Playing with a hyper-spectral imager makes you rethink how we see things. I've talked about this before, but human vision essentially "low resolution" on the spectral bands. Using an HSI that "sees" in 4nm spectral slices from 350-1000nm is really interesting (Cubert Ultris X20 Plus). There's so much spectral information that we just totally miss. I really wish the equipment for capturing images at these higher spectral resolutions wasn't so expensive so we could see people experimenting with them on a large scale. The one I got to work with was more than a nice SUV and that's cheap in the space. The ones we looked at from Headwall that did something like 400-2500nm started at $250k. Those weren't even full-frame imagers like the Ultris, they were line scanning imagers. That massive cost jump was down to the fact that from ~1050nm+ you need much different hardware to capture spectral data.
If anyone is interested in some technical aspects of the "full frame" HSI I worked with, it's quite interesting. It had a 20MP Monochromatic Sensor that captured single-band 12-bit data behind an array of lenses that split the incoming spectral range (350-100nm) into 164 individual 4nm wide bands of light that hit 410x410px squares on the sensor. The sensor can capture from 350-1100, but the QE drops of really fast past about 850nm and the product limited the upper range to 1000nm. I'm sure I munged something there, but you should get the general idea. I highly recommend researching the space of HSI, it's fascinating.
Last thing to point out, when working with an HSI like this, one thing you can do is capture a "spectral fingerprint". Since you've gone from three bands on spectral intensity information to, in our case, 164 bands you have the ability to turn that high-density spectral data for each pixel into essentially a line graph. Using that information you can do matching against a database of known spectral fingerprints and identify materials and material properties really well. In the multi-spectra world you'll see this capability used to identify crop health. In the hyperspectral world you can identify so much more. For instance, it can see skin anomalies that aren't visible to the human eye. You can identify specific minerals in a picture of a bunch of rocks (you need up into the 2500nm range for this though). You can easily spot foreign objects on a conveyor of food items. Overall, it's a long list of capabilities and I'm certain there are many more uses we could discover if the imagers were cheaper. And if you are into the wider ML world (not just focused on LLMs I mean), you'll see ML Classification Models being trained on these spectral fingerprints as well.
Anyway, the "full-spectrum" is fascinating, especially when you are able to slice it thin.