Random Forest image classification in R

randomForest sample.R

# Random Forest image classification
# Adapted from http://gis.stackexchange.com/a/57786/12899

#' Performs a random forest classification of the specified raster.
#'
#' @param rasterPath  The full path to the raster file to be classified.
#' @param samplesPath The full path to the .SHP file of the training samples.
#' @param samplesCol  The column in the training samples shape file that contains the class names.
#' @param classifiedPath The full path to the output classified image.
#'
#' @author Franz Alex Gaisie-Essilfie
randomForest.classify <- function(rasterPath, samplesPath,
                                  samplesCol="CLASS", classifiedPath=NA){
  # Install the required packages that aren't already installed
  toInstall <- setdiff(c("randomForest", "sp", "rgdal", "raster", "tcltk"),
                        rownames(installed.packages()))
  if (length(toInstall)) install.packages(toInstall)

  # Add required libraries
  require(randomForest)
  require(sp)
  require(rgdal)
  require(raster)

  # Ensure we have a valid path to the training samples
  if (is.na(samplesPath) || !file.exists(samplesPath)){
    shapeFileFilter <- matrix(c("All Files", "*.*", "Shape Files", "*.shp"), ncol=2, byrow=T)
    samplesPath <- tcltk::tk_choose.files(caption="Select training samples shape file",
                                          filters=shapeFileFilter, multi=F)
  }
  if (length(samplesPath) == 0 || !file.exists(samplesPath))
    stop("Could not locate training samples.")


  # Load the training samples
  samples <- readOGR(dirname(samplesPath),
                     tools::file_path_sans_ext(basename(samplesPath)))


  # Ensure we have a valid path to the unclassified image
  if (is.na(rasterPath) || !file.exists(rasterPath)){
    rasterFilter <- matrix(c("All Files", "*.*",
                             "TIFF Image", "*.tif",
                             "ERDAS IMAGINE", "*.img"), ncol=2, byrow=T)
    rasterPath <- tcltk::tk_choose.files(caption="Select unclassified image",
                                         filters=rasterFilter, multi=F)
  }
  if (length(rasterPath) == 0 || !file.exists(rasterPath))
    stop("Could not locate raster file for classification.")

  # Read the image to be classified
  multispectral <- stack(rasterPath)

  # Use sample points to extract raster signatures
  samples@data <- data.frame(samples@data, extract(multispectral, samples))

  # Create RandomForest model
  rf.mdl <- randomForest(x=samples@data[, names(multispectral)],
                         y=samples@data[, samplesCol],
                         ntree=501, proximity=T, importancce=T)


  # Ensure we have a valid path to the classified image
  if (is.na(classifiedPath) || file.exists(classifiedPath)){
    rasterFilter <- paste('{"All Files" {"*.*"}}',
                          '{"TIFF Image" {"*.tif"}} ',
                          '{"ERDAS IMAGINE" {"*.img"}}',
                           sep=" ")
    default <- file.path(dirname(rasterPath),
                         paste(tools::file_path_sans_ext(basename(rasterPath)),
                               "classified", tools::file_ext(rasterPath), sep="."))
    classifiedPath <- tcltk::tkgetSaveFile(title="Save classified image as",
                                           initialfile=default,
                                           filetypes=rasterFilter)
    if (nchar(classifiedPath <- as.character(classifiedPath)) == 0)
      stop("No raster file selected for saving classified image.")
  }

  # Predict classified raster
  predict(multispectral, rf.mdl, filename=classifiedPath, type="response",
          na.rm=T, overwrite=T, progress="text", datatype="INT2U")
}

usage.r

setwd("C:/data/")   # set the working directory to the directory where the data is stored
randomForest.classify("multispectral.img",    # file name of the unclassified image
                      "trainingSamples.shp",  # point shape file of the locations of the training samples
                      "Class_Name",           # the column in the training samples shape file which contain the class names
                      "classifiedImage.img"   # file name of the classified image
                     )

You can do that by modifying lines 58 to 60 ( https://gist.github.com/franzalex/a95e227cab9b146a6092#file-randomforest-sample-r-L58-L60) as you require. For example, to set up a different number of trees, change *ntree=501* on line 60 to the value you desire. Hope this helps.

…

On Wed, 7 Apr 2021, 14:48 githubArian, ***@***.***> wrote: ***@***.***Arian* commented on this gist. ------------------------------ Sorry, but where do I change the randomForest parameters like the number of trees, for example? — You are receiving this because you authored the thread. Reply to this email directly, view it on GitHub <https://gist.github.com/a95e227cab9b146a6092#gistcomment-3696773>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/ABZVXAUY5GLKZ7O53XVBTNDTHRWCJANCNFSM4M7T5UJQ> .