Author of ideas Ali Santacruz. Video in Youtube, A descriptive post in his blog, Source
The algorithm is adapted for classification of PlanetScope data. PlanetScope Imagery Products specification.
Example data in the PlanetDataExample folder.
Start R in terminal
R
We will need these packages: snow, sp, ggplot2, raster, rgdal, pbkrtest, stats, e1071, lme4, lattice, randomForest, caret.
You need to install the necessary packages, if they are not installed.
install.packages(c("snow", "sp", "ggplot2", "raster", "rgdal", "pbkrtest", "stats", "e1071", "lme4", "lattice", "randomForest", "caret"), dependencies = TRUE)
Load the required packages.
library("snow")
library("sp")
library("ggplot2")
library("raster")
library("rgdal")
library("pbkrtest")
library("stats")
library("e1071")
library("lme4")
library("lattice")
library("randomForest")
library("caret")
Set the working directory with the data
setwd("~/PlanetData/")
Load the raster file
img <- brick("TIF/SourceRaster.tif")
Write the channel names in the raster object
names(img) <- c(paste0("B", 1:4, coll = ""))
We can visualize the raster in the RSL. Not an obligatory step.
plotRGB(img * (img >= 0), r = 3, g = 4, b = 1)
Add training data
trainData <- shapefile("SHP/ROI.shp")
Indicate a field with classes
responseCol <- "Class"
dfAll = data.frame(matrix(vector(), 0, length(names(img)) + 1))
for (i in 1:length(unique(trainData[[responseCol]]))){
category <- unique(trainData[[responseCol]])[i]
categorymap <- trainData[trainData[[responseCol]] == category,]
dataSet <- extract(img, categorymap)
dataSet <- dataSet[!unlist(lapply(dataSet, is.null))]
dataSet <- lapply(dataSet, function(x){cbind(x, class = as.numeric(rep(category, nrow(x))))})
df <- do.call("rbind", dataSet)
dfAll <- rbind(dfAll, df)
}
Generate random samples for training the RandomForests models (For a start, 10000 random samples).
nsamples <- 10000
sdfAll <- subset(dfAll[sample(1:nrow(dfAll), nsamples), ])
Next we must define and fit the RandomForests model using the train function from the ‘caret’ package. First, let’s specify the model as a formula with the dependent variable (i.e., the land cover types ids) encoded as factors. For this we will use four bands as explanatory variables (Blue, Green, Red, Near infrared bands). We then define the method as ‘rf’ which stands for the random forest algorithm. (Note: try names(getModelInfo()) to see a complete list of all the classification and regression methods available in the ‘caret’ package). This step can last a very long time
modFit_rf <- train(as.factor(class) ~ B1 + B2 + B3 + B4, method = "rf", data = sdfAll)
If a large amount of data was taken into the analysis, then after the previous step the computer may hang and can not begin classification of the data.
To check the memory status, run the command:
beginCluster()
If it returns: 4 cores detected, using 3, etc. Go to step Create a raster with predictions
If it returns: ERROR: Free cores not found, etc. You need to free up memory and CPU.
To free memory and CPU, you need to close R with the save of history and workspace.
q("yes")
Close terminal
exit
Open the terminal and start R again.
R
Load the packages.
library("snow")
library("sp")
library("ggplot2")
library("raster")
library("rgdal")
library("pbkrtest")
library("stats")
library("e1071")
library("lme4")
library("lattice")
library("randomForest")
library("caret")
Set working directory with the data.
setwd("~/PlanetData/")
Load previously created R objects and history.
load(".RData")
loadhistory(file=".Rhistory")
Use the clusterR function from the raster package, which supports multi-core computations for functions such as forecasting.
beginCluster()
preds_rf <- clusterR(img, raster::predict, args = list(model = modFit_rf))
endCluster()
Display the result of the classification
plot(preds_rf)
Save the raster to a GeoTiff file
writeRaster(preds_rf, filename="ResultClassification.tif", format = "GTiff", datatype='INT1U', overwrite=TRUE)
Close R with the save of history and workspace.
q("yes")