to perform circle search.
numeric - pixel side length to discretize the point cloud layers while performing the Hough Transform circle search.
numeric - approximately the largest stem cross section radius expected in the point cloud.
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.
integer - Hough Transform parameter - minimum number of circle intersections over a pixel to assign it as a circle center candidate.
Each point in the LAS object output represents a pixel center that is
possibly also a stem cross-section center.
The variables describing each point in the output are:
Intensity: number of votes received by that point
PointSourceID: unique stem segment ID (among all trees)
Keypoint_flag: if TRUE, the point is the most likely circle center
of its stem segment (PointSourceID)
Radii: approximate radius estimated by that point - always a multiple of the pixel_size
TreeID: unique tree ID of the point
TreePosition: if TRUE, the point represents its tree's approximate coordinate
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.
An initial tree filter is used to select probable trees in the input point
cloud. Parallel stacked layers, each one as thick as hstep, undergo the
circle search within the hmin/hmax limits. On every layer, pixels
above the min_votes criterion are clustered, forming
probability zones. Probability zones vertically aligned
on at least 3/4 of the stacked layers are assigned as tree occurrence regions
and exported in the output map.
Conto, T. ; Olofsson, K. ; Gorgens, E. B. ; Rodriguez, L. C. E. ; Almeida, G. 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, 2017.
# NOT RUN {
file = system.file("extdata", "model_boles.laz", package="TreeLS")
tls = readTLS(file)
plot(tls)
## build a 3D map of tree occurrences
map = treeMap(tls)
plot(map, color='Radii')
## get a 2D representation of the tree map
xymap = treePositions(map)
head(xymap)
# }
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