The first debate is on "Will contrast enhancement improve supervised classification accuracy?"
Analysts perform contrast enhancement to better visualize their objects of interest. So would this enhancement improve the between-class separability?
The analyst contrast stretches the image and chooses the training pixels. He burns the LUT (look up table) to create a new image. He uses the same training pixels for classification in the original image and on the stretched image.
The analyst uses a different set of traning pixels on both the images.
I would like to know your opinion on the results of the two scenarios. Which one would you consider better and the reason.