Hyperspectral imaging (HSI) is an analytical technique based on spectroscopy, which collects thousands of images at different wavelengths for the same spatial area. While the human eye has only three color receptors in the blue, green, and red, HSI measures the continuous spectrum of light for each pixel of the scene with fine wavelength resolution not only in the visible, but also in the near-infrared (NIR). The light striking each pixel is broken down into many different spectral bands to provide more information on what is imaged. The collected data form a 3D hyperspectral cube, in which two dimensions represent the spatial extent of the scene and the third its spectral content.
From an article in Laser Focus World by Jeremy Picot-Clemente.
HSI is part of a class of techniques commonly referred to as spectral imaging or spectral analysis, which also includes multispectral imaging (MSI). The main difference between these two imaging systems is that HSI uses continuous and contiguous ranges of wavelengths (e.g., 400 to 1100 nm in steps of 1 nm), whereas MSI uses a subset of targeted wavelengths at chosen locations (e.g., 400 to 1100 nm in steps of 20 nm). MSI sensors usually have between 3 and 10 different band measurements in each pixel, while HSI images can contain thousands of narrower, but contiguous spectral bands. As a result, HSI sensors contain much more data than MSI sensors. MSI can be used to map forested areas, for example, while hyperspectral imagery can be used to map tree species within the forest.
Although MSI can be thought of as a reduced subset of HSI, the two technologies are complementary and choosing between them comes down to application requirements. In the visible channel, for example, MSI can be used to identify the contour of fruits and spot difficult-to-see bruises on fruits such as blueberries. But if the aim is to obtain a very detailed analysis of the fat content of avocados, this can only be done with HSI technology.
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