Monday, June 22, 2009

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?"
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.