I have a program which compares the similarity of two images for different positions, so my surface consists of points which correspond to X and Y translations each with a value (mutual information).

Here is an example of a surface I want to be ranked as very good:

https://dl.dropboxusercontent.com/u/51282958/good.png

And this is an example of surface which needs a low rank:

https://dl.dropboxusercontent.com/u/51282958/bad.png

So the metric is some kind of measure of certainty, lots of similar peaks = bad and one big peak in center of the surface is good. Also a peak on the border of the surface is bad as it might not be a peak at all.

Now I've already created some kind of metric to rank these surfaces, and it does work in some cases. What I would really like to know is if there is some mathematical or computational method well defined to do this kind of ranking?

I have extracted information such as the location of maxima and standard deviations. Also this surface is sometimes up to 6 dimensional when rotation and scale are included.