Hyperspectral imaging is the analysis of images using a large number of channels (corresponding to spectrum intervals). The distinction between hyperspectral and multispectral is not defined by a set number of spectral bands. It is best defined by the manner in which the data is collected. Hyperspectral data is a set of contiguous bands (usually by one sensor). Multispectral is a set of optimally chosen spectral bands that are typically not contiguous (usually by multiple sensors). Capturing the same object on many bands of the spectrum to generate a data cube can reveal objects and information that more limited scanners can not pick up.
Imaging spectroscopy
The acquisition and processing of hyperspectral images is referred to as imaging spectroscopy.
Acquisition
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.[1] However, for many development and validation studies handheld sensors are used.[2]
Hyperspectral scanners look at objects using a vast portion of the light spectrum, most notably in the visible and infrared areas of the spectrum. Certain objects 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 precision 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.
Analysis
One of the unique aspects of this form of imaging is the massive amount of data that it generates. Because every image is taken many 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.[1]
Application
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[2][3] 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 PhD dissertations of Werff[4] and Noomen[5].
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 spectrometers to detect grape variety, and develop an early warning system for disease outbreaks.[6] Furthermore work is underway to use hyperspectral data to detect the chemical composition of plants[7] which can be used to detect the nutrient and water status of wheat in irrigated systems[8]
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 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.[1]
See also
Analysis Software
References
- ^ a b c Schurmer, J.H., (Dec 2003) Hyperspectral imaging from space, Air Force Research Laboratories Technology Horizons
- ^ a b 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
- ^ 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.
- ^ Lacar, F.M., et al., 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
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