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| [[Image:ev26221 KlyuchevskayaSopka.A2004012.0035.500m.jpg|thumb||250px|Ash plumes on Kamchatka Peninsula, eastern Russia. A [[MODIS]] image.]]
| | This is a preview for the new '''MathML rendering mode''' (with SVG fallback), which is availble in production for registered users. |
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| '''Imaging spectroscopy''' (also '''[[Hyperspectral imaging|hyperspectral]]''' or '''[[spectral imaging]]''' or [[chemical imaging]]) | | If you would like use the '''MathML''' rendering mode, you need a wikipedia user account that can be registered here [[https://en.wikipedia.org/wiki/Special:UserLogin/signup]] |
| is similar to [[color photography]], but each pixel acquires many bands of light intensity data from the spectrum, instead of just the three bands of the [[RGB color model]]. More precisely, it is the simultaneous acquisition of spatially [[Image registration|coregistered images]] in many [[electromagnetic spectrum|spectrally]] contiguous [[frequency band|bands]].
| | * Only registered users will be able to execute this rendering mode. |
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| Some spectral images contain only a few [[image plane]]s of a spectral [[data cube]], while others are better thought of as full spectra at every location in the image. For example, [[solar physics|solar physicist]]s use the [[spectroheliograph]] to make images of the [[Sun]] built up by scanning the slit of a spectrograph, to study the behavior of surface features on the Sun; such a spectroheliogram may have a spectral resolution of over 100,000 (<math>\lambda / \Delta \lambda</math>) and be used to measure local motion (via the [[Doppler shift]]) and even the [[magnetic field]] (via the [[Zeeman splitting]] or [[Hanle effect]]) at each location in the image plane. The [[multispectral image]]s collected by the [[Opportunity rover]], in contrast, have only four wavelength bands and hence are only a little more than [[color photography|3-color image]]s.
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| To be scientifically useful, such measurement should be done using an internationally recognized system of units.
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| One application is spectral [[geophysical imaging]], which allows quantitative and qualitative characterization of the surface and of the [[Earth's atmosphere|atmosphere]], using geometrically [[Coherence (physics)|coherent]] spectrodirectional{{Huh?|date=February 2013}} [[Radiometry|radiometric]] measurements. These measurements can then be used for unambiguous direct and indirect identification of surface materials and atmospheric trace gases, the measurement of their relative concentrations, subsequently the assignment of the proportional contribution of mixed pixel signals (e.g., the spectral unmixing problem), the derivation of their spatial distribution (mapping problem), and finally their study over time (multi-temporal analysis). The [[Moon Mineralogy Mapper]] on [[Chandrayaan-1]] was an [[imaging spectrometer]].<ref>{{cite news|title=Large quantities of water found on the Moon|url=http://www.telegraph.co.uk/science/space/6224974/Large-quantities-of-water-found-on-the-Moon.html|newspaper=The Telegraph|date=24 Sep 2009}}</ref>
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| == Background == | | '''source''' |
| In 1704, [[Isaac Newton|Sir Isaac Newton]] demonstrated that white light could be split up into component colours. The subsequent [[history of spectroscopy]] led to precise measurements and provided the empirical foundations for atomic and [[molecular physics]] (Born & Wolf, 1999). Significant achievements in imaging spectroscopy are attributed to airborne instruments, particularly arising in the early 1980s and 1990s (Goetz et al., 1985; Vane et al., 1984). However, it was not until 1999 that the first imaging spectrometer was launched in space (the [[MODIS|NASA Moderate-resolution Imaging Spectroradiometer]], or MODIS).
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| Terminology and definitions evolve over time. At one time, >10 spectral bands sufficed to justify the term "[[imaging spectrometer]]" but presently the term is seldom defined by a total minimum number of spectral bands, rather by a contiguous (or redundant) statement of [[spectral bands]].
| | <span style="color: red">Follow this [https://en.wikipedia.org/wiki/Special:Preferences#mw-prefsection-rendering link] to change your Math rendering settings.</span> You can also add a [https://en.wikipedia.org/wiki/Special:Preferences#mw-prefsection-rendering-skin Custom CSS] to force the MathML/SVG rendering or select different font families. See [https://www.mediawiki.org/wiki/Extension:Math#CSS_for_the_MathML_with_SVG_fallback_mode these examples]. |
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| The term [[hyperspectral]] imaging is sometimes used interchangeably with imaging spectroscopy. Due to its heavy use in military related applications, the civil world has established a slight preference for using the term imaging spectroscopy.
| | ==Demos== |
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| == Unmixing ==
| | Here are some [https://commons.wikimedia.org/w/index.php?title=Special:ListFiles/Frederic.wang demos]: |
| Hyperspectral data is often used to determine what materials are present in a scene. Materials of interest could include roadways, vegetation, and specific targets (i.e. pollutants, hazardous materials, etc.). Trivially, each pixel of a hyperspectral image could be compared to a material database to determine the type of material making up the pixel. However, many hyperspectral imaging platforms have low resolution (>5m per pixel) causing each pixel to be a mixture of several materials. The process of unmixing one of these 'mixed' pixels is called hyperspectral image unmixing or simply hyperspectral unmixing.
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| === Models ===
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| A solution to hyperspectral unmixing is to reverse the mixing process. Generally, two models of mixing are assumed: linear and nonlinear.
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| Linear mixing models the ground as being flat and incident sunlight on the ground causes the materials to radiate some amount of the incident energy back to the sensor. Each pixel then, is modeled as a linear sum of all the radiated energy curves of materials making up the pixel. Therefore, each material contributes to the sensor's observation in a positive linear fashion. Additionally, a conservation of energy constraint is often observed thereby forcing the weights of the linear mixture to sum to one in addition to being positive. The model can be described mathematically as follows:
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| :<math>p = A*x\,</math> | | * accessibility: |
| | ** Safari + VoiceOver: [https://commons.wikimedia.org/wiki/File:VoiceOver-Mac-Safari.ogv video only], [[File:Voiceover-mathml-example-1.wav|thumb|Voiceover-mathml-example-1]], [[File:Voiceover-mathml-example-2.wav|thumb|Voiceover-mathml-example-2]], [[File:Voiceover-mathml-example-3.wav|thumb|Voiceover-mathml-example-3]], [[File:Voiceover-mathml-example-4.wav|thumb|Voiceover-mathml-example-4]], [[File:Voiceover-mathml-example-5.wav|thumb|Voiceover-mathml-example-5]], [[File:Voiceover-mathml-example-6.wav|thumb|Voiceover-mathml-example-6]], [[File:Voiceover-mathml-example-7.wav|thumb|Voiceover-mathml-example-7]] |
| | ** [https://commons.wikimedia.org/wiki/File:MathPlayer-Audio-Windows7-InternetExplorer.ogg Internet Explorer + MathPlayer (audio)] |
| | ** [https://commons.wikimedia.org/wiki/File:MathPlayer-SynchronizedHighlighting-WIndows7-InternetExplorer.png Internet Explorer + MathPlayer (synchronized highlighting)] |
| | ** [https://commons.wikimedia.org/wiki/File:MathPlayer-Braille-Windows7-InternetExplorer.png Internet Explorer + MathPlayer (braille)] |
| | ** NVDA+MathPlayer: [[File:Nvda-mathml-example-1.wav|thumb|Nvda-mathml-example-1]], [[File:Nvda-mathml-example-2.wav|thumb|Nvda-mathml-example-2]], [[File:Nvda-mathml-example-3.wav|thumb|Nvda-mathml-example-3]], [[File:Nvda-mathml-example-4.wav|thumb|Nvda-mathml-example-4]], [[File:Nvda-mathml-example-5.wav|thumb|Nvda-mathml-example-5]], [[File:Nvda-mathml-example-6.wav|thumb|Nvda-mathml-example-6]], [[File:Nvda-mathml-example-7.wav|thumb|Nvda-mathml-example-7]]. |
| | ** Orca: There is ongoing work, but no support at all at the moment [[File:Orca-mathml-example-1.wav|thumb|Orca-mathml-example-1]], [[File:Orca-mathml-example-2.wav|thumb|Orca-mathml-example-2]], [[File:Orca-mathml-example-3.wav|thumb|Orca-mathml-example-3]], [[File:Orca-mathml-example-4.wav|thumb|Orca-mathml-example-4]], [[File:Orca-mathml-example-5.wav|thumb|Orca-mathml-example-5]], [[File:Orca-mathml-example-6.wav|thumb|Orca-mathml-example-6]], [[File:Orca-mathml-example-7.wav|thumb|Orca-mathml-example-7]]. |
| | ** From our testing, ChromeVox and JAWS are not able to read the formulas generated by the MathML mode. |
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| where <math>p</math> represents a pixel observed by the sensor, <math>A</math> is a matrix of material reflectance signatures (each signature is a column of the matrix), and <math>x</math> is the proportion of material present in the observed pixel. This type of model is also referred to as a [[simplex]].
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| With <math>x</math> satisfying the two constraints:
| | To test the '''MathML''', '''PNG''', and '''source''' rendering modes, please go to one of the following test pages: |
| 1. Abundance Nonnegativty Constraint (ANC) - each element of x is positive.
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| 2. Abundance Sum-to-one Constraint (ASC) - the elements of x must sum to one.
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| Non-linear mixing results from multiple scattering often due to non-flat surface such as buildings and vegetation.
| | *[[Inputtypes|Inputtypes (private Wikis only)]] |
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| ===Unmixing (Endmember Detection) Algorithms===
| | ==Bug reporting== |
| | | If you find any bugs, please report them at [https://bugzilla.wikimedia.org/enter_bug.cgi?product=MediaWiki%20extensions&component=Math&version=master&short_desc=Math-preview%20rendering%20problem Bugzilla], or write an email to math_bugs (at) ckurs (dot) de . |
| There are many algorithms to unmix hyperspectral data each with their own strengths and weaknesses. Many algorithms assume that pure pixels (pixels which contain only one materials) are present in a scene.
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| Some algorithms to perform unmixing are listed below:
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| * Pixel Purity Index (PPI) - Works by projecting each pixel onto one vector from a set of random vectors spanning the reflectance space. A pixel receives a score when it represent an extremum of all the projections. Pixels with the highest scores are deemed to be spectrally pure.
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| * NFINDR
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| * Gift Wrapping Algorithm
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| * Independent Component Analysis Endmember Extraction Algorithm (ICA-EEA) - Works by assuming that pure pixels occur independently than mixed pixels. Assumes pure pixels are present. | |
| * Vertex Component Analysis (VCA) - Works on the fact that the affine transformation of a simplex is another simplex which helps to find hidden (folded) verticies of the simplex. Assumes pure pixels are present.
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| * Principal component analysis -(PCA) could also be used to determine endmembers, projection on principal axes could permit endmember selection [ Smith,Johnson et Adams (1985), Bateson et Curtiss (1996) ]
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| * Multi Endmembers Spatial Mixture Analysis (MESMA) based on the SMA algorithm
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| * Spectral Phasor Analysis (SPA) based on Fourier transformation of spectra and plotting them on a 2D plot.
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| Non-linear unmixing algortithm also exist ([[support vector machine]]s (SVM)) or Analytical Neural Network (ANN).
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| Probabilistics methods have also been attempted to unmix pixel through Monte Carlo Unmixing (MCU) algorithm.
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| ===Abundance Maps===
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| Once the fundamental materials of a scene are determined, it is often useful to construct an abundance map of each material which displays the fractional amount of material present at each pixel. Often [[linear programming]] is done to observed ANC and ASC.{{Disambiguation needed|date=February 2013}}
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| ==Sensors==
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| *[[MODIS]] — on board [[Earth Observing System|EOS]] [[Terra (satellite)|Terra]] and [[Aqua (satellite)|Aqua]] platforms | |
| *[[MERIS]] — on board [[Envisat]]
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| *Several commercial manufacturers for laboratory, ground based, aerial, or industrial imaging spectrographs
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| == See also == | |
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| * [[Remote sensing]]
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| * [[Hyperspectral imaging]]
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| * [[Full Spectral Imaging]]
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| * [[List of Earth observation satellites]]
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| * [[Chemical Imaging]]
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| * [[Imaging spectrometer]]
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| * [[Microscopy#Infrared microscopy|Infrared Microscopy]]
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| * [[Phasor approach to fluorescence lifetime and spectral imaging]]
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| == References ==
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| {{Reflist}}
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| * Goetz, A.F.H., Vane, G., Solomon, J.E., & Rock, B.N. (1985) Imaging spectrometry for earth remote sensing. Science, 228, 1147.
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| * Schaepman, M. (2005) Spectrodirectional Imaging: From Pixels to Processes. Inaugural address, Wageningen University, Wageningen (NL).
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| * Vane, G., Chrisp, M., Emmark, H., Macenka, S., & Solomon, J. (1984) Airborne Visible Infrared Imaging Spec-trometer ([[AVIRIS]]): An Advanced Tool for Earth Remote Sensing. European Space Agency, (Special Publication) ESA SP, 2, 751.
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| == External links ==
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| * About imaging spectroscopy (USGS): http://speclab.cr.usgs.gov/aboutimsp.html
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| * Link to resources (OKSI): http://www.techexpo.com/WWW/opto-knowledge/IS_resources.html
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| * Special Interest Group Imaging Spectroscopy (EARSeL): http://www.op.dlr.de/dais/SIG-IS/SIG-IS.html
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| * Applications of Spectroscopic and Chemical Imaging in Research: http://www3.imperial.ac.uk/vibrationalspectroscopyandchemicalimaging/research
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| * Analysis tool for spectral unmixing : http://www.spechron.com
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| [[Category:Spectroscopy]]
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