I'm working on an accuracy assessment of a classified landsat image and I'd like input on the best possible way to generate random points across both the classified image and a ground truth image I created by processing vector data from aerial photo interpretation.
I've processed both images and converted them to vectors in R and would like to generate random points in specific landcovers across both images so I can compare the predicted vs. actual landcover value at each random site.
My methodology so far has been to compare both vectors (in their entirety) and create a confusion matrix on them but the images this would include the training pixels in the supervised classification so I'd rather not have them as part of the accuracy assessment. A smaller subset of assessment pixels is probably best but I'd like feedback on those who have done this in R in the past as to best practices.
What approach should I take to generate random pixels in each image so I can compare the landcover values?