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
                     )

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")
}

i'm sorry, i don't undestand ... classifiedPath... where i find it? because, you use that only in the ultimate script... and if i use that, R tell me that don't find classifiedPath... i need to know what is it that...

@SirLotus

i'm sorry, i don't undestand ... classifiedPath... where i find it? because, you use that only in the ultimate script... and if i use that, R tell me that don't find classifiedPath... i need to know what is it that...

The classifiedimage parameter is the path to the file that the output should be saved to. You can specify just the file name or the full path (including drive and parent directory). If you specify a relative path (i.e. just the file name), R will save the file in the current working directory. Specifying a relative path (i.e. drive, directory, file name), then R will save the file to the the path you specified.

I hope I clarified the issue for you.

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> .