I currently am trying to cluster "types" of changes on bitemporal multispectral satellite images.
I applied a thing called a mad transform to both images, 5000 x 5000 pixels x 5 bands. Each band is a "variable" as it is radiance information from a different spectrum of light. This transform is basically equivalent to PC applied to the substraction of both images.
Naturally I can get up to 5 mad components. Now I would like to find this types of change on these components. If I use K-means on the components I would use an euclidean distance but I just wanted to know what could be the gain in using a mahalanobis distance if there is any.