Hyperspectral imaginglike other spectral imagingcollects and processes information from across the electromagnetic spectrum. The goal of hyperspectral imaging is to obtain the spectrum for each pixel in the image of a scene, with the purpose of finding objects, identifying materials, or detecting processes.
There are push broom scanners and the related whisk broom scannerswhich read images over time, and snapshot hyperspectral imagingwhich uses a staring array to generate an image in an instance. Whereas the human eye sees color of visible light in mostly three bands red, green, and bluespectral imaging divides the spectrum into many more bands. This technique of dividing images into bands can be extended beyond the visible. In hyperspectral imaging, the recorded spectra have fine wavelength resolution and cover a wide range of wavelengths.
Hyperspectral imaging measures contiguous spectral bands, as opposed to multispectral imaging which measures spaced spectral bands. Engineers build hyperspectral sensors and processing systems for applications in astronomy, agriculture, biomedical imaging, geosciences, physics, and surveillance. Hyperspectral sensors look at objects using a vast portion of the electromagnetic spectrum. Certain objects leave unique 'fingerprints' in the electromagnetic spectrum.
Known as spectral signatures, these 'fingerprints' enable identification of the materials that make up a scanned object. For example, a spectral signature for oil helps geologists find new oil fields. Figuratively speaking, hyperspectral sensors collect information as a set of 'images'. Each image represents a narrow wavelength range of the electromagnetic spectrum, also known as a spectral band.
Technically speaking, there are four ways for sensors to sample the hyperspectral cube: Spatial scanning, spectral scanning, snapshot imaging,   and spatio-spectral scanning. 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. The acquisition and processing of hyperspectral images is also referred to as imaging spectroscopy or, with reference to the hyperspectral cube, as 3D spectroscopy.
The choice of technique depends on the specific application, seeing that each technique has context-dependent advantages and disadvantages. Hyperspectral imaging HSI devices for spatial scanning obtain slit spectra by projecting a strip of the scene onto a slit and dispersing the slit image with a prism or a grating.
These systems have the drawback of having the image analyzed per lines with a push broom scanner and also having some mechanical parts integrated into the optical train. With these line-scan systemsthe spatial dimension is collected through platform movement or scanning.
Nonetheless, line-scan systems are particularly common in remote sensing, where it is sensible to use mobile platforms.
Line-scan systems are also used to scan materials moving by on a conveyor belt. A special case of line scanning is point scanning with a whisk broom scannerwhere a point-like aperture is used instead of a slit, and the sensor is essentially one-dimensional instead of 2-D.
In spectral scanning, each 2-D sensor output represents a monochromatic 'single-colored'spatial xy map of the scene. HSI devices for spectral scanning are typically based on optical band-pass filters either tuneable or fixed.
The scene is spectrally scanned by exchanging one filter after another while the platform must be stationary. Nonetheless, there is the advantage of being able to pick and choose spectral bands, and having a direct representation of the two spatial dimensions of the scene.
HSI devices for non-scanning yield the full datacube at once, without any scanning. Figuratively speaking, a single snapshot represents a perspective projection of the datacube, from which its three-dimensional structure can be reconstructed. A number of systems have been designed, including computed tomographic imaging spectrometry CTISfiber-reformatting imaging spectrometry FRISintegral field spectroscopy with lenslet arrays IFS-Lmulti-aperture integral field spectrometer Hyperpixel Arrayintegral field spectroscopy with image slicing mirrors IFS-Simage-replicating imaging spectrometry IRISfilter stack spectral decomposition FSSDcoded aperture snapshot spectral imaging CASSIimage mapping spectrometry IMSand multispectral Sagnac interferometry MSI.
Scanning can be achieved by moving the whole system relative to the scene, by moving the camera alone, or by moving the slit alone.
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Spatiospectral scanning unites some advantages of spatial and spectral scanning, thereby alleviating some of their disadvantages. 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 imaging in the visible wavelength 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 of multispectral imaging. Hyperspectral deals with imaging narrow spectral bands over a continuous spectral range, producing the spectra of all pixels in the scene.
While a sensor with 20 discrete bands covering the VIS, NIR, SWIR, MWIR, and LWIR would be considered multispectral. 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 thanksgiving week stock market hours 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 amake extra money 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 Australiawork is under way to use imaging spectrometers to detect grape variety and develop an early warning system for disease outbreaks. Another application in agriculture is the detection of animal proteins in compound feeds to avoid bovine spongiform encephalopathy BSEalso 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 NIRwhich combines the advantages of microscopy and NIR. Inthe first study relating this problem with hyperspectral imaging was published. These libraries can be used together with chemometric underground system binary options 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.
The metabolic hyperspectral camera will detect a drop in oxygen consumption in the retina, which indicates potential disease. An ophthalmologist will then be able to treat the retina with injections to prevent any potential damage. In the food processing industry, hyperspectral imaging, combined with intelligent software, enables digital sorters also called optical sorters to identify and remove defects and foreign material FM that are invisible to traditional camera and laser sorters.
Adopting hyperspectral imaging on digital sorters achieves non-destructive, percent inspection in-line at full production volumes. The recent commercial adoption of hyperspectral sensor-based food sorters is most advanced in the nut industry where installed systems maximize the removal of stones, shells and other foreign material FM and extraneous vegetable matter EVM from walnuts, pecans, almonds, pistachios, peanuts and other nuts. Here, improved product quality, low false reject rates and the ability to handle high incoming defect loads often justify the cost of the technology.
Commercial adoption of hyperspectral sorters is also advancing at a fast pace in the potato processing industry where the technology promises to solve a number of outstanding product quality problems. Geological samples, such as drill corescan 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 feldsparsilicacalcitegarnetand olivine groups, as these minerals have their most distinctive deferred stock options canada 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.
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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, exchange rate indian rupee to us dollar history 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 indikator forex terbaru gratis a few of the many bands that are scanned.
The SEALs from NSWDG who killed Osama bin Laden in May used this technology while conducting the raid Operation Neptune's Spear on Osama bin Laden's compound in AbbottabadPakistan.
Traditionally, commercially available thermal infrared hyperspectral imaging systems have needed liquid nitrogen or learn to trade penny stocks free cooling, which has made them impractical for most surveillance applications.
InSpecim introduced a thermal infrared hyperspectral camera that can be used for outdoor surveillance and UAV best dividend paying stocks in the world 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 EDSelectron energy loss spectroscopy EELSinfrared spectroscopy IRRaman spectroscopyor cathodoluminescence CL spectroscopy, in which the entire spectrum measured at each point is recorded. 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 spectroscopyand examples of this technique include FLAMES  and SINFONI  on the Very Large Telescopebut also the Advanced CCD Imaging Spectrometer on Chandra X-ray Observatory uses this technique. Soldiers can be exposed to a wide variety of chemical hazards.
These threats are mostly invisible but detectable by hyperspectral imaging technology. 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 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. From Wikipedia, the free encyclopedia. Techniques for Spectral Detection and Classification.
Techniques and Applications of Hyperspectral Image Analysis. Archived from the original on 20 September Retrieved 2 February Journal of Biomedical Optics. One-shot camera obtains simultaneous hyperspectral data". Elsevier — Experimental Eye Research. Retrieved 6 September Non-Destructive Detection of Hollow Heart in Potatoes Using Hyperspectral Imaging PDF.
Common scab detection on potatoes using an infrared hyperspectral imaging system. Archived from the original on May 24, Retrieved September 12, Retrieved 30 November Legault, "High-Performance Field-Portable Imaging Radiometric Spectrometer Technology For Hyperspectral imaging Applications," Proc.
SPIEN, September 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,Oct Retrieved from " https: Satellite meteorology and remote sensing Materials science Imaging Remote sensing Infrared imaging Surveillance Spectroscopy Infrared spectroscopy Military electronics.
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