Hyperspectral imaging, like other spectral imaging, collects and processes information from across the electromagnetic spectrum. Much as the human eye sees visible light in three bands (red, green, and blue), spectral imaging divides the spectrum into many more bands. This technique of dividing images into bands can be extended beyond the visible.
Engineers build sensors and processing systems to provide such capability for application in agriculture, mineralogy, physics, and surveillance. Hyperspectral sensors look at objects using a vast portion of the electromagnetic spectrum. Certain objects leave unique 'fingerprints' across the electromagnetic spectrum. These 'fingerprints' are known as spectral signatures and enable identification of the materials that make up a scanned object. For example, a spectral signature for oil helps mineralogists find new oil fields.
Acquisition and analysis 
Hyperspectral sensors collect information as a set of 'images'. Each image represents a range of the electromagnetic spectrum and is also known as a spectral band. These 'images' are then combined and form a three-dimensional hyperspectral data cube for processing and analysis.
Hyperspectral cubes are generated from airborne sensors like the NASA's Airborne Visible/Infrared Imaging Spectrometer (AVIRIS), or from satellites like NASA's Hyperion. However, for many development and validation studies, handheld sensors are used.
The precision of these sensors is typically measured in spectral resolution, which is the width of each band of the spectrum that is captured. If the scanner detects a large number of fairly narrow frequency bands, it is possible to identify objects even if they are only captured in a handful of pixels. However, spatial resolution is a factor in addition to spectral resolution. If the pixels are too large, then multiple objects are captured in the same pixel and become difficult to identify. If the pixels are too small, then the energy captured by each sensor cell is low, and the decreased signal-to-noise ratio reduces the reliability of measured features.
MicroMSI, Opticks and Envi are three remote sensing applications that support the processing and analysis of hyperspectral data. The acquisition and processing of hyperspectral images is also referred to as imaging spectroscopy.
Different hyperspectral imaging technologies 
There are basically three different techniques used to realize a hyperspectral imaging (HSI) device. A first class of HSI devices is obtained by integrating a dispersive means (a prism or a grating) in an optical system, with the drawback of having the image analyzed per lines (push broom scan) and some mechanical parts integrated in the optical train. The HSI devices belonging to the second class are based on optical band-pass filters (either tuneable or fixed) and the spectrum has to be scanned in steps. Both HSI techniques lead to good quality results but have low efficiency in term of light gathering power and long integration times are necessary to obtain a full HSI cube.
A third method for HSI to obtain the spectrum of a light source is the so-called Fourier transform (FT) spectroscopy; in this technique the FT is applied to the interferogram acquired by a scanning interferometer in order to calculate the spectral composition of the light entering in the interferometer. There are two effects which make this type of spectrometers intrinsically faster:
- There is no spectral scanning and all the spectral components are acquired at the same time (Felgett or multiplex advantage).
- The aperture used in FTIR spectrometers has a larger area than the slits used in dispersive spectrometers, thus enabling higher throughput of radiation (Jacquinot or throughput advantage).
There are two different type of interferometers that could be implemented: either the Michelson interferometer or the Fabry-Perot interferometer.
Differences between hyperspectral and multispectral imaging 
Hyperspectral imaging is part of a class of techniques commonly referred to as spectral imaging or spectral analysis. Hyperspectral imaging is related to multispectral imaging. The distinction between hyper- and multi-spectral is sometimes based on an arbitrary "number of bands" or on the type of measurement, depending on what is appropriate to the purpose.
Multispectral imaging deals with several images at discrete and somewhat narrow bands. Being "discrete and somewhat narrow" is what distinguishes multispectral in the visible from color photography. A multispectral sensor may have many bands covering the spectrum from the visible to the longwave infrared. Multispectral images do not produce the "spectrum" of an object. Landsat is an excellent example.
Hyperspectral deals with imaging narrow spectral bands over a continuous spectral range, and produce the spectra of all pixels in the scene. So a sensor with only 20 bands can also be hyperspectral when it covers the range from 500 to 700 nm with 20 bands each 10 nm wide. (While a sensor with 20 discrete bands covering the VIS, NIR, SWIR, MWIR, and LWIR would be considered multispectral.)
'Ultraspectral' could be reserved for interferometer type imaging sensors with a very fine spectral resolution. These sensors often have (but not necessarily) a low spatial resolution of several pixels only, a restriction imposed by the high data rate.
Hyperspectral remote sensing is used in a wide array of applications. Although originally developed for mining and geology (the ability of hyperspectral imaging to identify various minerals makes it ideal for the mining and oil industries, where it can be used to look for ore and oil), it has now spread into fields as widespread as ecology and surveillance, as well as historical manuscript research, such as the imaging of the Archimedes Palimpsest. This technology is continually becoming more available to the public. Organizations such as NASA and the USGS have catalogues of various minerals and their spectral signatures, and have posted them online to make them readily available for researchers.
Although the cost of acquiring hyperspectral images is typically high, for specific crops and in specific climates, hyperspectral remote sensing use is increasing for monitoring the development and health of crops. In Australia, work is under way to use imaging spectrometers to detect grape variety and develop an early warning system for disease outbreaks. Furthermore, work is underway to use hyperspectral data to detect the chemical composition of plants, which can be used to detect the nutrient and water status of wheat in irrigated systems.
Another application in agriculture is the detection of animal proteins in compound feeds to avoid bovine spongiform encephalopathy (BSE), also known as mad-cow disease. Different studies have been done to propose alternative tools to the reference method of detection, (classical microscopy). One of the first alternatives is near infrared microscopy (NIR), which combines the advantages of microscopy and NIR. In 2004, the first study relating this problem with hyperspectral imaging was published. Hyperspectral libraries that are representative of the diversity of ingredients usually present in the preparation of compound feeds were constructed. These libraries can be used together with chemometric tools to investigate the limit of detection, specificity and reproducibility of the NIR hyperspectral imaging method for the detection and quantification of animal ingredients in feed.
Geological samples, such as drill cores, can be rapidly mapped for nearly all minerals of commercial interest with hyperspectral imaging. Fusion of SWIR and LWIR spectral 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 remote sensing of minerals is well developed. Many minerals can be identified from airborne images, and their relation to the presence of valuable minerals, such as gold and diamonds, is well understood. Currently, progress is towards understanding the relationship between oil and gas leakages from pipelines and natural wells, and their effects on the vegetation and the spectral signatures. Recent work includes the PhD dissertations of Werff and Noomen.
Hyperspectral 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. Aerial surveillance was used by French soldiers using tethered balloons to spy on troop movements during the French Revolutionary Wars, and since that time, soldiers have learned not only to hide from the naked eye, but also to mask their heat signatures to blend into the surroundings and avoid infrared scanning. The idea that drives hyperspectral surveillance is that hyperspectral scanning draws information from such a large portion of the light spectrum that any given object should have a unique spectral signature in at least a few of the many bands that are scanned. The SEALs from DEVGRU who killed Osama bin Laden in May 2011 used this technology while conducting the raid (Operation Neptune's Spear) on Osama bin Laden's compound in Abbottabad, Pakistan.
Traditionally, commercially available thermal infrared hyperspectral imaging systems have needed liquid nitrogen or helium cooling, which has made them impractical for most surveillance applications. In 2010, Specim introduced a thermal infrared hyperspectral camera that can be used for outdoor surveillance and UAV applications without an external light source such as the sun or the moon.
Physicists use an electron microscopy technique that involves microanalysis using either energy-dispersive X-ray spectroscopy (EDS), electron energy loss spectroscopy (EELS), infrared spectroscopy(IR), Raman spectroscopy, or cathodoluminescence (CL) spectroscopy, in which the entire spectrum measured at each point is recorded. EELS hyperspectral imaging is performed in a scanning transmission electron microscope (STEM); EDS and CL mapping can be performed in STEM as well, or in a scanning electron microscope or electron microprobe (also called an electron probe microanalyzer or EPMA). Often, multiple techniques (EDS, EELS, CL) are used simultaneously.
In a "normal" mapping experiment, an image of the sample is simply the intensity of a particular emission mapped in an XY raster. For example, an EDS map could be made of a steel sample, in which iron X-ray intensity is used for the intensity grayscale of the image. Dark areas in the image would indicate non-iron-bearing impurities. This could potentially give misleading results; if the steel contained tungsten inclusions, for example, the high atomic number of tungsten could result in bremsstrahlung radiation that would make the iron-free areas appear to be rich in iron.
By hyperspectral mapping, instead, the entire spectrum at each mapping point is acquired, and a quantitative analysis can be performed by computer postprocessing of the data, and a quantitative map of iron content produced. This would show which areas contained no iron, despite the anomalous X-ray counts caused by bremsstrahlung. Because EELS core-loss edges are small signals on top of a large background, hyperspectral imaging allows large improvements to the quality of EELS chemical maps.
Similarly, in CL mapping, small shifts in the peak emission energy could be mapped, which would give information regarding slight chemical composition changes or changes in the stress state of a sample.
In astronomy, hyperspectral imaging is used to determine a spatially-resolved spectral image. Since a spectrum is an important diagnostic, having a spectrum for each pixel allows more science cases to be addressed. In astronomy, this technique is commonly referred to as integral field spectroscopy, and examples of this technique include FLAMES and SINFONI on the Very Large Telescope, but also the Advanced CCD Imaging Spectrometer on Chandra X-ray Observatory uses this technique.
Chemical Imaging 
At war, Chemical Warfare Agents (CWAs) and Toxic Industrial Compounds (TICs) attacks are some of the most malicious threats for any field troops. In fact, soldiers can be exposed to a wide variety of chemical hazards both on and off the battlefield. These threats are mostly invisible and very hard to detect. However, the hyperspectral imaging technology offers a unique standoff detection, identification and imaging capability for such chemical warfare agents. The Telops Hyper-Cam, introduced in 2005, has demonstrated this capability in multiple field CWA-TIC measurement campaigns. With its standoff capability, the Hyper-Cam enables on-the-field detection and identification of multiple CWAs and TICs in various environments, at distances up to 5 km and with concentrations as low as a few ppm.
Most countries require continuous monitoring of emissions produced by coal and oil-fired power plants, municipal and hazardous waste incinerators, cement plants, as well as many other types of industrial sources. This monitoring is usually performed using extractive sampling systems coupled with infrared spectroscopy techniques. Some recent standoff measurements performed allowed the evaluation of the air quality but not many remote independent methods allow for low uncertainty measurements. The Telops Hyper-Cam, an infrared hyperspectral imager, now offers the possibility of obtaining a complete image of emissions resulting from industrial smokestacks from a remote location, without any need for extractive sampling systems. Emission quantification measurements have been achieved with the Hyper-Cam which can now be used to independently, safely and rapidly identify and quantify polluting emissions from a remote location.
Advantages and disadvantages 
The 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 also 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.
The primary disadvantages are cost and complexity. Fast computers, sensitive detectors, and large data storage capacities are needed for analyzing hyperspectral data. Significant data storage capacity is necessary since hyperspectral cubes are large, multidimensional datasets, potentially exceeding hundreds of megabytes. All of these factors greatly increase the cost of acquiring and processing hyperspectral data. Also, one of the hurdles researchers have had to face is finding ways to program hyperspectral satellites to sort through data on their own and transmit only the most important images, as both transmission and storage of that much data could prove difficult and costly. As a relatively new analytical technique, the full potential of hyperspectral imaging has not yet been realized.
See also 
- Airborne Real-time Cueing Hyperspectral Enhanced Reconnaissance
- Full spectral imaging
- Multispectral image
- Metamerism (color), the perceptual equivalence that hyperspectral imaging overcomes
- Chemical imaging
- Remote sensing
- Sensor fusion
- ERDAS IMAGINE
- Liquid Crystal Tunable Filter
- Schurmer, J.H., (Dec 2003), Air Force Research Laboratories Technology Horizons
- Ellis, J., (Jan 2001) Searching for oil seeps and oil-impacted soil with hyperspectral imagery, Earth Observation Magazine.
- Smith, R.B. (July 14, 2006), Introduction to hyperspectral imaging with TMIPS, MicroImages Tutorial Web site
- Lacar, F.M., et al., Use of hyperspectral imagery for mapping grape varieties in the Barossa Valley, South Australia , Geoscience and remote sensing symposium (IGARSS'01) - IEEE 2001 International, vol.6 2875-2877p. doi:10.1109/IGARSS.2001.978191
- Ferwerda, J.G. (2005), Charting the quality of forage: measuring and mapping the variation of chemical components in foliage with hyperspectral remote sensing, Wageningen University, ITC Dissertation 126, 166p. ISBN 90-8504-209-7
- Tilling, A.K., et al., (2006) Remote sensing to detect nitrogen and water stress in wheat, The Australian Society of Agronomy
- Fernández Pierna, J.A., et al., 'Combination of Support Vector Machines (SVM) and Near Infrared (NIR) imaging spectroscopy for the detection of meat and bone meat (MBM) in compound feeds' Journal of Chemometrics 18 (2004) 341-349
- Holma, H., (May 2011), Thermische Hyperspektralbildgebung im langwelligen Infrarot, Photonik
- Werff H. (2006), Knowledge based remote sensing of complex objects: recognition of spectral and spatial patterns resulting from natural hydrocarbon seepages, Utrecht University, ITC Dissertation 131, 138p. ISBN 90-6164-238-8
- Noomen, M.F. (2007), Hyperspectral reflectance of vegetation affected by underground hydrocarbon gas seepage, Enschede, ITC 151p. ISBN 978-90-8504-671-4.
- "Fleurus (Municipality, Province of Hainaut, Belgium)". CRW Flags Inc.. Retrieved 2010-04-21
- Marc Ambinder (May 3, 2011). "The secret team that killed bin Laden". National Journal. Retrieved September 12, 2012.
- Frost&Sullivan, Technical Insights, Aerospace&Defence (Feb 2011): World First Thermal Hyperspectral Camera for Unmanned Aerial Vehicles
- Specim's Owl sees an invisible object and identifies its materials even in a pitch-dark night.
- "FLAMES - Fibre Large Array Multi Element Spectrograph". ESO. Retrieved 30 November 2012.
- "SINFONI - Spectrograph for INtegral Field Observations in the Near Infrared". ESO. Retrieved 30 November 2012.
- M. Chamberland, V. Farley, A. Vallières, L. Belhumeur, A. Villemaire, J. Giroux et J. Legault, "High-Performance Field-Portable Imaging Radiometric Spectrometer Technology For Hyperspectral imaging Applications," Proc. SPIE 5994, 59940N, September 2005.
- Farley, V., Chamberland, M., Lagueux, P., et al., "Chemical agent detection and identification with a hyperspectral imaging infrared sensor," Proceedings of SPIE Vol. 6661, 66610L (2007).
- Kevin C. Gross, Kenneth C Bradley and Glen P. Perram, "Remote identification and quantification of industrial smokestack effluents via imaging Fourier-transform spectroscopy," Environmental Sci Tech, 44, 9390-9397, Oct 2010.
- Tremblay, P., Savary, S., Rolland, M., et al., "Standoff gas identification and quantification from turbulent stack plumes with an imaging Fourier-transform spectrometer," Proceedings of SPIE Vol. 7673, 76730H (2010).
- A. Picon, O. Ghita, P.F. Whelan, P. Iriondo (2009), Spectral and Spatial Feature Integration for Classification of Non-ferrous Materials in Hyper-spectral Data,IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, VOL. 5, NO. 4, NOVEMBER 2009