I'm using Google Earth Engine to classify mangrove forests from Landsat imagery to calculate extent. Let's say I perform an unsupervised K-means cluster analysis on a Landsat scene to identify general patterns and themes in the spectral bands.
After grouping the clusters together into several relevant classes (e.g. mangrove, other_vegetation, soil, urban, water), would it then be possible to use this classified image for training a supervised classification algorithm such as random forest?
The idea is to use a stratified random sampling technique (~5000 points) to assign the training points within the classes obtained through the cluster analysis.
Can anyone see a flaw in this method and would it increase the accuracy of the final classification?
TLDR - is it possible (or advisable) to use the output from an unsupervised classification technique to train a supervised classification algorithm?