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What is Hyperspectral Imaging?

Hyperspectral imaging is a technique that acquires and analyzes a portion of the spectrum of light (often a broader portion than our human eyes can see) in very fine detail instead of simply assigning light into a small number of bins at each pixel.
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What is the Difference Between Color, Multispectral, and Hyperspectral Imaging?

Hyperspectral imaging is a technique that acquires and analyzes a portion of the spectrum of light (often a broader portion than our human eyes can see) in very fine detail instead of simply assigning light into a small number of bins at each pixel. This enables detection of otherwise visually indistinguishable substances because the unique spectral signature of a individual object can be detected and separated from the rest of the image.

HSI-Explained_RGB-MSI-HSI

Humans see color as combinations of red, green, and blue in a very small part of the electromagnetic spectrum. The addition of bands represents a multispectral example, where more than three areas of the spectrum are captured and analyzed. The cells in our retina are sensitive to three colors: red, green, and blue. And combinations of these three colors allow us to see subtle shades of orange, purple, turquoise, and more. However, we cannot distinguish the difference between, say, a pure yellow color and a mixture of red and green colors. Hyperspectral imaging can.

HSI Explained Line Scan Acquisition
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Hyperspectral imaging utilizes hundreds of bands of the spectrum instead of just a few. This enables the high performance of a point-measurement spectrophotometer but at each pixel of an image that could consist of millions of pixels. Headwall’s imaging systems capture a scene one line at a time. The entire scene is imaged by moving the sensor over the area of interest (e.g., on a drone), or by positioning moving objects in front of the sensor (e.g. over a product-laden conveyor belt).

This full-spectrum capability of the technique of hyperspectral imaging allows one to see the chemical (spectral) variation in a scene while also knowing the specific location of this (spatial) variation. There are numerous applications for this ability to visualize and classify chemical variations. These include identifying variations in artwork, crop health, coastline, forests, exposed minerals or rocks for mining exploration, urban and industrial infrastructure, or product quality in production or inspection environments.