Hi all,
I wanted a few opinions on a metric that I'm using to measure the "difference" between spectral plots. I have these spectral plots of grass, conifer and pinewood; wavelength on x-axis and reflectance% of material on y-axis.
Visually, it is easy to see that pinewood is way too different from grass or conifer and grass is very similar to conifer except for a tiny “bump” near the near-infrared portion.
Since the spectra are basically column vectors I can compute abs(mean(abs(spectra1) – abs(spectra2))) for all three.

For normalized data, the respective mean distances are:
dm(grass, conifer) = 0.0271; dm(grass, pinewood) = 0.1518 and dm(pinewood, conifer) = 0.1789
So the more dm is closer to 0, the more similar the spectra are?

Can I use this to mathematically say “this spectra is different by this measure from the other spectra”? Do I need more results from various other materials?
Or do you think I must go back to textbook methods like Euclidean distance or Kullback-Leibler?

Cheers.