I want to compute an unsupervised random forest classification out of a raster stack in R. The raster stack represents the same extent in different spectral bands and as a result I want to obtain an unsupervised classification of the stack. I am having problems with my code as my data is very huge.
Is it okay to just convert the stack into a dataframe in order to run the random forest algorithm like this?
stack_median <- stack(b1_mosaic_median, b2_mosaic_median, b3_mosaic_median, b4_mosaic_median, b5_mosaic_median, b7_mosaic_median) stack_median_df <- as.data.frame(stack_median)
Here is the data as a csv file (https://www.dropbox.com/s/gkaryusnet46f0i/stack_median_df.csv?dl=0) - and you can read it in via:
stack_median_df<-read.csv(file="stack_median_df.csv") stack_median_df<-stack_median_df[,-1] stack_median_df_na <- na.omit(stack_median_df)
My next step would be the unsupervised classification:
median_rf <- randomForest(stack_median_df_na, importance=TRUE, proximity=FALSE, ntree=500, type=unsupervised, forest=NULL)
Due to my huge dataset a proximity measure can't be calculated (would need around 6000GB).
Do you know how to be able to have a look at the classification?
plot(median_rf) don't return anything.