I am working with hyperspectral image data in R, so I have subset an image to a region of 5000 pixels, each containing a vector 254 bands in length.


I would like to cluster this data in order to try and map regions with similar surface composition.


Due to differences in surface reflectance, if I plot two pixels, where for example:
x=1:254, y=0:1 (reflectance)


They may have very similar shape (values across all bands) but be vertically offset from one another due to the overall reflectance of the surface. 


For my region I have a mean spectrum, and each pixel contains a vector of 254 residual values. I can't use Euclidean distance to compare vectors, because it will change depending on the overall reflectance, so I'm not sure if there's a more appropriate measure to use that will give me a better comparison.


Apologies for my novice question.


\*Edit: This question is also posted at: <https://stats.stackexchange.com/questions/67781/appropriate-distance-measures>



Appropriate histogram comparison distance measure
A good place to start would be the [Wasserstein metric](http://en.wikipedia.org/wiki/Wasserstein_metric#Applications).


