How to perform Random Forest land classification?

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This is a follow-up to a previous post: Machine Learning Algorithms for Land Classification.

It seems that the Random Forest (RF) classification method is gaining much momentum in the remote sensing world. I am particularly interested in RF due to many of its strengths:

• A nonparametric approach suited to remote sensing data
• High reported classification accuracy
• Variable importance is reported

Given these strengths, I would like to perform Random Forest land classification using high resolution 4 band imagery. There is a lot of material and research touting the advantages of Random Forest, yet very little information exists on how to actually perform the classification analysis. I am familiar with RF regression using R and would prefer to use this environment to run the RF classification algorithm.

How do I collect, process and input training data (i.e. based on high resolution CIR aerial imagery) into the Random Forest algorithm using R? Any step-wise advice on how to produce a classified land cover raster would be greatly appreciated.



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