Skip to contents

Large-scale segmentation using Mean-Shift

segm_lsms.Rd

Applies the Mean-Shift segmentation algorithm to an image file or a SpatRaster. Suitable for large images

Usage

segm_lsms(
  image,
  otb,
  spatialr = 5L,
  ranger = 15,
  minsize = 100L,
  tilesize = 500L,
  mode = "vector",
  ram = 256L
)

Arguments

image

path to raster, 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

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

tilesize

integer. Size of the tiles during the tile-wise processing

mode

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

ram

integer. Available memory for processing (in MB)

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_lsms(
    image = image_sr,
    otb   = otblink,
    spatialr = 5,
    ranger   = 25,
    minsize  = 10
)

plotRGB(image_sr)
plot(st_geometry(results_ms_sf), add = TRUE)
} # }