Hyperspectral imaging

Hyperspectral imaging

Hyperspectral imaging collects and processes information from across the electromagnetic spectrum. Unlike the human eye, which just sees visible light, hyperspectral imaging is more like the eyes of the mantis shrimp, which can see visible light as well as from the ultraviolet to infrared. Hyperspectral capabilities enable the mantis shrimp to recognize different types of coral, prey, or predators, all which may appear as the same color to the human eye.

Humans build sensors and processing systems to provide the same type of 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, having the 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 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.Schurmer, J.H., (Dec 2003) " [http://www.afrlhorizons.com/Briefs/Dec03/VS0302.html Hyperspectral imaging from space] ", Air Force Research Laboratories Technology Horizons] However, for many development and validation studies handheld sensors are used.Ellis, J., (Jan 2001) " [http://www.eomonline.com/Common/currentissues/Jan01/ellis.htm Searching for oil seeps and oil-impacted soil with hyperspectral imagery] ," Earth Observation Magazine.]

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 picks up on a large number of fairly small wavelengths, it is possible to identify objects even if said objects 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 and Opticks are two 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.

Differences between Hyperspectral and Multispectral

bands (usually by one sensor). Multispectral is a set of optimally chosen spectral bands that are typically not contiguous and can be collected from multiple sensors.


Hyperspectral remote sensing is used in a wide array of real-life 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 oilSmith, R.B. (July 14, 2006), " [http://www.microimages.com/getstart/pdf/hyprspec.pdf Introduction to hyperspectral imaging with TMIPS] ", MicroImages Tutorial Web site] it has now spread into fields as wide-spread 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, and has been used in a wide variety of ways. 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 costs of acquiring hyperspectral images is typically high, for specific crops and in specific climates hyperspectral remote sensing is used more and more for monitoring the development and health of crops. In Australia work is underway to use imaging spectrometers to detect grape variety, and develop an early warning system for disease outbreaks.Lacar, F.M., et al., " [http://hdl.handle.net/2440/39292 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] Furthermore work is underway to use hyperspectral data to detect the chemical composition of plantsFerwerda, J.G. (2005), " [http://www.itc.nl/library/Papers_2005/phd/ferwerda.pdf 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] which can be used to detect the nutrient and water status of wheat in irrigated systemsTilling, A.K., et al., (2006) " [http://www.regional.org.au/au/asa/2006/plenary/technology/4584_tillingak.htm Remote sensing to detect nitrogen and water stress in wheat] ", The Australian Society of Agronomy]


The original field of development for hyperspectral remote sensing, hyperspectral sensing of minerals is now well developed. Many minerals can be identified from images, and their relation to the presence of valuable minerals such as gold and diamonds is well understood. Currently the move is towards understanding the relation between oil and gas leakages from pipelines and natural wells; their effect on the vegetation and the spectral signatures. Recent work includes the PhD dissertations of Werff [Werff H. (2006), " [http://www.itc.nl/library/papers_2006/phd/vdwerff.pdf 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] and Noomen [Noomen, M.F. (2007), " [http://www.itc.nl/library/papers_2007/phd/noomen.pdf Hyperspectral reflectance of vegetation affected by underground hydrocarbon gas seepage] ", Enschede, ITC 151p. ISBN 978-90-8504-671-4.] .


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 probe microanalyzer (EPMA). Often, multiple techniques (EDS, EELS, CL) are used simultaneously.

In a "normal" mapping experiment, an image of the sample will be made that 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 not-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 made 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 post-processing 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.


Hyperspectral surveillance is the implementation of hyperspectral scanning technology for surveillance purposes. Hyperspectral imaging is particularly useful in military surveillance because of measures that military entities now take to avoid airborne surveillance. Airborne surveillance has been in effect since soldiers used tethered balloons to spy on troops during the American Civil War, and since that time we have learned not only to hide from the naked eye, but to mask our heat signature to blend in to the surroundings and avoid infrared scanning, as well. 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 unique spectral signature in at least a few of the many bands that get scanned.

Advantages and Disadvantages

The primary advantages to hyperspectral imaging is that, because an entire spectrum is acquired at each point, the operator needs no a priori knowledge of the sample, and post-processing allows all available information from the dataset to be mined.

The primary disadvantages are cost and complexity. Fast computers, sensitive detectors, and large data storage capacities are needed for analyzing hyperspectral data. Significant amounts of data storage is necessary due to the fact that hyperspectral cubes are large multi-dimensional datasets, conceivably exceeding hundreds of megabytes. All of these factors greatly increase the cost of acquiring and processing hyperspectral data. Also, one of the hurdles that researchers have had to face has been 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 discovered.

ee also

* Airborne Real-time Cueing Hyperspectral Enhanced Reconnaissance
* Full Spectral Imaging
* Multi-spectral image
* Remote Sensing
* Sensor fusion


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