Jump to content

Hyperspectral imaging: Difference between revisions

From Wikipedia, the free encyclopedia
Browse history interactively
← Previous editNext edit →
Content deleted Content added
VisualWikitext
→Agriculture: two minor edits wikilinks
→Physics: minor edit of wikilink
Line 33: Line 33:


===Physics===
===Physics===
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.
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 microanalyser|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.
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.

Revision as of 01:36, 5 August 2010

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

Example of a hyperspectral cube

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.[1] However, for many development and validation studies handheld sensors are used.[2]

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 narrow frequency bands, 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, 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.

Differences between hyperspectral and multispectral imaging

Hyperspectral and Multispectral Differences.

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 should not be based on a random or arbitrary "number of bands". A distinction that is based on the type of measurement may be more appropriate.

Multispectral deals with several images at discrete and somewhat narrow bands. The "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 contiguous 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 10-nm wide bands. (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 sensor often have (but not necessarily) a low spatial resolution of several pixels only, a restriction imposed by the high data rate.

Applications

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 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, 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.

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 under way to use imaging spectrometers to detect grape variety, and develop an early warning system for disease outbreaks.[4] Furthermore work is underway to use hyperspectral data to detect the chemical composition of plants[5] which can be used to detect the nutrient and water status of wheat in irrigated systems[6].

Another important area in agriculture is the detection of animal proteins in compound feeds in order to avoid the Bovine spongiform encephalopathy (BSE) or mad-cow disease (MCD). For this, different studies have been done in order to propose alternative tools to the reference method (classical microscopy). One of the first alternatives is the use of NIR microscopy (Infrared microscopy), which combines the advantages of microscopy and NIR. In 2004, the first study relating this problematic with Hyperspectral imaging was published [7] . Hyperspectral libraries are constructed, which are representative of the wide diversity of ingredients usually present in the preparation of compound feeds. 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 ingredient in feed.

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[8] and Noomen[9].

Physics

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.

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 a unique spectral signature in at least a few of the many bands that get scanned.[1]

Advantages and disadvantages

The primary advantages to hyperspectral imaging is that, because an entire spectrum is acquired at each point, the operator needs no prior 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 data storage capacity is necessary since hyperspectral cubes are large multi-dimensional 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 that 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.[1] As a relatively new analytical technique, the full potential of hyperspectral imaging has not yet been realized.

See also

References

  1. ^ a b c Schurmer, J.H., (Dec 2003), Air Force Research Laboratories Technology Horizons
  2. ^ a b Ellis, J., (Jan 2001) Searching for oil seeps and oil-impacted soil with hyperspectral imagery, Earth Observation Magazine.
  3. ^ Smith, R.B. (July 14, 2006), Introduction to hyperspectral imaging with TMIPS, MicroImages Tutorial Web site
  4. ^ 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
  5. ^ 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
  6. ^ Tilling, A.K., et al., (2006) Remote sensing to detect nitrogen and water stress in wheat, The Australian Society of Agronomy
  7. ^ 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
  8. ^ 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
  9. ^ Noomen, M.F. (2007), Hyperspectral reflectance of vegetation affected by underground hydrocarbon gas seepage, Enschede, ITC 151p. ISBN 978-90-8504-671-4.
Retrieved from "https://en.wikipedia.org/w/index.php?title=Hyperspectral_imaging&oldid=377223170"