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”→
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.