16 Facts About Hyperspectral imaging

Hyperspectral imaging collects and processes information from across the electromagnetic spectrum.

The goal of hyperspectral imaging is to obtain the spectrum for each pixel in the image of a scene, with the purpose of finding objects, identifying materials, or detecting processes.

Hyperspectral imaging sensors look at objects using a vast portion of the electromagnetic spectrum.

The most prominent benefits of these snapshot hyperspectral imaging systems are the snapshot advantage and shorter acquisition time.

Hyperspectral imaging is part of a class of techniques commonly referred to as spectral imaging or spectral analysis.

Hyperspectral imaging remote sensing is used in a wide array of applications.

Hyperspectral imaging can provide information about the chemical constituents of materials which makes it useful for waste sorting and recycling.

Work is underway to use hyperspectral imaging to detect “sugar ends, ” “hollow heart” and “common scab, ” conditions that plague potato processors.

Fusion of SWIR and LWIR spectral Hyperspectral imaging is standard for the detection of minerals in the feldspar, silica, calcite, garnet, and olivine groups, as these minerals have their most distinctive and strongest spectral signature in the LWIR regions.

Hyperspectral imaging surveillance is the implementation of hyperspectral scanning technology for surveillance purposes.

Hyperspectral imaging is particularly useful in military surveillance because of countermeasures that military entities now take to avoid airborne surveillance.

Hyperspectral imaging has shown potential to be used in facial recognition purposes.

Facial recognition algorithms using hyperspectral imaging have been shown to perform better than algorithms using traditional imaging.

Traditionally, commercially available thermal infrared hyperspectral imaging systems have needed liquid nitrogen or helium cooling, which has made them impractical for most surveillance applications.

Primary advantage to hyperspectral imaging is that, because an entire spectrum is acquired at each point, the operator needs no prior knowledge of the sample, and postprocessing allows all available information from the dataset to be mined.

Hyperspectral imaging can take advantage of the spatial relationships among the different spectra in a neighbourhood, allowing more elaborate spectral-spatial models for a more accurate segmentation and classification of the image.