Hyperspectral imaging is an exciting and promising new technology. When combined with modern machine learning software and actuators such as robots, air-jets, or flippers, hyperspectral imaging has the capability to revolutionize industrial sorting, enabling machines to perform complex tasks previously limited to humans.
What Exactly Is It?
Hyperspectral imaging is measuring high-resolution spectral data at every pixel in a two-dimensional image. Standard cameras provide three spectral data points at each pixel: red, green, and blue (RGB). The viewer's brain inputs the mixture of these three and interprets them as a unique color. In a hyperspectral image, the materials’ reflectance spectrum is a continuous curve of wavelengths with hundreds of spectral data points at each pixel. It is really a form of spectroscopy. A standard spectrometer provides only one “pixel” per measurement — there is no imaging. “Multispectral” imaging refers to a camera with each pixel delivering a few spectral data points, typically between four and twelve. The term hyperspectral refers to many spectral data points producing a near continuous spectrum, typically hundreds of data points at each pixel. However, designing and manufacturing hyperspectral cameras is significantly more complex than for point spectrometers, as imaging all wavelengths across an entire image plane while minimizing distortions is a difficult optical problem.
Figure 1 shows a false-color RGB image from aerial hyperspectral data of a farm, along with reflectance spectra from three regions in the scene. Figure 2 shows the same spectral data, but reduced to a multispectral signal with four bands. These four bands are similar to what the early Landsat satellites measured. It is clear from Figures 1 and 2 that much more information is contained in the hyperspectral data, enabling precise and reliable classification of objects in a scene.
Where is the Technology Now?
Hyperspectral imaging is becoming popular for three primary reasons: the cameras are becoming inexpensive and operate at high speeds; compact computers provide sufficient computing capability to handle high data volumes; and modern machine learning algorithms can use hyperspectral data to accurately identify materials, even those that appear quite similar. The technology is advancing rapidly in both research and industrial applications. Its usefulness is based on the fact that different materials have different reflectance spectra.
Applications — Agriculture
In agriculture, different plant species have different spectra, as do the same plant species under different conditions. For example, hyperspectral data can be used to alert farmers to early signs of crop stress or noxious weed infestations, as well as to determine ripeness. In precision agriculture and environmental monitoring the technology is increasingly viable for commercial applications, and will expand as additional obstacles are overcome. Determining accurate relationships between spectral features and plant physiologies, for example, is a focus of much research worldwide. This will enable site-specific applications of herbicides, pesticides, and fertilizers. Ultimately, such targeted crop management techniques will allow farmers to use fewer chemicals more effectively and produce greater yields. Thus, there is strong institutional support for hyper-spectral technologies, especially in Asia where food supply and environmental protection are critical issues.
Applications — Industry
Because factory environments can be controlled better than outdoor farmlands and forests, hyperspectral imaging is gaining traction for industrial sorting applications. Conventional vision systems often fail to sort items that have similar colors or appearance. Two examples of difficult applications for traditional color sorting systems, are foods with similar-colored shells or peels, and materials whose composition is unrelated to their visible color, such as recycled plastics. When standard vision systems fail, these sorting tasks fall to humans, processes that are costly, sl