Introduction to Hyperspectral Imaging

Hyperspectral Imaging

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TNTmips

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Introduction to

I N T R O T O H Y P E R S P

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Introduction to Hyperspectral Imaging

Spectral Reflectance

In reflected-light spectroscopy the fundamental property that we want to obtain is spectral reflectance : the ratio of reflected energy to incident energy as a func-tion of wavelength. Reflectance varies with wavelength for most materials because energy at certain wavelengths is scattered or absorbed to different degrees. These reflectance variations are evident when we compare spectral reflectance curves (plots of reflectance versus wavelength) for different materials, as in the illustra-tion below. Pronounced downward deflections of the spectral curves mark the wavelength ranges for which the material selectively absorbs the incident energy.These features are commonly called absorption bands (not to be confused with the separate image bands in a multispectral or hyperspectral image). The overall shape of a spectral curve and the position and strength of absorption bands in many cases can be used to identify and discriminate different materials. For example, vegetation has higher reflectance in the near infrared range and lower reflectance of red light than soils.

Representative spectral reflectance curves for several common Earth surface ma-terials over the visible light to reflected infrared spectral range. The spectral bands used in several multispectral satellite remote sensors are shown at the top for comparison. Reflectance is a unitless quantity that ranges in value from 0 to 1.0,or it can be expressed as a percentage, as in this graph. When spectral measure-ments of a test material are made in the field or laboratory, values of incident energy are also required to calculate the material’s reflectance. These values are either measured directly or derived from measurements of light reflected (under the same illumination conditions as the test material) from a standard reference material with known spectral reflectance.

Vegetation

Dry soil (5% water)

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Clear lake water

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Introduction to Hyperspectral Imaging

Reflectance spectra of some representative minerals (naturally occurring chemical compounds that are the major components of rocks and soils).

Wavelength (micrometers)

Hematite

Montmorillonite

Calcite

Kaolinite

Orthoclase Feldspar

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Plant Spectra

Reflectance spectra of different types of green vegetation compared to a spectral curve for senescent (dry, yellowed) leaves. Different portions of the spectral curves for green vegetation are shaped by different plant components, as shown at the top.

The spectral reflectance curves of healthy green plants also have a characteristic shape that is dictated by various plant attributes. In the visible portion of the spectrum, the curve shape is governed by absorption effects from chlorophyll and other leaf pigments. Chlorophyll absorbs visible light very effectively but absorbs blue and red wavelengths more strongly than green, producing a charac-teristic small reflectance peak within the green wavelength range. As a consequence, healthy plants appear to us as green in color. Reflectance rises sharply across the boundary between red and near infrared wavelengths (some-times referred to as the red edge ) to values of around 40 to 50% for most plants.This high near-infrared reflectance is primarily due to interactions with the inter-nal cellular structure of leaves. Most of the remaining energy is transmitted, and can interact with other leaves lower in the canopy. Leaf structure varies signifi-cantly between plant species, and can also change as a result of plant stress. Thus species type, plant stress, and canopy state all can affect near infrared reflectance measurements. Beyond 1.3 µm reflectance decreases with increasing wavelength,except for two pronounced water absorption bands near 1.4 and 1.9 µm.At the end of the growing season leaves lose water and chlorophyll. Near infra-red reflectance decreases and red reflectance increases, creating the familiar yellow,brown, and red leaf colors of autumn.

Wavelength (micrometers)

R e f l e c t a n c e (%)

Grass

Walnut tree canopy Fir tree

Dry, yellowed grass

Visible Near Infrared Chlorophyll

Cell Structure Water

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Spectral Libraries

Sample spectra from the ASTER Spectral Library.ASTER will be one of the instruments on the planned EOS AM-1

satellite, and will record image data in 14 channels from the visible through thermal infrared wavelength regions as part of NASA’s Earth

Science Enterprise program.

Several libraries of reflectance spectra of natural and man-made materials are available for public use. These libraries provide a source of reference spectra that can aid the interpretation of hyperspectral and multispectral images.ASTER Spectral Library This library has been made available by NASA as part of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (AS-TER) imaging instrument program. It includes spectral compilations from NASA’s Jet Propulsion Laboratory, Johns Hopkins University, and the United States Geo-logical Survey (Reston). The ASTER spectral library currently contains nearly 2000 spectra, including minerals, rocks, soils, man-made materials, water, and snow. Many of the spectra cover the entire wavelength region from 0.4 to 14 µm.The library is accessible interactively via the Worldwide Web at http://speclib.jpl.nasa.gov. You can search for spectra by category, view a spectral plot for any of the retrieved spectra, and download the data for individual spectra as a text file. These spectra can be imported into a TNTmips spectral library. You can also order the ASTER spectral library on CD-ROM at no charge from the above web address.

USGS Spectral Library The United States Geological Survey Spectroscopy Lab in Denver, Colorado has compiled a library of about 500 reflectance spectra of minerals and a few plants over the wavelength range from 0.2 to 3.0 µm. This library is accessible online at

http://speclab.cr.usgs.gov/spectral.lib04/spectral-lib04.html .

You can browse individual spectra online, or download the entire library. The USGS Spectral library is also included as a standard reference library in the TNTmips Hyperspectral Analysis process.

Wavelength (micrometers)

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Granite

Concrete

Asphalt roof shingles

Basalt

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Example of a composite spectrum (C) that is a linear

brightness for a portion

of playa surface (red

square at right).

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This spectrum does not bear much resemblance to the reflectance spectra illus-trated previously. This is because the sensor has simply measured the amount of reflected light reaching it in each wavelength band (spectral radiance), in this case from an altitude of 20 kilometers. The spectral reflectance of the surface materials is only one of the factors affecting these measured values. The spectral reflectance curve for the sample area is actually relatively flat and featureless. In addition to surface reflectance, the spectral radiance measured by a remote

Atmospheric Effects Even a relatively clear atmosphere interacts with incom-ing and reflected solar energy. For certain wavelengths these interactions reduce the amount of incoming energy reaching the ground and further reduce the amount of reflected energy reaching an airborne or satellite sensor. The transmittance of the atmosphere is reduced by absorption by certain gases and by scattering by gas molecules and particulates. These effects combine to produce the transmittance curve illustrated below. The pronounced absorption features near 1.4 and 1.9µm, caused by water vapor and carbon dioxide, reduce incident and reflected energy almost completely, so little useful information can be obtained from im-age bands in these regions. Not shown by this curve is the effect of light scattered upward by the atmosphere. This scattered light adds to the radiance measured by the sensor in the visible and near-infrared wavelengths, and is called path radi-ance . Atmospheric effects may also differ between areas in a single scene if atmospheric conditions are spatially variable or if there are significant ground elevation differences that vary the path length of radiation through the atmo-sphere.

Sensor Effects A sensor converts detected radiance in each wavelength channel to an electric signal which is scaled and quantized into discrete integer values that represent “encoded” radiance values. Variations between detectors within an array, as well as temporal changes in detectors, may require that raw measure-ments be scaled and/or offset to produce comparable values.

Plot of atmospheric transmittance versus wavelength for typical atmospheric con-ditions. Transmittance is the proportion of the incident solar energy that reaches the ground surface. Absorption by the labeled gases causes pronounced lows in the curve, while scattering is responsible for the smooth decrease in transmittance with decreasing wavelength in the near infrared through visible wavelength range.

Atmospheric and Sensor Effects

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O 2O 3Visible Near Infrared Middle Infrared CO 2

CO 2O 2H 2O,CO 2

In order to directly compare hyperspectral image spectra with reference reflec-tance spectra, the encoded radiance values in the image must be converted to reflectance. A comprehensive conversion must account for the solar source spec-trum, lighting effects due to sun angle and topography, atmospheric transmission, and sensor gain. In mathematical terms, the ground reflectance spectrum is mul-tiplied (on a wavelength per wavelength basis) by these effects to produce the measured radiance spectrum. Two other effects contribute in an additive fashion to the radiance spectrum: sensor offset (internal instrument noise) and path radi-ance due to atmospheric scattering. Several commonly used reflectance conversion strategies are discussed below and on the following page. Some strategies use only information drawn from the image, while others require varying degrees of knowledge of the surface reflectance properties and the atmospheric conditions at the time the image was acquired.

Flat Field Conversion This image-based method requires that the image in-clude a uniform area that has a relatively flat spectral reflectance curve. The mean spectrum of such an area would be dominated by the combined effects of solar irradiance and atmospheric scattering and absorption The scene is con-verted to “relative” reflectance by dividing each image spectrum by the flat field mean spectrum. The selected flat field should be bright in order to reduce the effects of image noise on the conversion. Since few if any materials in natural landscapes have a completely flat reflectance spectrum, finding a suitable “flat field” is difficult for most scenes. For desert scenes, salt-encrusted dry lake beds present a relatively flat spectrum, and bright man-made materials such as con-crete may serve in urban scenes. Any significant spectral absorption features in the flat field spectrum will give rise to spurious features in the calculated relative reflectance spectra. If there is significant elevation variation within the scene, the converted spectra will also incorporate residual effects of topographic shad-ing and atmospheric path differences.

Average Relative Reflectance Conversion This method also normalizes image spectra by dividing by a mean spectrum, but derives the mean spectrum from the entire image. Before computing the mean spectrum, the radiance values in each image spectrum are scaled so that their sum is constant over the entire image. This adjustment largely removes topographic shading and other overall bright-ness variations. The method assumes that the scene is heterogeneous enough that spatial variations in spectral reflectance characteristics will cancel out, produc-ing a mean spectrum similar to the flat field spectrum described above. This assumption is not true of all scenes, and when it is not true the method will produce relative reflectance spectra that contain spurious spectral features.

Match Each Image Spectrum

One approach to analyzing a hyperspectral image is to attempt to match each image spectrum individually to one of the reference reflectance spectra in a spec-tral library. This approach requires an accurate conversion of image spectra to reflectance. It works best if the scene includes extensive areas of essentially pure materials that have corresponding reflectance spectra in the reference library. An observed spectrum will typically show varying degrees of match to a number of similar reference spectra. The matching reference spectra must be ranked using some measure of goodness of fit, with the best match designated the “winner.”Spectral matching is compli-cated by the fact that most hyperspectral scenes include

many image pixels that repre-sent spatial mixtures of different

materials (see page 10). The re-

sulting composite image spectra may match a variety of “pure” reference spectra to varying degrees, perhaps in-cluding some spectra of materials that are not actually

present. If the best-matching reference spectrum has a sufficient fit to the image spectrum, then this material is probably the dominant one in the mixture and the pixel is assigned to this material. If no reference spectrum achieves a sufficient match, then no endmember dominates, and the pixel should be left unassigned.The result is a “material map” of the image that portrays the dominant material for most of the image cells, such as the example shown below. Sample mixed spectra can be included in the library to improve the mapping, but it is usually not possible to include all possible mixtures (and all mixture proportions) in the ref-

erence library.Mineral map for part of the Cuprite AVIRIS scene,created by matching image spectra to mineral spectra in the USGS Spectral Library. White areas did not produce a sufficient match to any of the selected reflectance spectra, and so are left

unassigned.

Alunite

Kaolinite

Alunite + Kaolinite

Montmorillonite

Chalcedony Minerals

Sample image spectrum and a matched spectrum

of the mineral alunite from the USGS Spectral Library (goodness of fit = 0.91). 2.42.1 2.2 2.3Wavelength (micrometers)1.00.80.60.40.2R e f l e c t a n c e Image Library

Spectral Matching Methods

Reflectance spectrum for the mineral gypsum (A) with several absorption features. Curve B shows the

continuum for the spectrum, and C the spectrum after removal of the continuum.0.5 1.5 2.51.00.80.60.40.20Wavelength (µm)R e f l e c t a n c e

A B C 1.0 2.0The shape of a reflectance spectrum can usually be broken down into two com-ponents: broad, smoothly changing regions that define the general shape of the spectrum and narrow, trough-like absorption features. This distinction leads to two different approaches to matching image spectra with reference spectra.

Many pure materials, such as minerals, can be recognized by the position, strength (depth), and shape of their absorption features. One common matching strategy attempts to match only the absorption features in each candidate reference spec-trum and ignores other parts of the spectrum. A unique set of wavelength regions is therefore examined for each reference candidate, determined by the locations of its absorption features. The local position and slope of the spectrum can affect the strength and shape of an absorption feature, so these parameters are usually determined relative to the continuum : the upper limit of the spectrum’s general shape. The continuum is computed for each wavelength subset and removed by dividing the reflectance at each spectral channel by its corresponding continuum value. Absorption features can then be matched using a set of derived values (including depth and the width at half-depth), or by using the complete shape of the feature. These types

of procedures have been organized into an expert system by researchers at

the U.S. Geological Sur-vey Spectroscopy Lab (Clark and others, 1990).

Many other materials,such as rocks and soils,may lack distinctive ab-

sorption features. These

spectra must be character-ized by their overall shape.Matching procedures uti-lize full spectra (omitting

noisy image bands severely affected by atmospheric absorption) or a uniform wavelength subset for all candidate materials. One approach to matching seeks the spectrum with the minimum difference in reflectance (band per band) from the image spectrum (quantified by the square root of the sum of the squared errors).Another approach treats each spectrum as a vector in spectral space and finds the reference spectrum making the smallest angle with the observed image spec-trum.

Introduction to Hyperspectral Imaging

Linear Unmixing

Portion of an AVIRIS scene with forest, bare and vegetated fields,and a river, shown with a color-infrared band combination (vegetation is red). Fraction images from linear unmixing are shown below.Vegetation fraction Water / shade fraction

Soil fraction

Linear unmixing is an alternative approach to simple

spectral matching. Its underlying premise is that a scene

includes a relatively small number of common materi-

als with more or less constant spectral properties.

Furthermore, much of the spectral variability in a scene

can be attributed to spatial mixing, in varying propor-

tions, of these common endmember components. If

we can identify the endmember spectra, we can math-

ematically “unmix” each pixel’s spectrum to identify

the relative abundance of each endmember material.

The unmixing procedure models each image spectrum

as the sum of the fractional abundances of the

endmember spectra, with the further constraint that the

fractions should sum to 1.0. The best-fitting set of frac-

tions is found using the same spectral-matching

procedure described on the previous page. A fraction

image for each endmember distills the abundance in-

formation into a form that is readily interpreted and

manipulated. An image showing the residual error for

each pixel helps identify parts of the scene that are not

adequately modeled by the selected set of endmembers.

The challenge in linear unmixing is to identify a set of

spectral endmembers that correspond to actual physi-

cal components on the surface. Endmembers can be

defined directly from the image using field information

or an empirical selection technique such as the one

outlined on the next page can be used. Alternatively,

endmember reflectance spectra can be selected from a

reference library, but this approach requires that the

image has been accurately converted to reflectance.

Variations in lighting can be included directly in the

mixing model by defining a “shade” endmember that

can mix with the actual material spectra. A shade spec-

trum can be obtained directly from a deeply shadowed

portion of the image. In the absence of deep shadows,

the spectrum of a dark asphalt surface or a deep water

body can approximate the shade spectrum, as in the

example to the right.

Introduction to Hyperspectral Imaging

Partial Unmixing Some hyperspectral image applications do not require finding the fractional abun-dance of all endmember components in the scene. Instead the objective may be to detect the presence and abundance of a single target material. In this case a complete spectral unmixing is unnecessary. Each pixel can be treated as a poten-tial mixture of the target spectral signature and a composite signature representing all other materials in the scene. Finding the abundance of the target component is then essentially a partial unmixing problem.

Methods for detecting a target spectrum against a background of unknown spec-tra are often referred to as matched filters, a term borrowed from radio signal processing. Various matched filtering algorithms have been developed, includ-ing orthogonal subspace projection and constrained energy minimization (Farrand and Harsanyi, 1994). All of these approaches perform a mathematical transfor-mation of the image spectra to accentuate the contribution of the target spectrum while minimizing the background. In a geometric sense, matched filter methods find a projection of the n-dimensional spectral space that shows the full range of abundance of the target spectrum but “hides” the variability of the background. In most instances the spectra that contribute to the background are unknown, so most matched filters use statistical methods to estimate the composite background signature from the image itself. Some methods only work well when the target material is rare and does not contribute significantly to the background signature.

A modified version of matched filtering uses derivatives of the spectra rather than the spectra themselves, which improves the matching of spectra with differ-ing overall brightness.

Fraction images produced by Matched Filtering (left) and Derivative Matched Filtering (right) for a portion of the Cuprite AVIRIS scene. The target image spectrum represents the mineral alunite. Brighter tones indicate pixels with higher alunite fractions. The image produced by Derivative Matched Filtering shows less image noise, sharper boundaries, and better contrast between areas with differing alunite fractions.Introduction to Hyperspectral Imaging

References

General

Kruse, F.A. (1999). Visible-Infrared Sensors and Case Studies. In Renz, Andrew N. (ed), Remote Sensing for the Earth Sciences: Manual of Remote Sens-ing (3rd ed.), V ol 3. New York: John Wiley & Sons, pp. 567-611. Landgrebe, David (1999). Information Extraction Principles and Methods for Mul-tispectral and Hyperspectral Image Data. In Chen, C.H. (ed.), Information Processing for Remote Sensing. River Edge, NJ: World Scientific Publish-ing Company, pp. 3-38.

V ane, Gregg, Duval, J.E., and Wellman, J.B. (1993). Imaging Spectroscopy of the Earth and Other Solar System Bodies. In Pieters, Carle M. and Englert, Peter A.J. (eds.), Remote Geochemical Analysis: Elementatl and Miner-alogic Composition. Cambridge, UK: Cambridge University Press, pp.

121-143.

Vane, Gregg, and Goetz, A.F.H. (1988). Terrestrial Imaging Spectroscopy. Re-mote Sensing of Environment, 24, pp. 1-29.

Spectral Reflectance Signatures

Ben-Dor, E., Irons, J.R., and Epema, G.F. (1999). Soil Reflectance. In Renz, Andrew N. (ed), Remote Sensing for the Earth Sciences: Manual of Remote Sens-ing (3rd ed.), V ol 3. New York: John Wiley & Sons, pp. 111-188. Clark, Roger N. (1999). Spectroscopy of Rocks and Minerals, and Principles of Spectroscopy. In Renz, Andrew N. (ed), Remote Sensing for the Earth Sciences: Manual of Remote Sensing (3rd ed.), V ol 3. New York: John Wiley & Sons, pp. 3-58.

Ustin, S.L., Smith, M.O., Jacquemoud, S., V erstraete, M., and Govaerts, Y. (1999).

Geobotany: Vegetation Mapping for Earth Sciences. In Renz, Andrew N.

(ed), Remote Sensing for the Earth Sciences: Manual of Remote Sensing (3rd ed.), V ol 3. New York: John Wiley & Sons, pp. 1-248.

Reflectance Conversion

Farrand, William H., Singer, R.B., and Merenyi, E., 1994, Retrieval of Apparent Surface Reflectance from A VIRIS Data: A Comparison of Empirical Line, Radiative Transfer, and Spectral Mixture Methods. Remote Sensing of Environment, 47, 311-321.Introduction to Hyperspectral Imaging

References Goetz, Alexander F.H., and Boardman, J.W. (1997). Atmospheric Corrections: On Deriving Surface Reflectance from Hyperspectral Imagers. In Descour, Michael R. and Shen, S.S. (eds.), Imaging Spectrometry III: Proceedings of SPIE, 3118, 14-22.

van der Meer, Freek (1994). Calibration of Airborne Visible/Infrared Imaging Spectrometer Data (AVIRIS) to Reflectance and Mineral Mapping in Hydrothermal Alteration Zones: An Example from the “Cuprite Mining District”. Geocarto International, 3, 23-37.

Hyperspectral Image Analysis

Adams, John B., Smith, M.O., and Gillespie, A.R. (1993). Imaging Spectros-copy: Interpretation Based on Spectral Mixture Analysis. In Pieters, Carle M. and Englert, Peter A.J. (eds.), Remote Geochemical Analysis: Elementatl and Mineralogic Composition. Cambridge, UK: Cambridge University Press, pp. 145-166.

Clark, R.N., Gallagher, A.J., and Swayze, G.A. (1990). Material absorption band depth mapping of imaging spectrometer data using a complete band shape least-squares fit with library reference spectra. Proceedings of the Sec-ond Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) Workshop, JPL Publication 90-54, pp. 176-186.

Cloutis, E.A., (1996). Hyperspectral Geological Remote Sensing: Evaluation of Analytical Techniques. International Journal of Remote Sensing, 17, 2215-2242.

Farrand, William H., and Harsanyi, J.C. (1994). Mapping Distributed Geologi-cal and Botanical Targets through Constrained Energy Minimization.

Proceedings of the Tenth Thematic Conference on Geological Remote Sensing, San Antonio, Texas, 9-12 May 1994, pp. I-419 - I-429. Green, Andrew A., Berman, M., Switzer, P., and Craig, M.D. (1988). A Trans-formation for Ordering Multispectral Data in Terms of Image Quality with Implications for Noise Removal. IEEE Transactions on Geoscience and Remote Sensing, 26, 65-74.

Mustard, John F., and Sunshine, J.M. (1999). Spectral Analysis for Earth Sci-ence: Investigations Using Remote Sensing Data. In Renz, Andrew N.

(ed), Remote Sensing for the Earth Sciences: Manual of Remote Sensing (3rd ed.), V ol 3. New York: John Wiley & Sons, pp. 251-306.

Introduction to Hyperspectral Imaging Advanced Software for Geospatial Analysis MicroImages,Inc.

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Index

absorption

bands..................................................5-7

atmospheric...................................13,18

atmosphere

absorption by...........................13,18

scattering by (13)

continuum (18)

illumination..........................................11,12

imaging spectrometer........................4,10,16

irradiance, solar (12)

linear unmixing....................................19-21

matched filtering (21)

matching, spectral................................17,18

minimum noise fraction transform (20)

pixel purity index (20)

resolution, spatial (10)

scattering.............................................4,5,13

sensor effects (13)

shadowing (12)

spectral libraries..........................................8spectral radiance.........................................11spectral reflectance.................................5-11converting image to.........................14-15curve See spectrum defined.................................................5spectral space..............................................9spectrometer..................................................4spectroscopy.........................................4,5spectrum (spectra)endmember....................................19,20image....................................3,17-20in library.........................................8mineral......................................6mixed.................................................10plant.....................................................7plotting.................................................9reflectance.......................................5-11soil.......................................................5solar...................................................12water. (5)

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