Using hyperspectral remote sensing for land cover classification
Wendy Zhang1, Southern University at New Orleans, New Orleans, Louisiana 70126, USA
Shobha Sriharan2, Virginia State University, Petersburg, Virginia 23806, USA
ABSTRACT
This project used hyperspectral data set to classify land cover using remote sensing techniques. Many different earthsensing satellites, with diverse sensors mounted on sophisticated platforms, are currently in earth orbit. 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. The Airborne Visible-Infrared Imaging
Spectrometer (AVIRIS) collects data in 224 bands that are approximately 9.6 nm wide in contiguous bands between 0.40
and 2.45 m. Hyperspectral sensors acquire images in many, very narrow, contiguous spectral bands throughout the
visible, near-IR, and thermal IR portions of the spectrum. The unsupervised image classification procedure
automatically categorizes the pixels in an image into land cover classes or themes. Experiments on using hyperspectral
remote sensing for land cover classification were conducted during the 2003 and 2004 NASA Summer Faculty
Fellowship Program at Stennis Space Center. Research Systems Inc.’s (RSI) ENVI software package was used in this
application framework. In this application, emphasis was placed on: (1) Spectrally oriented classification procedures for
land cover mapping, particularly, the supervised surface classification using AVIRIS data; and (2) Identifying data
endmembers.
Keywords: Hyperspectral, AVIRIS, classification, endmember
1. INTRODUCTION
Remote sensing is the science and art of obtaining information about an object, area, or phenomenon through the
analysis of data acquired by a device that is not in contact with the object, area, or phenomenon under investigation.
Remote sensing has become a powerful tool for land cover identification, the regional mapping of natural resources, and
geological features3. Digital processing of remote sensing data has gained momentum in the last decade14. One
important aspect in remote sensing is the characterization and classification of spectral measurements taken from
satellites into various features of the land surface. Pattern recognition can be carried out if appropriate procedures are
adopted for classification12.
The National Aeronautics and Space Administration (NASA) Earth Sciences program primarily focuses 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 generate enormous amounts of data that must be processed,
stored, and made available to the user community.
Imaging spectrometers are remote sensing instruments that combine the spatial presentation of an imaging sensor with
the analytical capabilities of a spectrometer. Hyperspectral remote sensing involves data collection in 32 to 1024
spectral bands, with spectral resolution as narrow as 1 nanometer (nm) 13. Hyperspectral sensors acquire images
throughout the visible, near-IR, and thermal IR portions of the spectrum. Airborne Visible-Infrared Imaging
Spectrometer (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 spectral channels (bands) with
1
2
wzhang@suno.edu; phone 504-286-5139; fax 504-284-5506
sriharan@vsu.edu; phone 804-524-6768, fax 804-524-5145
wavelengths from 400 to 2500 nm. AVIRIS has been flown on two aircraft platforms: a NASA ER-2 jet and the Twin
Otter turboprop. Because of the large number of very narrow bands sampled, hyperspectral data enable data collection
that was formally limited to laboratory testing or ground site surveys. The main objective of the AVIRIS research
program 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 predominantly focuses on understanding
processes related to the global environment and climate change.
AVIRIS data has hundreds of spectral bands, compared to broad-band multispectral scanners such as Landsat Thematic
Mapper ™, which only has 6 spectral an 1 thermal bands and spectral resolution on the order of 100 nm or greater.
Each pixel has an associated, continuous spectrum that can be used to identify the earth surface materials.
Hyperspectral imaging is a powerful tool for many applications, including pattern classification for scene analysis.
However, hyperspectral imaging can generate data at rates that challenge communication, processing, and storage
capacities.
Recently, numerous research with various approaches3,4,9,10,14,19,20 have been done for analyzing and improving
information capacity and efficiency in hyperspectral systems. A spectrally oriented classification procedure, called
endmember identification, for land cover mapping, using AVIRIS data is discussed in this paper.
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, the data patterns can be easily
retrieved, and 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.
In testing the proposed endmember base classification algorithm, it has become clear that identifying the endmember
plays a very important role. In Section 3, the process steps for identifying the endmembers are listed and the results
using unsupervised classification and endmember-based supervised classification are compared. In Section 4, we draw
conclusions and limitation from the results of our study.
2. HYPERSPECTRAL IMAGE SPECTRAL ANALYSIS AND MAPPING
2.1 Spectral Feature Fitting and Analysis
Most methods used for analysis of hyperspectral data 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.
Spectral feature fitting and analysis require conversion of data to reflectance and removal of a continuum from the
reflectance data prior to analysis. A continuum is a mathematical function used to isolate a particular absorption feature
for analysis14. It corresponds to a background signal unrelated to specific absorption features of interest. Spectra are
normalized to a 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 FittingTM (SFFTM)17 is an absorption-feature-based method for matching image spectra to reference
endmembers. 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 16. 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 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-by-band
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 Angle Mapper (SAM)10 is a physically-based spectral classification that uses the n-dimensional 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 bands16.
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 matches unknown spectra to library spectra and provides a score with respect to the library
spectra.
The Binary Encoding classification technique encodes the data and endmember spectra into 0s and 1s based on whether
a band falls below or above the spectrum mean16. An exclusive OR function is used to compare each encoded reference
spectrum with the encoded data spectra and a classification image produced. All pixels are classified to the endmember
with the greatest number of bands that match. Some pixels may be unclassified if the user-specified minimum match
threshold is not exceeded.
2.2 Experiment Data and Reference Libraries
The experiment was set up to find the appropriate endmembers to identify unique vegetation spectral signatures of
Stennis Space Center using AVIRIS data acquired on July 29, 1999. The data file used was AVIRIS apparent reflectance
mosaic data of part of Stennis Space Center, Mississippi, USA, calibrated using Atmospheric Removal (ATREM)
atmospheric modeling software. The data covers the 0.3704 to 2.5101 micron (μm) range in 224 spectral bands and the
image data file size was 984 megabyte (MB). The software used to complete this project was the Environment for
Visualizing Images (ENVI) version 3.6 running on UNIX systems. The spectral libraries employed were the United
States Geological Survey (USGS) Vegetation Spectral Library (wavelength is 0.3951 to 2.56 μm and the wavelength
accuracy is 0.5 nm in the near-IR and 0.2 nm 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).
2.3 Methodology
Traditional supervised and unsupervised classification techniques require very long processing times for hyperspectral
data because of the number of bands of data (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. When training supervised classifiers a minimum ratio of the number of training pixels to number of spectral
bands is needed to ensure reliable estimates of class statistics are obtained. As the dimensionality of data set increases,
the minimum number of training pixels per class must be increased to preserve the accuracy of the statistical estimates.
Adding more spectral bands, as in the case of AVIRIS, is not helpful unless more training pixels per class are available.
This turns out to one of the major limitations in attempting to apply traditional image classification procedures. 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 in classifying images
using AVIRIS hyperspectral data.
Image spectra were browsed and compared to the spectral library that was provided by different agencies and labs.
When visually comparing and contrasting the corresponding AVIRIS spectra with the features shown in the laboratory
spectra, we found how similar the two spectra were. A spectral profile was created by putting the image spectra and
similar library spectra together.
ENVI has a spectral matching tool, the Spectral AnalystTM 17. The Spectral AnalystTM scores the unknown spectrum
against the library and provides a score with respect to the library spectra. The spectral analysis used three methods,
Spectral Angle Mapping, Spectral Feature Fitting, and Binary Encoding, to produce a score between 0 and 1, with 1
equaling a perfect match. Equal weight (0.33) was put on each of the three methods when using Spectral AnalystTM to
identify spectra.
Regions of Interest (ROIs) were also used to extract statistics and average spectra from groups of pixels. We created the
ROIs of pixels and then we examined and extracted statistics and spectral plots of the selected ROIs. We expected that
comparing the spectral features of each mean spectrum could identify some unique characteristics.
2.4 Findings
2.4.1 Comparison of image spectra and spectral library
We compared the pixel spectra in the spectral profile of SSC hyperspectral data with the spectra from spectral libraries
and had the following findings:
Figure 2.1
Library: USGS Vegetation Spectral Library
Spectra: aspenlf2.spc Aspen_Leaf_B DW92-3
Plot File: Aspenlf2_plot.jpg
Figure 2.2
Library: USGS Vegetation Spectral Library
Spectra: grass.spc Lawn-grass GDS91 (Green)
Plot File: Grass_plot.jpg
Figure 2.3
Library: USGS Vegetation Spectral Library
Spectra: bluespru.spc
Blue_Spruce DW92-5 Needle
Plot File: Bluespru_plot.jpg
Figure 2.4
Library: USGS Vegetation Spectral Library
Spectra: juniper.spc
Juniper_Bush IH91-4B whole
Plot File: Juniper_plot.jpg
Figure 2.5
Library: USGS Vegetation Spectral Library
Spectra: firtree.spc
Fir_Tree IH91-2 complete
Plot File: Firtree_plot.jpg
Figure 2.6
Library: USGS Vegetation Spectral Library
Spectra: rabbit.spc
Rabbitbrush ANP92-27 whole
Plot File: Rabbit_plot.jpg
Figure 2.1 showed the spectra of aspen leaf. Figure 2.2 showed the spectra of lawn grass. Figure 2.3 showed the
spectra of blue spruce needle, Figure 2.4 showed the spectra of whole juniper bush. Figure 2.5 showed the spectra of
complete fir tree. Figure 2.6 showed the spectra of whole rabbit brush. We noticed from these figures that our image
over Stennis apparent reflectance spectra best-matched the library spectrum from wavelength 1.5 –2.5. We compared
our vegetation spectra to various spectral graphs from the USGS library in these figures. It was hard to identify any
particular vegetation by just visualizing these figures.
Figure 2.7 The Spectral Analyst Dialog
2.4.2
Result of Spectral Analyst
The results from the Spectral AnalystTM were disappointing. Figure 2.7 Spectral Analyst dialog shows that the Spectral
Feature Fitting (SSF) scores 0, meaning it does not match the pixel to any spectrum in the USGS Vegetation library.
The highest score, 0.543, matches walnut leaf rather than grass the pixel spectrum presents. Figure 2.8 gives a better
match with grass but the SSF scores 0.
2.4.3 Use Average Spectrum
We selected an ROI and then extracted statistics and created a spectral plot. The outcomes of ROI were close to the
spectral profile of individual pixels as showed in Figure 2.8. Our expectation of using spectra to identify vegetation
endmember was faded.
Figure 2.8 The Spectral Analyst Dialog of ROI
3. ENDMEMBER-BASED SUPERVISED CLASSIFICATION
Since AVIRIS data has hundreds of spectral bands, traditional image processing and data handling techniques face
difficulties. The classification accuracy first grows and then declines as the number of spectral bands increases, which
is often referred to as the Hughes phenomenon7. In supervised classification, 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. This situation creates the Hughes phenomenon14. 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. It is not feasible to use regular classification methods to classify images using AVIRIS hyperspectral data.
The experiment was set up to find the appropriate way to classify vegetation of Stennis Space Center using AVIRIS data.
The data covers the 0.3704 to 2.5101 μm range in 224 spectral bands and the image data file size is 984 MB. The
software employed for this project was the ENVI version 4.0 running on a Windows system. The following steps were
used for this methodology.
Step1: Data Preprocessing
The high spectral resolution of hyperspectral data includes atmospheric absorption features will be detected and
displayed. ATREM program overcomes a difficulty with the other approaches in removing water vapor absorption
feature in AVIRIS data. Evaluate the ATREM correction applied to the AVIRIS spectral radiance to remove the bulk of
the solar and atmospheric effect6. The AVIRIS data set used for this project was already converted from radiance to
reflectance. We viewed the spectral profile to identify the atmospheric absorptions. Bands 1-4, 107-116, and 152-168
contained the atmospheric absorption windows.
Figure 3.1 Spectral Profile and Absorption
Step 2: Resize Data Spectrally and Spatially
Much of the additional data recorded by hyperspectral sensors does not add to the inherent information content for a
particular application. There is often substantial overlap of information content over the bands of data for a given pixel.
Feature reduction would be an important preprocessing step to image analysis. Bands 1-4, 107-116, and 152-168 were
subset out for spectral reduction. 192 bands remained. The specific study area was subset too.
Step 3: Reduce Dimensionality of Data
The minimum noise fraction (MNF) transformation is used to determine the inherent dimensionality of image data,
to separate noise in the data, and to reduce the computational requirements for subsequent processing17. The inherent
dimensionality of the data is determined by examination of the final eigenvalues and the associated images. Eigenvalues
for bands that contain information will be an order of magnitude larger than those that contain only noise.
Applied the MNF transform to the subset AVIRIS data and viewed MNF eigenvalue plots to determine break-in-slop and
MNF cut-off between “signal” and “noise”. Bands with high eigenvalue were selected to reduce the spectral bands from
192 to 30.
Figure 3.2 MNF Eigenvalue Plot
Step 4: Determine Purity of Pixels
The Pixel_Purity_IndexTM (PPITM)16 is a means of finding the most “spectrally pure” or extreme, pixels in a
multispectral and hyperspectral images in ENVI. Separating pure from more mixed pixels reduces the number of pixels
to be analyzed for endmember determination and makes separation and identification of endmembers easier. The most
spectrally pure pixels typically correspond to endmembers. Pure image pixels are identified by n-dimensional data
cloud (n-dimensional scatter plots) onto a random unit vector. The extreme pixels in each projection are recorded and
the total number of times each pixel is marked as extreme is noted. A PPI image is created in which the digital number
(DN) of each pixel corresponds to the number of times that pixel was recorded as extreme.
We applied PPI analysis to the MNF output by using the first 30 bands. The number of iteration was set to 10,000 and
the number of iteration per block was 250 to ensure that some vegetation pixels were picked. Since there are few pure
vegetation pixels, the ‘tolerance factor”, the threshold factor in standard deviation was set to 2.5. The total number of
pixels picked was 82,199.
Step 5: Define Region of Interest (ROI) of PPI
To visualize pixels in the n-Dimensional scatter plot, a ROI from the PPI image must be defined. Our PPI image has
a minimum value of 0 (pixels that were never identified as pure) and a maximum value of 668 (pixels that were
identified 668 times during the PPI iteration). Traditionally the ROI would be one that includes all pixels with high PPI
values. However, the vegetation pixels identified by the PPI, valued from 2 to 8 in our study case. All pixels that were
hit between 2 and 8 times were selected and a ROI was created from them.
DN
# of Pixels
Color
2
13387
Red
3
4564
Green
4
2074
Blue
5
1135
Yellow
6
712
7
499
8
378
Figure 3.3 RIO of PPI
Cyan
Purple
Maroon
Step 6: n-Dimensional (N-D) Visualization
Spectra can be thought of as points in an n-dimensional scatter plot, where n is the number if bands. The coordinates
of the points in n-space consist of “n” values that are simply the spectral radiance or reflectance values in each band for
a given pixel. The distribution of these points in n-space can be used to estimate the number if spectral endmembers
and their pure spectral signatures.
n-Dimensional VisualizerTM 17 provides an interactive tool for selecting the endmembers in n-space where n is the
number of bands of interest. The most important pixels, those best suggest the endmember materials, are the purest
pixels, previously selected using PPI threshold. Those bands encompass almost all the signal variability and limiting the
number of bands will improve the interactive visualization performance.
We used N-D Visualizer to separate the pure pixels into a set of endmembers. Thirty bands from the MNF data were
used in this process.
Figure 3.4 N-D Visualizer
Step 7: Endmember-based Supervised Classification and Unsupervised Classification
The sensitivity to category variance can be considered by the range of values in each category training set. This range
may be defined by the highest and lowest digital number values in each band appeared as a rectangular area. An
unknown pixel is classified according to the category range, or decision region. The multidimensional analogs of these
rectangular areas are called parallelepipeds.
The parallelepiped classifier is a very simple supervised classifier14. Parallelepiped classification uses a simple
decisional rule to classify hyperspectral data. The decision boundaries form an n-dimensional parallelepiped in the
image data space. The dimensions of the parallelepiped are defined based upon a standard deviation threshold from the
mean of each selected class. The parallelepiped classifier is very fast and efficient computationally 17. We used the
established ROIs of PPI as the trained signature and set standard deviation as 2.0 in parallelepiped classification.
Uunsupervised classification calculates class means evenly distributed in the data space and then iteratively clusters the
pixels using minimum distance techniques. Each iteration recalculates class means and reclassifies pixels with respect
to the new means. Iterative class splitting, merging, and deleting are done based on input threshold parameters. All
pixels are classified to the nearest class. This process continues until the number of pixels in each class changes by less
than the selected pixel change threshold or the maximum number of iterations is reached 16. We applied unsupervised
classification on the MNF subset data. The first thirty bands were used to generate the image with ten classifications.
Comparing image using the endmember-based supervised classifier (Figure 3.5) with the image using unsupervised
classifier (Figure 3.6), Figure 3.5 gave much more detail of the vegetation. The area we were interested was well
classified such as pixels of cogan grass were identified.
Figure 3.5 Endmember-based Classification
Figure 3.6 Unsupervised Classification
4. CONCLUSIONS, LIMITATIONS, AND FUTURE STUDIES
Analysis of land cover is important for ecosystem and environmental management. When image data is available in
digital form, spatially quantified into pixels and radiometrically quantified into discrete brightness levels, several
approaches are possible to extract information. The image classification procedure automatically categorizes all pixels
in an image into land cover classes or themes. This can be accomplished by performing unsupervised and supervised
classification using software such as ENVI. Classification can be extended into agriculture research. The data collected
through classification of study site can be applied in precision agriculture and management of invasive species control
strategies.
There is a gap between theory, methodology and application. Through these experiments, we have found, it is
extremely difficult to identify vegetation species by spectral analysis itself. The spectra of vegetation changes due to
season, climate, environment, and growing condition. As with the image restoration and enhancement techniques, there
is no single correct manner in which to approach an image classification problem. Image classifiers may be used in
combination in a hybrid mode. The particular approach one might take depends upon the nature of the data being
analyzed, the computational resources available, and the intended application of the classified data.
All of experiments were conducted in the computer lab. We did not have any true ground picture, timely image data,
and hyperspectral vegetation libraries. Without ground truthing, there is no validation for our study.
It is a great challenge for us to identify the vegetation species using hyperspectral data. We will seek opportunity to
continue our search with colleagues in NASA Stennis Space Center. We need to acquire more timely data and ground
sampling and truthing to verify if we can indeed identify invaded species such as cogan grass. The ultimate objective of
our research is to increase the performance of image processing using AVIRIS hyperspectral data.
ACKNOWLEDGEMENTS
This research project is supported by NASA Faculty Fellowship Program (NFFP). We are very grateful to Dr. Ramona
Travis, University Affairs Officer, NASA Stennis Space Center, Dr. Eddie Hildreth and Dr. James Miller, Directors of
NFFP, for providing assistance and encouragement during the program. Special thanks to Ms. Guest and Mr. Lloyd
McGregor of Remote Sensing Directorate, Lockheed Martin Space Operations, Stennis Programs for the mentoring and
helping.
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