rem_outliers: Remove statistical outliers in sky points

In rcaiman: CAnopy IMage ANalysis

Remove statistical outliers in sky points

Description

Remove sky points considered outliers relative to their local neighbors in a user-specified variable.

Usage

rem_outliers(
  sky_points,
  r,
  z,
  a,
  k = 20,
  angular_radius = 20,
  laxity = 2,
  cutoff_side = "both",
  use_window = TRUE,
  trend = NULL
)

Arguments

sky_points	data.frame with columns row and col (raster coordinates).
r	terra::SpatRaster. Image from which sky_points were sampled (or any raster with identical dimensions).
z	terra::SpatRaster generated with zenith_image().
a	terra::SpatRaster generated with azimuth_image().
k	numeric vector of length one. Number of neighbors.
angular_radius	numeric vector of length one. The maximum radius for searching k-nearest neighbors (KNN) in degrees.
laxity	numeric vector of length one.
cutoff_side	character vector of length one. Options are "both" (default), "upper" or "lower". Controls which side(s) of the inequality are evaluated to detect outliers. See Details.
use_window	logical of length one. If TRUE (default), use a 3 \times 3 local mean around each point; if FALSE, use only the central pixel.
trend	numeric vector of length one or NULL. Zero to three. Specifies the order of the polynomial surface fitted to the neighbors to account for spatial trends. Use NULL (default) to skip detrending.

Details

Based on the Statistical Outlier Removal (SOR) filter from the PCL library. Distances are computed on a spherical surface. The number of neighbors is controlled by k, and angular_radius sets the maximum search radius (deg). If fewer than k neighbors are found within that radius, the point is retained due to insufficient evidence for removal. The decision criterion follows \insertCiteLeys2013;textualrcaiman:

M - laxity \times MAD < x_i < M + laxity \times MAD

where x_i is the value from r at sky point i, M and MAD are the median and median absolute deviation, respectively, computed from the the neighbors of x_i, and laxity is the user-defined threshold.

cutoff_side controls which side(s) of the inequality are evaluated: "both" (default), "left" (left tail only), or "right" (right tail only).

Value

The retained points represented as a data.frame with columns row and col, same as sky_points.

Note

This function assumes that sky_points and the terra::SpatRaster objects refer to the same image geometry. No checks are performed.

References

\insertAllCited

Examples

## Not run: 
caim <- read_caim()
r <- caim$Blue
z <- zenith_image(ncol(caim), lens())
a <- azimuth_image(z)
m <- !is.na(z)
bin <- binarize_by_region(r, ring_segmentation(z, 30),
                          method = "thr_isodata")
bin <- bin & select_sky_region(z, 0, 80)
g <- sky_grid_segmentation(z, a, 5, first_ring_different = TRUE)
sky_points <- extract_sky_points(r, bin, g,
                                 dist_to_black = 3)
plot(r)
points(sky_points$col, nrow(caim) - sky_points$row, col = 2, pch = 10)

sky_points <- rem_outliers(sky_points, r, z, a,
                                 k = 5,
                                 angular_radius = 20,
                                 laxity = 2,
                                 cutoff_side = "left")
points(sky_points$col, nrow(caim) - sky_points$row, col = 3, pch = 0)

## End(Not run)

rcaiman documentation built on Sept. 9, 2025, 5:42 p.m.