Set Phone To… Hyperspectral

September 26, 2025 by Al Williams 24 Comments

While our eyes are miraculous little devices, they aren’t very sensitive outside of the normal old red, green, and blue spectra. The camera in your phone is far more sensitive, and scientists want to use those sensors in place of expensive hyperspectral ones. Researchers at Purdue have a cunning plan: use a calibration card.

The idea is to take a snap of the special card and use it to understand the camera’s exact response to different colors in the current lighting conditions. Once calibrated to the card, they can detect differences as small as 1.6 nanometers in light wavelengths. That’s on par with commercial hyperspectral sensors, according to the post.

You may wonder why you would care. Sensors like this are useful for medical diagnostic equipment, analysis of artwork, monitoring air quality, and more. Apparently, high-end whisky has a distinctive color profile, so you can now use your phone to tell if you are getting the cheap stuff or not.

We also imagine you might find a use for this in phone-based spectrometers. There is plenty to see in the hyperspectral world.

Hyperspectral Imaging – Seeing The Unseeable

October 25, 2017 by Will Sweatman 25 Comments

They say that a picture is worth a thousand words. But what is a picture exactly? One definition would be a perfect reflection of what we see, like one taken with a basic camera. Our view of the natural world is constrained to a bandwidth of 400 to 700 nanometers within the electromagnetic spectrum, so our cameras produce images within this same bandwidth.

For example, if I take a picture of a yellow flower with my phone, the image will look just about how I saw it with my own eyes. But what if we could see the flower from a different part of the electromagnetic spectrum? What if we could see less than 400 nm or greater than 700 nm? A bee, like many other insects, can see in the ultraviolet part of the spectrum which occupies the area below 400 nm. This “yellow” flower looks drastically different to us versus a bee.

In this article, we’re going to explore how images can be produced to show spectral information outside of our limited visual capacity, and take a look at the multi-spectral cameras used to make them. We’ll find that while it may be true that an image is worth a thousand words, it is also true that an image taken with a hyperspectral camera can be worth hundreds of thousands, if not millions, of useful data points. Continue reading “Hyperspectral Imaging – Seeing The Unseeable” →

Hyperspectral Imaging With A DSLR

May 18, 2014 by Brian Benchoff 12 Comments

It’s a relatively simple task to find evidence of helium by just looking at the sun; all you need is a prism, diffraction grating, and a web cam. DIY spectrometers have been around for ages, but most of them only produce a spectrum, not a full image complete with spectral data. Now it’s possible to take an image of an object, complete with that objects spectra using a DSLR, some lenses, a PVC pipe, and the same diffraction grating from your DIY interferometer.

The idea behind a hyperspectral imager is to gather the spectral data of each pixel of an image. The spectral data is then assembled into a 3D data cube, with two dimensions dedicated to the image, and the third dimension used to represent wavelength. There are a surprising number of applications for this technique, ranging from agriculture and medicine to some extremely creepy surveillance systems.

The authors of this paper (freakin’ huge PDF) used a piece of PVC pipe, three camera lenses, a diffraction grating, and a small paper aperture to construct their hyperspectral imager. Images are captured using a standard, multi exposure HDR method, assembling the raw data from the camera into a hyperspectral image with MATLAB.

There’s a ton of awesome info in the PDF, covering how the authors calibrated their system for different lighting conditions, interpreted the RGGB Bayer sensor in the camera, and a few examples of what kind of image can be constructed with this kind of data. That’s a recommended read, right there.

Thanks [Yannick] for the tip.