Information about Hyperspectral

Hyperspectral scanners look at objects using a vast portion of the light spectrum, most notably in the visible and infrared areas of the spectrum. Hyperspectral imaging collects the same picture on many bands of the light spectrum to generate a “datacube” that can reveal objects and information that more limited scanners cannot pick up. Another advance of this form of imaging is that different elements leave unique spectral signatures behind in various bands of the spectrum. Using these specific signatures, it is possible to identify the materials that make up a scanned object. The accuracy of these scanners 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 increased signal-to-noise ratio reduces the reliability of measured features. Hyperspectral images are usually generated from airborne scanners like the NASA’s Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) or from satellites like NASA’s Hyperion. (Schurmer, 2003). The acquisition & processing of hyperspectral images is referred to as imaging spectroscopy. However, for many development and validation studies handheld sensors are used (Ellis, 2001). One of the unique aspects of this form of imaging is the massive amount of data that it generates. Because every image is taken 200 times over, the result of any given scan contains millions of pixels. 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 (Schurmer, 2003).

Application fields

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 oil (Ellis 2001, Smith 2006)) it has now spread into fields as wide-spread as ecology and surveillance. 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.

Mineralogy

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 recent PhD work by vd Werff (2006) and Noomen (2006)

Agriculture

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 spectrometry to detect grape variety, and develop an early warning system for disease outbreaks (Lacar 2001). Furthermore work is underway to use hyperspectral data to detect the chemical composition of plants (Ferwerda, 2005) which can be used to detect the nutrient and water status of wheat in irrigated systems (Tilling, 2006)

Hyperspectral Surveillance

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 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 (Schurmer, 2003).

See also

• Airborne Real-time Cueing Hyperspectral Enhanced Reconnaissance
• Imaging spectroscopy
• Multispectral
• Sensor fusion

References

• Ellis, J. (2001, Jan.). Searching for oil seeps and oil-impacted soil with hyperspectral imagery. Retrieved March 18, 2007, from Earth Observation Magazine Web site: http://www.eomonline.com/Common/currentissues/Jan01/ellis.htm
• Ferwerda, J.G. (2005) Charting the quality of forage : measuring and mapping the variation of chemical components in foliage with hyperspectral remote sensing. Wageningen, Wageningen University, 2005. ITC Dissertation 126, 166 p. ISBN 90-8504-209-7.
• Lacar, F.M.; Lewis, M.M.; Grierson, I.T.; Geoscience and Remote Sensing Symposium, 2001. IGARSS '01. IEEE 2001 International

Volume 6, 9-13 July 2001 Page(s):2875 - 2877 vol.6 Digital Object Identifier 10.1109/IGARSS.2001.978191 PDF

• Noomen, M.F. (2007) Hyperspectral reflectance of vegetation affected by underground hydrocarbon gas seepage. Enschede, ITC, 2007. 151 p. ISBN: 978-90-8504-671-4.
• Schurmer, J.H. (2003, Dec). “Hyperspectral imaging from space”. Retrieved March 18, 2007, from Air Force Research Laboratories Technology Horizons Web site: http://www.afrlhorizons.com/Briefs/Dec03/VS0302.html
• Smith, R.B. (2006, July 14). Introduction to Hyperspectral Imaging with TMIPS. Retrieved March 18, 2007, from MicroImages Tutorial Web site: http://www.microimages.com/getstart/pdf/hyprspec.pdf
• Adam K Tilling, Garry O’Leary, Jelle G Ferwerda, Simon D Jones, Glenn Fitzgerald and Robert Belford., Remote Sensing to Detect Nitrogen and Water Stress in Wheat http://www.regional.org.au/au/asa/2006/plenary/technology/4584_tillingak.htm
• Werff H. vd (2006) Knowledge based remote sensing of complex objects : recognition of spectral and spatial patterns resulting from natural hydrocarbon seepages. Utrecht, Enschede, Utrecht University, ITC, 2006. ITC Dissertation 131, 138 p. ISBN: 90-6164-238-8.