Cat on the wall -1

Blogs and comments are the best way to get people talking. I like to listen to people discussing on a few murky aspects in remote sensing.

The first debate is on "Will contrast enhancement improve supervised classification accuracy?"

Preamble:

Analysts perform contrast enhancement to better visualize their objects of interest. So would this enhancement improve the between-class separability?

Scenario 1:

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.

Scenario 2:

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.