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Mean-Shift Segmentation

segm_meanshift.Rd

Applies the mean-shift segmentation algorithm to an image file or a SpatRaster

Usage

segm_meanshift(
  image,
  otb,
  spatialr = 5L,
  ranger = 15,
  thresh = 0.1,
  maxiter = 100L,
  minsize = 100L,
  mode = "vector",
  vector_neighbor = FALSE,
  vector_stitch = TRUE,
  vector_minsize = 1L,
  vector_simplify = 0.1,
  vector_tilesize = 1024L,
  mask = NULL
)

Arguments

image

path or SpatRaster

otb

output of link2GI::linkOTB()

spatialr

integer. Spatial radius of the neighborhood

ranger

range radius defining the radius (expressed in radiometry unit) in the multispectral space

thresh

algorithm iterative scheme will stop if mean-shift vector is below this threshold or if iteration number reached maximum number of iterations

maxiter

integer. Algorithm iterative scheme will stop if convergence hasn’t been reached after the maximum number of iterations

minsize

integer. Minimum size of a region (in pixel unit) in segmentation. Smaller clusters will be merged to the neighboring cluster with the closest radiometry. If set to 0 no pruning is done

mode

processing mode, either 'vector' or 'raster'. See details

vector_neighbor

logical. If FALSE (the default) a 4-neighborhood connectivity is activated. If TRUE, a 8-neighborhood connectivity is used

vector_stitch

logical. If TRUE (the default), scans polygons on each side of tiles and stitch polygons which connect by more than one pixel

vector_minsize

integer. Objects whose size in pixels is below the minimum object size will be ignored during vectorization

vector_simplify

simplify polygons according to a given tolerance (in pixel). This option allows reducing the size of the output file or database.

vector_tilesize

integer. User defined tiles size for tile-based segmentation. Optimal tile size is selected according to available RAM if NULL

mask

an optional raster used for masking the segmentation. Only pixels whose mask is strictly positive will be segmented

Value

sf or SpatRaster

Details

Mean-Shift is a region-based segmentation algorithm that groups pixels with similar characteristics. It's a non-parametric clustering technique that groups pixels based on spatial proximity and feature similarity (color, intensity). This method is particularly effective for preserving edges and defailt while simplifying textures in high-resolution images. Steps:

  1. Initialization: Each pixel is treated as a point in a multi-dimensional space (combining spatial and color features).

  2. Mean Shift Iterations: For each pixel, a search window moves toward the region with the highest data density (local maxima) by calculating the mean of neighboring pixels within the window.

  3. Convergence: The process repeats until the movement of the window becomes negligible, indicating convergence.

  4. Label Assignment: Pixels that converge to the same mode (local maxima) are grouped into the same region.

The most important parameters are:

  • spatialr: defines the size of the neighborhood

  • ranger: determines similarity in the feature space

  • maxiter: limits the number of iterations for convergence

  • thresh: defines the convergence criterion based on pixel movement

The processing mode 'vector' will output a vector file, and process the input image piecewise. This allows performing segmentation of very large images. IN contrast, 'raster' mode will output a labeled raster, and it cannot handle large data. If mode is 'raster', all the 'vector_*' arguments are ignored.

Examples

if (FALSE) { # \dontrun{
## load packages
library(link2GI)
library(OTBsegm)
library(terra)

## load sample image
image_sr <- rast(system.file("raster/pnoa.tiff", package = "OTBsegm"))

## connect to OTB (change to your directory)
otblink <- link2GI::linkOTB(searchLocation = "C:/OTB/")

## apply segmentation
results_ms_sf <- segm_meanshift(
    image    = image_sr,
    otb      = otblink,
    spatialr = 5,
    ranger   = 25,
    maxiter  = 10,
    minsize  = 10
)
} # }