Spectral Analysis and Classification of Hyperspectral Data
Wendy Zhang
Southern University at New Orleans
Mentor
Lloyd McGregor
Lockheed Martin Space Operations-Stennis Programs
NASA Faculty Fellowship Program 2003
ABSTRACT
Imagine spectrometers or “hyperspectral sensors” are remote sensing instruments that
combine the spatial presentation of an imaging sensor with the analytical capabilities of a
spectrometer. The AVIRIS (Airborne Visible-Infrared Imaging Spectrometer) collects data in 224
bands that are approximately 9.6 nm (narometer) wide in contiguous bands between 0.40 and
2.45 m (micron). The main objective of the AVIRIS object is to identify, measure, and monitor
constituents of the Earth’s surface and atmosphere based on molecular absorption and particle
scattering signatures. Research with AVIRIS data is predominantly focuses on understanding
processes related to the global environment and climate change. In this project, we emphasize
on spectrally oriented classification procedures for land cover mapping, particularly, the surface
classification using AVIRIS data.
Keywords: Hyperspectral data, spectral analysis, endmember, classification
Wendy Zhang, Southern University at New Orleans
1. Introduction
NASA's Earth Sciences program is primarily focused on providing high quality data products to
its science community. NASA also recognizes the need to increase its involvement with the
general public, including areas of information and education. Many different Earth-sensing
satellites, with diverse sensors mounted on sophisticated platforms, are in Earth orbit or soon to
be launched. These sensors are designed to cover a wide range of the electromagnetic spectrum
and are generating enormous amounts of data that must be processed, stored, and made available
to the user community.
Imagine spectrometers or “hyperspectral sensors” are remote sensing instruments that
combine the spatial presentation of an imaging sensor with the analytical capabilities of a
spectrometer. They may have up to several hundred narrow spectral bands with spectral
resolution on the order of 10nm or marrow. They acquire images throughout the visible, near-IR,
and thermal IR portions of the spectrum. The AVIRIS (Airborne Visible-Infrared Imaging
Spectrometer) collects data in 224 bands that are approximately 9.6 nm (narometer) wide in
contiguous bands between 0.40 and 2.45 m (micron). Because of the large number of very
narrow bands sampled, hyperspectral data enable the use of remote sensing data collection to
replace data collection that was formally limited to laboratory testing or ground site surveys. The
main objective of the AVIRIS object is to identify, measure, and monitor constituents of the
Earth’s surface and atmosphere based on molecular absorption and particle scattering signatures.
Research with AVIRIS data is predominantly focuses on understanding processes related to the
global environment and climate change.
AVIRIS data has hundreds of spectral bands, compare this to broad-band multispectral
scanners such as Landsat Thematic Mapper ™, which only has 6 spectral bands and spectral
resolution on the order of 100nm or greater. Each pixel has an associated, continuous spectrum
than can be used to identify the surface materials. The end result of the high spectral resolution
of imaging spectrometer is that we can identify materials, where with broad-band sensors we
could only discriminate between materials.
In this project, we emphasize on spectrally oriented classification procedures for land
cover mapping, particularly, the surface classification using AVIRIS data. The initial goal of the
research is to decide which are the most appropriate spectra from the spectral library for
identifying a particular ground cover. The term ”endmember” describes a substance or material
that has a unique spectral signature. Endmembers may be a single substance or a composite of
materials. It is expected that, based on endmember spectra extracted, the data patterns can be
easily retrieved, and that the most appropriate bands can be identified. The overall objective of
image classification procedure is to automatically categorize all pixels in an image into land
cover classes or themes.
2. Overview of Hyperspectral Data and Image Analysis
2.1 AVIRIS
AVIRIS is a proven instrument in the realm of Earth Remote Sensing. It is a unique optical
sensor that delivers calibrated images of the upwelling spectral radiance in 224 contiguous
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spectral channels (bands) with wavelengths from 400 to 2500 nanometers. AVIRIS has been
flown on two aircraft platforms: a NASA ER-2 jet and the Twin Otter turboprop.
The AVIRIS sensor collects data that can be used for characterization of the Earth's
surface and atmosphere from geometrically coherent spectroradiometric measurements. This data
can be applied to studies in the fields of oceanography, environmental science, snow hydrology,
geology, volcanology, soil and land management, atmospheric and aerosol studies, agriculture,
and limnology. Applications under development include the assessment and monitoring of
environmental hazards such as toxic waste, oil spills, and land/air/water pollution. With proper
calibration and correction for atmospheric effects, the measurements can be converted to ground
reflectance data which can then be used for quantitative characterization of surface features.
The main objective of the AVIRIS object is to identify, measure, and monitor constituents
of the Earth’s surface and atmosphere based on molecular absorption and particle scattering
signatures. Research with AVIRIS data is predominantly focuses on understanding processes
related to the global environment and climate change.
2.2 Spectral Regions
The utility of the electromagnetic spectral regions are depicted according to the wavelength
measured in nanometers (nm), micron (m), and centimeters (cm).
Region
Wavelength
Remarks
Gamma ray
< 0.03 nm
X-ray
0.03 to 3.0 nm
Ultraviolet
0.03 to 0.4 m
Photographic 0.3 to 0.4 m
UV band
Visible
0.4 to 0.7 m
Infrared
0.7 to 100 m
Reflected
IR band
0.7 to 3.0 m
Thermal
IR band
3
to 5 m
8 to 14 m
Microwave
0.1 to 30 cm
Radar
0.1 to 30 cm
Radio
> 30 cm
Incoming radiation is completely absorbed by the upper atmosphere
and is not available for remote sensing.
Completely absorbed by atmosphere. Not employed in remote
sensing.
Incoming wavelengths less than 0.3 m are completely absorbed by
ozone in the up atmosphere.
Transmitted through atmosphere. Detectable with film and
photodectectors, but atmospheric scattering is severe.
Imaged with film and photodetectors. Includes reflected energy
peak of earth at 05. m.
Interaction with matter varies with wavelength. Atmospheric
transmission windows are separated by absorption bands.
Reflected solar radiation that contains no information about thermal
properties of materials. The band from 0.7 to 0.9 m is detectable
with film and is called the photographic IR band.
Principal atmospheric windows in the thermal region. Images at
these wavelengths are acquired by optical-mechanical scanners and
special vidicon systems but not by film.
Longer wavelengths can penetrate clouds, fog, and rain. Images
may be acquired in the active or passive mode.
Active form of microwave remote sensing. Radar images are
acquired at various wavelength bands.
Longest wavelength portion of electromagnetic spectrum. Some
classified radars with very long wavelength operate in this region.
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2.3 Spectral Response Patterns
The broad feature types of three basic types of earth features, green vegetation, dry bare soil, and
clear later water, are normally spectrally separable. Water and vegetation might reflect nearly
equally in visible wavelengths, yet these features are always separable in near-IR wavelengths.
Since spectral responses measured by remote sensors over various features often permit an
assessment of the type and condition of the features, these responses are referred to as spectral
signature. Many earth surfaces manifest very distinctive spectral reflectance and emittance
characteristics. These characteristics result in spectral response patterns.
Spectral signature is different from spectral pattern. Signature tends to imply a pattern
that is absolute and unique. Spectral response patterns measured by remote sensors may be
quantitative, but they are not absolute. They may be distinctive but they are not necessarily
unique.
2.4 Spectral Resolution, Spectral Sampling, and Spectral Modeling
Spectral resolution determines the way we see individual spectral features in materials measured
using imaging spectrometry. Spectral resolution refers to the width of an instrument response
(band-pass) at half of the band depth (the Full Width Half Max [FWHN]). The spectral
resolution required for a specific sensor is a direct function of the material you are trying to
identify, and the contrast between that material and the background materials.
Spectral sampling refers to the band spacing – the quantization of the spectrum at discrete
steps. Quality spectrometers are usually designed so that the band spacing is about equal to the
band FWHM.
Spectral modeling shows that spectral resolution requirements for imaging spectrometers
depend upon the character of the material being measured.
2.5 HyMap Data
HyMap is a state-of-the-art aircraft-mounted commercial hyperspectral sensor developed by
Integrated Spectronics, Sydney, Australia, and operated by HyVista Corporation.
HyMap provides unprecedented spatial, spectral and radiometric excellence. The system
is a whiskbroom scanner utilizing diffraction gratings and four 32-element detector arrays (1 Si,
3 liquid-nitrogen-cooled InSb) to provide 126 spectral channels covering the 0.44 – 2.5 m range
over a 512-pixel swath. Spectral resolution varies from 10 –20 nm with 3 – 10m spatial
resolution and SNR over 1000:1.
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3. Hyperspectral Image Spectral Analysis and Mapping
3.1 Spectral Feature Fitting and Analysis
Spectral Feature FittingTM (SFFTM) is an absorption-feature-based method for matching image
spectra to reference endmembers (different materials contained in each spatial resolution cell).
Most methods used for analysis of hyperspectral data still do not directly identify specific
materials. They only indicate how similar the material is to another known material or how
unique it is with respect to other materials. Techniques for direct identification of materials via
extraction of specific spectral features from field and laboratory reflectance spectra have been in
use for many years.
All the methods require that data to be reduced to reflectance and that a continuum be
removed from the reflectance data prior to analysis. A continuum is a mathematical function
used to isolate a particular absorption feature for analysis. It corresponds to a background signal
unrelated to specific absorption features of interest. Spectra are normalized to common reference
using a continuum formed by defining high points of the spectrum (local maximal) and fitting
straight line segments between these points. The continuum is removed by dividing it into the
original spectrum.
Spectral feature fitting requires that reference endmembers be selected from either the
image or a spectral library, that both the reference and unknown spectra have the continuum
removed, and that each reference endmember spectrum be scaled to match the unknown
spectrum. A “scale” image is produced for each endmember selected for analysis by first
subtracting the continuum-removed spectra from one, thus inverting them and making the
continuum zero. A single multiplicative scaling factor is then determined that makes the
reference spectrum match the unknown spectrum. Assuming that a reasonable spectral ranges
have been selected, a large scaling factor is equivalent to deep spectral feature, while a small
scaling factor indicates a weak spectral feature. A least-square-fit is then calculated band-byband between each reference endmember and the unknown spectrum. The total root-mean-square
(RMS) error is used to form an RMS image for each endmember.
The Spectral AnalystTM matches unknown spectra to library spectra and provides a score
with respect to the library spectra. The spectral analyst uses several methods to produce a score
between 0 and 1, with 1 equaling a perfect match.
3.2 Spectral Angle Mapper (SAM)
The Spectral Angle Mapper (SAM) is a physically-based spectral classification that uses the ndimensional angle to match pixels to reference spectra. The algorithm determines the spectral
similarity between two spectra by calculating the angle between the spectra, treating them as
vectors in a space with dimensionality equal to the number of bands. SAM compares the angle
between the endmember spectrum (considered as a n-dimensional vector, where n is the number
of bands) and each pixel vector in n-dimensional space. Smaller angles represent closer matches
to the reference spectrum.
SAM is an automated method for comparing image spectra to individual spectra or a
spectral library. SAM assumes that the data have been reduced to apparent reflectance (true
reflectance multiplied by some unknown gain factor controlled by topography and shadows).
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The algorithm determines the similarity between two spectra by calculating the “spectral
angle” between them, treating them as vectors in a space with dimensionality equal to the
number of bands (nb). Considering a reference spectrum and an unknown spectrum from twoband data, the two different materials will be represented in the 2-D scatter plot by a point for
each given illumination, or as a line (vector) for all possible illumination. The method uses only
the “direction” of the spectra and not their length. The angle between the vectors is the same
regardless the length. The length of the vector relates on how fully the pixel is illuminated. It is
insensitive to the unknown gain factor and all possible illuminations are treated equally. Poorly
illuminated pixels will fall closer to the origin. The “color” of a material is defined by the
direction of its unit vector.
material A
spectral angle
Band 1
material B
Band 2
Figure 3.1 Two-dimensional example of the Spectral Angle Mapper
The SAM algorithm generalizes this geometric interpretation to nb-dimensional space.
SAM determined the similarity of an unknown spectrum t to a reference spectrum r, by applying
the following equation:
nb
Σ tiri
i=1
α = cos –1
____________________
nb
½
nb
½
2
2
Σ ti
Σ ri
i=1
i=1
where nb equals the number of bands in the image.
For each reference spectrum chosen in the analysis of a hyperspectral image, the spectral
angle α is determined for every image spectrum (pixel). This value, in radians is assigned to the
corresponding pixel in the output SAM image. One output image for each referenced spectrum.
The derived spectral angle maps from a new data cube with the number of bands equal to the
number of reference spectra used in the mapping. Gray-level threshold is typically used to
empirically determine those areas that mostly match the reference spectrum while retaining
spatial coherence.
The SAM algorithm takes as input a number of “training classes” or reference spectra
from ASCII files, ROIs, or spectral libraries. It calculates the angular distance between each
spectrum in the image and the reference spectra in n-dimensions. The result is a classification
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image showing the best SAM match at each pixel and a “rule” image for each reference spectra
showing the actual angular distance in radians between each spectrum in the images and the
reference spectrum. Darker pixels in the rule images represent smaller spectral angles, the
spectra are more similar to the reference spectrum.
SAM assumes that the data have been reduced to apparent reflectance and uses only the
“direction” of the spectra, and not the “length”. Thus the SAM classification is insensitive to
illumination effects. This technique is relatively insensitive to illumination and albedo effects
when used on calibrated reference data.
3.3 Spectrum Profile
Every material has its own set of reflectance values for each band that can be used to identify it.
In a spectral profile, the y-coordinate shows the reflectance value of the material from 0-8000.
The numbers on the x-coordinate identify the band wave. Wavelengths in the profile are
measured in “microns,” where 1m = 1 millionth meter. The shape of the plot in the spectrum
profile can be used to identify the material.
4. Experiments
The experiment is set up to find the appropriate endmebers to classify surface cover of Stennis
Space Center using AVIRIS data that was taken on July 29, 1999. The data file used is Airborne
Visible/Infrared Imaging Spectrometer (AVIRIS) apparent reflectance mosaic data of part of
Stennis Space Center, Mississippi, USA, calibrated using ATREM (ATmospheric REMoval)
atmospheric modeling software. The data cover the 0.3704 to 2.5101μm range in 224 spectral
bands and the image data file size is 984MB. The software used to do this project is the
Environment for Visualizing Images (ENVI) version 3.6 running on UNIX systems. The spectral
libraries used are USGS Vegetation Spectral Library (wavelength is 0.3951 to 2.56μm and the
wavelength accuracy is on the order of 0.5nm in the near_IR and 0.2nm in the visible), Jasper
Ridge Spectral Library for Green Vegetation, Dry Vegetation, and Rocks, The John Hopkins
University Spectra Library for Man Made Materials (0.42 –14 m), Man Made Materials (0.3 –
12.5 m), and Vegetation (0.3 –14 m).
4.1 Methodology
Traditional supervised and unsupervised classification techniques will require very long
processing times for hyperspectral data because of the dependence on the number of wave bands
(224). A more serious problem is the need to estimate class signatures, i.e., the mean vector and
covariance matrix, when using algorithms such as maximum likelihood, based on second order
statistics. The difficulty lies in the small number of available training pixels per class compared
with the number of wavebands used and is related directly to the Hughes phenomenon. If too
few training samples are used then the class model may be very accurate for the training data and
classification accuracy on training data can be very high. However, classification accuracy on
testing data will be poor. In this case, the classifier is over trained and the statistics estimated are
unreliable. Most methods used for analysis of hyperspectral data still do not directly identify
specific materials. They only indicate how similar the material is to another known material or
how unique it is with respect to other materials. Techniques for direct identification of materials
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via extraction of specific spectral features from field and laboratory reflectance spectra have
been in use for many years.
It is not feasible to use regular classification methods to classify images using AVIRIS
hyperspectral data.
The spectral libraries are provided by different agencies and labs. We browse image
spectra and compare it to spectral library. When visually comparing and contrasting the
corresponding AVIRIS spectra with the features shown in the laboratory spectra, find how similar
the two spectra are. We put image spectra and similar library spectra together to create a spectral
profile.
ENVI has a spectral matching tool, the Spectral AnalystTM. The Spectral AnalystTM scores
the unknown spectrum against the library and provides a score with respect to the library
spectral. The spectra analyst uses three methods, Spectral Angle Mapping, Spectral Feature
Fitting, and Binary Encoding, produce a score between 0 and 1, with 1 equaling a perfect match.
We have put 0.33 weight on each method and tried to use Spectral AnalystTM to identify spectra.
Regions of Interest (ROIs) are used to extract statistics and average spectra from groups
of pixels. We create the ROIs of the pixels we have examined and extract statistics and spectral
plots of the selected ROIs. Compare the spectral features of each mean spectrum and may
identify some unique characteristics.
4.2 Findings:
4.2.1
Comparison of image spectra and spectral library
We compare the pixel spectra in spectral profile of SSC hyperspectral data (n_03_mosaic) with
the spectra from spectral libraries and have following findings:
Library: USGA Vegetation Spectral Library
Spectra: aspenlf2.spc Aspen_Leaf_B DW92-3
Plot File: Aspenlf2_plot.jpg
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Library: USGA Vegetation Spectral Library
Spectra: grass.spc Lawn-grass GDS91 (Green)
Plot File: Grass_plot.jpg
Library: USGA Vegetation Spectral Library
Spectra: bluespru.spc
Blue_Spruce DW92-5 Needle
Plot File: Bluespru_plot.jpg
Library: USGA Vegetation Spectral Library
Spectra: juniper.spc
Juniper_Bush IH91-4B whole
Plot File: Juniper_plot.jpg
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Library: USGA Vegetation Spectral Library
Spectra: firtree.spc
Fir_Tree IH91-2 complete
Plot File: Firtree_plot.jpg
Library: USGA Vegetation Spectral Library
Spectra: rabbit.spc
Rabbitbrush ANP92-27 whole
Plot File: Rabbit_plot.jpg
We notice from the above spectral profiles that the image apparent reflectance spectra are
best-match library spectrum from wavelength 1.5 –2.5.
4.2.2
Result of Spectral Analyst
The results from the Spectral AnalystTM are disappointed. Select the spectrum for pixel x:110,
y:1759. Figure 4.1 Spectral Analyst dialog shows that the Spectral Feature Fitting (SSF) scores
0 that means it does not match the pixel to any spectrum in USDA Vegetation library. The
highest score, 0.543, matches walnut leaf rather than grass that the pixel spectrum presents.
Figure 4.2 gives a better match with grass but SSF still scores 0.
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Figure 4.1 The Spectral Analyst Dialog for Pixel 110, 1759
Figure 4.2 The Spectral Analyst Dialog for Pixel 589, 1585
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4.2.3. Use Average Spectrum
We select an ROI and then extract statistics and a spectral plot of the selected ROI. We have
following findings:
The outcomes of ROI are close to the spectral profile of individual pixels.
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5. Problems and Future Studies
I have given opportunities by my NASA colleagues, the SSC University Affairs Officer, others
at SSC center to gain training with and exposure to new remote sensing technologies,
approaches, and processes. I have gained deep understanding and hands-on experience by
working on the AVIRIS hyperspectral data this summer.
My ultimate object of research is increase the performance of image processing using
AVIRIS hyperspectral data by grouping the useful endmenbers and reducing the bands used.
My current study only involves to basic spectral analysis. There is a great gap between theory
methodology and application.
Through the experiments, we have found that it is extremely difficult to identify
vegetation species by spectral analysis. The spectra of vegetation changes due to season,
climate, environment, growing condition, and etc. There lack of vegetation spectral libraries.
The Spectral Angle Mapper (SAM) assumes that the data have been reduced to apparent
reflectance and uses only the “direction” of the spectra, and not the “length”. Thus the SAM
classification is insensitive to illumination effects. This technique is relatively insensitive to
illumination and albedo effects when used on calibrated reference data.
It is a great challenge for me to find spectral patterns of vegetation using hyperspectral
data. I’ll continue my search with NASA and Lockheed Martin Space Operations colleagues and
also seek help from USDA research group in Westlaco, Texas.
6. References
[1]
[2]
[3]
[4]
[5]
[6]
[7]
[8]
Gonzalez, R. C. and Wintz, P., Digital Image Processing, Addison-Wesley Publishing
Company, 1977.
Lo, C. P. and Yeung, A. K.W., Concepts and Techniques of Geographic Information
System, Prentice-Hall, Inc., 2002. ISBN 013-080427-4.
Lillesand, T. M. and Kiefer, R. W., Remote Sensing and Image Interpretation, 4th
Edition, John Wiley & Sons, Inc., 2000. ISBN 0-471-25515-7.
Richards, J. A. and Jia, X., Remote Sensing Digital Image Analysis, Third, Revised and
Enlarged Edition, Springer, 1999. ISBN 3-540-64860-7.
The Environment for Visualizing Images, User’s Guide, Research Systems, Inc., 1999.
The Environment for Visualizing Images, Tutorials, Research Systems, Inc., 1999.
Multispectral Imagery Reference Guide, LOGICON Geodynamics, Inc., 1997
ERDAS Field GuideTM, Sixth Edition, Leica Geosystems, GIS & Mapping Division,
2002.
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