I have been trying to predict Land Use Classes in R using Random Forest and Caret packages. My area of interest is 4300km2 (raster dimension X 4109:Y2800). I am working with 4 multispectral Landsat bands.
I was able to do the whole process and classify the target classes but some areas within my map were not properly classified. When I plot the results from the function prediction() I have like "gaps" within my final map. The whole process runs without any warning or error.
I would like to know which is the best way to predict Land Use classes for a a big dataset.
I have created my model
Model using the training dataset (caret package)
trControl = trainControl(method="cv",number=10, repeats =30, allowParallel=TRUE) rfGrid
I was able to do the whole process and classify the target classes but some areas within my map were not properly classified. When I plot the results from the function prediction() I have like "gaps" within my final map. The whole process runs without any warning or error.
I would like to know which is the best way to predict Land Use classes for a a big dataset.
I have created my model
Model using the training dataset (caret package)
trControl = trainControl(method="cv",number=10, repeats =30, allowParallel=TRUE) rfGrid