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Comparison of Lithologic Mapping with
ASTER, Hyperion, and ETM Data in the
Southeastern Chocolate Mountains, USA
Xianfeng Zhang and Micha Pazner
Abstract
An empirical comparison of the EO-1 Hyperion, EOS ASTER,
and Landsat ETM sensors was performed to examine the utility
of these three sensors for gold-associated lithologic mapping
in the southeastern Chocolate Mountains area, California.
Three images were evaluated with respect to three aspects:
classification accuracy, matched filtering score index, and
separability of the five significant rock types in the study area.
The results show that the classifications from Hyperion
and ASTER data are mostly similar with an overall accuracy
of over 85 percent and kappa coefficient 0.81. Due to the
presence of more SWIR and thermal bands, the Hyperion and
ASTER images can achieve better lithologic mapping than
ETM. The assessment of matched filtering score index and
the separability also supports these findings. Hyperion can
discriminate more similar classes than ASTER and ETM, while
the better availability and spatial coverage makes the ASTER
sensor more suitable for large-area lithologic mapping.
Introduction
Spectral remote sensing has the potential to provide detailed
mineralogy, chemistry, and morphology of the Earth’s surface.
This information is useful for mapping potential host rocks,
alteration assemblages, and regolith characteristics (Papp and
Cudahy, 2002; Kruse et al., 2003; Perry, 2004) in the context of
gold exploration. In the early stage of remote sensing development (1970s and 1980s), geologic applications and mineral
exploration were among the most important applications of
this technology. Spaceborne multispectral systems such as
Landsat MSS, TM, and SPOT offered four to seven spectral bands.
Landsat MSS data typically was interpreted for structural and
geomorphic features at regional map scales (Perry, 2004), and
used to identify alteration associated mineral deposits (Abrams
et al., 1983; Goetz et al., 1983). Landsat Thematic Mapper (TM)
imagery has been used more routinely for mineral exploration
because the two shortwave infrared (SWIR) bands may be useful
for predicting alteration mineral associations (Knepper and
Simpson, 1992; Spatzm and Wilson, 1994; Sabine, 1997). In
the late 1990s and early 2000s new spaceborne multispectral
and hyperspectral remote sensors such as ASTER and Hyperion
were launched and provided more and better remotely-sensed
data for mineral exploration. These developments have enabled
remote sensing technology to become an increasingly important
tool for mineral exploration, in particular when no or few
detailed topographic and geologic maps are available, and
where ground access is difficult or sensitive (e.g., military
areas) (Perry, 2004).
Generally speaking, the mineral spectral features in the
visible to near-infrared (VNIR) wavelength region are largely
related to the charge transfer effect of electrons between
energy levels of constituent elements, especially the transitional metals, Fe, Mn, and Cr (Hunt et al., 1971). The
VNIR wavelength region is potentially useful for mapping
gossans, rich in iron and associated with weathered sulphide occurrence, as well as regolith characterization (Papp
and Cudahy, 2002). The mineral spectral features in the
SWIR are largely related to the overtones and combination
tones of vibrationals of octahedrally coordinated cations
(typically, Al, Fe, Mg) bonded with OH groups (Hunt and
Vincent, 1968). This wavelength region is potentially useful
for mapping alteration minerals, carbonates, and regolith
characterization (Gupta, 2003). Mineral spectral features in
the thermal infrared (TIR) wavelength region are related to
fundamental vibrations (bends and stretches) of Si-O bonds
in various structural environments (Lyon, 1965). The
TIR region spectrum is useful for characterizing features
exhibited by many rock-forming mineral groups such
as silicates (e.g., quartz, feldspars, and pyroxenes) and
carbonates. Physical properties such as grain size and
packing can produce changes in emission spectra in terms
of relative depth of the absorption, but not in the position
of the spectral band. Thus, the TIR region is useful for
mapping lithologies and rock-forming minerals (Jensen,
2002; Papp and Cudahy, 2002; Gutpa, 2003).
In the past several years scientists and practical users
have had a growing choice among several spaceborne and
airborne remote sensing systems. Among them Landsat ETM,
the Advanced Spaceborne Thermal Emission and Reflection
Radiometer (ASTER) and Hyperion are components of the
NASA EOS constellation of satellites, an international collaboration of spaceborne imaging systems. The EOS ASTER sensor
is the first spaceborne instrument with a thermal subsystem
(Abrams and Hook, 2001), while the Hyperion sensor is the
first spaceborne hyperspectral instrument which has both
Xianfeng Zhang is with the Institute of Remote Sensing &
GIS, Peking University, Beijing, China 100871
([email protected]).
Photogrammetric Engineering & Remote Sensing
Vol. 73, No. 5, May 2007, pp. 555–561.
Micha Pazner is with the Department of Geography,
University of Western Ontario, London, Canada N6A 5C2
([email protected]).
0099-1112/07/7305–0555/$3.00/0
© 2007 American Society for Photogrammetry
and Remote Sensing
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and SWIR wavelength regions. The Landsat TM/ETM
sensor is a well-known multispectral instrument which
provides data for a variety of applications such as landcover classification and lithologic mapping. A graphic
comparison of ASTER and ETM sensors is shown in Figure 1,
and Table 1 sums up the characteristic of these three
sensors. This raises the question: what is the relative
performance of these three sensors for lithologic or mineral
assemblages mapping? The answer to the question is
significant for both the remote sensing and the geology and
mineral exploration applications communities. In this paper,
we present a comparative study of the performance of ETM,
ASTER, and Hyperion data for lithologic and mineral assemblages mapping.
VNIR
Methodology
Study Area and Geologic Background
The southeastern Chocolate Mountains (32.9° N, 115.5° W)
mineral district is located in the southeast part of Imperial
County, California, and is used as the study area for this
comparative study. The area contains the Picacho and
Potholes gold districts and is bounded by the limits of the
range on the south, by the Colorado River on the east and
north, and by the Carrizo Wash on the west (Morton, 1977).
Early mineral production of gold in California took place in
this district. The southeastern Chocolate Mountains district
has been mined for lode and placer gold, silver, lead, and
Data and Methodology
A subscene of a Hyperion image acquired on 09 April 2002 was
used for the comparative study. An ETM image of 05 January
2000, and an ASTER image of 03 October 2003 were geo-corrected
and cropped to cover an area that overlaps the Hyperion image.
For purposes of comparison, the ETM thermal bands and ASTER
VNIR and TIR bands were resampled to 30 meters. The comparison was then made empirically with regards to three aspects:
the accuracy of lithologic classification, average matched
filtering (MF) score, and the spectral separability of the ETM,
ASTER, and Hyperion data. The maximum likelihood classification and matched filtering method (Boardman et al., 1995)
were employed for lithologic information retrieval from ASTER,
Hyperion, and ETM data. The average and minimum Bhattacharyya distance was used as a separability indicator for
significant rock types in the study area.
Figure 1. Comparison of spectral bands between
Landsat-7 ETM and EOS ASTER.
TABLE 1.
Sensors
(Swath)
ETM
CHARACTERISTICS
No. of Bands
VNIR
4
SWIR
TIR
VNIR
SWIR
TIR
VNIR
SWIR
TIR
2
1
3
6
5
72*
172*
0
(185 km)
ASTER
(60 km)
Hyperion
(7.5 km)
*Only 196 unique bands are available in the
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May 2007
USGS
copper (Morton, 1977; Liebler, 1987). A subset of the Hyperion scene covers the middle part of this area from southwest
to northeast. Figure 2 shows the location of the study area
(Figure 2a) and a reproduced geologic map of the Hyperion
coverage (Figure 2b).
The oldest rock unit exposed in this area is quartz
biotite gneiss, which is tentatively correlated with the
Precambrian Chuckwalla Complex (mc) (Morton, 1977;
Liebler, 1987). The next rock unit is pre-Tertiary Orocopia
Schist (mso), which crops out in a several-square-mile area
bordering the Colorado River from Picacho to Ferguson
Lake. By far the most abundant rocks in the area are Tertiary
volcanic rocks of widely variant composition and types.
The Pliocene basalt flows (Tv b) cap most of the mountains
between Senator Wash and the southwestern limit of the
range. Most of the volcanic and pyroclastic rocks (Tv) crop
out in a wide west-northwest-trending belt extending from
the Colorado River between Ferguson and Senator Wash
all the way to the western limit of the southeastern Chocolate Mountains district. A small portion of unsorted early
Tertiary sedimentary breccias (Tbr) and pre-Tertiary granitic
rocks (gr) was observed at the southeastern corner of the
study area.
The lode gold deposits of the district are pre-Tertiary in
age; none of the volcanic rocks appear to be associated with
the mineral deposits as they are in some other southern
California areas (Morton, 1977; Willis, 1987; Durning et al.,
1998). Based on the analysis of the existing mines, prospecting for lode gold in the southeastern Chocolate Mountains
district should be focused in pre-Tertiary metamorphic
rocks, among which gneiss appears to be the more favorable
host of the gold deposits. Mapping these rock units (Table 2)
is significant for searching for gold and understanding the
regional geology.
OF THE ETM, ASTER, AND
HYPERION SENSORS
Wavelength
Range (m)
Spatial
Resolution (m)
0.45–0.90
1.55–1.75
2.09–2.35
10.40–12.50
0.52–0.86
1.60–2.43
8.125–11.65
0.40–1.00
1.00–2.50
N/A
30 m
60 120/discrete
30
60
15
30
90
300/discrete
2100/discrete
60–80/discrete
100/discrete
1000/discrete
m
m
m
m
m
30 m
Spectral
Gap (nm)
Signal
Quantization
8 bits
8 bits
8 bits
12 bits
10/continuous
16 bits
Hyperion Level 1 product.
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which usually occur on a few fixed columns, and intermittent stripe patterns with different positive or negative DNs,
which usually are discontinuous and occur in a random
way. The former was removed by averaging the intimate
left and right neighboring columns, while the latter was
removed by using a forward-inverse maximum noise fractions (MNF) transformation approach (Zhang, 2005). After
the destriping, the Atmospheric Correction Now (ACORN)
program was employed to convert the radiance at-sensor
to surface apparent reflectance (Analytical Imaging and
Geophysics, LCC, 2002). Minimum noise fraction (MNF)
transformation (Green et al., 1988) was then applied to the
Hyperion data and the first 30 MNF bands were extracted as
features for further lithologic classification.
The maximum likelihood classifier (MLC) was used to
classify lithologic units. The overall classification accuracy is
about 86 percent and the Kappa Coefficient is 0.81 when the
geologic map (Morton, 1977) is used as reference data. Ground
verification also indicated that lithologic mapping from the
Hyperion image was accurate and helpful for gold exploration.
In the comparative study, the result from the Hyperion image
is used as a benchmark to check the utility of ASTER data and
ETM data. The aforementioned classifier was later also used to
extract rock type information from ASTER and ETM.
Figure 2. The location of the southeastern Chocolate
Mountains (a) and generalized geologic map (b)
(adapted from Morton, 1977).
Comparison Based on MLC Classification
Lithologic Classification of Hyperion Data
Prior to further analysis, the Hyperion image was preprocessed, including the removal of vertical stripes and
atmospheric correction. The vertical stripes were processed
as two categories: continuous with positive or negative DNs,
TABLE 2.
Rock Types
SIGNIFICANT LITHOLOGIC GROUPS
Age
Tc: Clastic rocks
Tertiary
Tvb: Basalt flows
Tertiary
Tv/Tvb: Volcanic rocks
Tertiary
Tbr: Tectonic breccia
Tertiary
gr: Granitic rocks
pre-Tertiary
mso: Orocopia Schist
pre-Tertiary
mc: Chuckwalla Complex
Precambrian
PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING
Lithologic Information Extraction from ASTER
The MLC method was applied to classify the subset of
the ASTER surface radiance image (AST_09) which covers
the Hyperion scene area. The three VNIR bands and six
SWIR bands were used for the classification. Three steps
were used to classify the ASTER data. First, the minimum
noise fraction (MNF) transformation was applied using the
nine bands, to extract features for the classification. The
noise level of the image was estimated from a patch of
homogeneous sand deposit using a method called “shift
difference” (RSI, 2003). Second, training samples were
interactively selected based on false color composite
images of three MNF bands. Finally, noisy MNF bands were
excluded and a subset of the bands was used to perform
the classification based on the selected training samples.
The classification result was then geocorrected and cropped
to the Hyperion scene coverage. The accuracy of the ASTER
image classification was assessed using the error matrix
method (Congalton, 1991). The geologic map (Morton,
1977) was used as reference data. The overall accuracy is
IN THE
STUDY AREA
Compositions
moderately to poorly sorted, consolidated
siltstone, sandstone, and conglomerate
Fine-grained basalt and minor
interbedded conglomerate
Various volcanic rocks such as
intrusive (Tvi), pyroclastic (Tvp),
andesitic (Tva).
Pale, gray-yellow, poorly sorted
breccias, largely metavolcanic and
metasedimentary rocks
Biotite granite, leucigranite,
quartz diorite, quartz monzonite
Sericite albite schist, quartz sericite
schist, biotite schist, phillite,
quartzite, and actinolite schist
Quartz diorite gnesis (mc),
foliated hybrid granitic rocks,
and granophyres
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TABLE 3.
ERROR MATRIX
OF THE
CLASSIFICATION MAP DERIVED
FROM ASTER
Reference Data (pixels)
Classification
gr
mc
Tv Tvp
Tvb
mso
Tbr
Producer’s Accuracy (%)
gr
mc
Tv Tvp
Tvb
mso
Tbr
User’s Accuracy (%)
1478
0
38
15
4
202
85
17
487
666
949
85
227
20
317
693
23967
772
2674
657
82
6
2253
3307
24567
120
394
80
1189
0
2393
53
21323
2997
76
409
351
621
692
181
885
28
43
13
77
91
87
17
Overall Accuracy 76.5%/85.9%*
Kappa Coefficient 0.68/0.81*
*The accuracy when thermal bands are included in the classification.
about 76 percent with Kappa Coefficient 0.68 (Table 3).
The rocks gr, Tv Tvp, mso, and Tvb can be identified
with a producer’s accuracy over 80 percent. The rocks mc
and Tbr cannot be classified very well.
Spectral response in the TIR region is characterized by
spectral features exhibited by many rock-forming mineral
groups such as silicates, carbonates, oxides, nitrites,
hydroxyls (Gupta, 2003; Jensen, 2002). The ASTER sensor
has five thermal bands covering the wavelength range from
8 to 12 m. For comparison purposes, the five thermal
bands of the ASTER image were then included in the data
set. The MLC classifier was then re-applied to the 14 ASTER
bands and the lithologic information was extracted once
more using the same training sites. Compared with the
classification result without the thermal bands, the accuracy of the classification with thermal bands was much
improved (Figure 3a). The overall accuracy is improved
from 76.5 percent to 85.9 percent, and the Kappa Coefficient improved from 0.68 to 0.81 (Table 3). The significant
improvement is mainly in the rocks gr, mso, and Tvb,
whose constituents such as quartz, granite, biotite granites,
and bulk SiO2 (in Tvb and Tv), have spectral absorption
features in TIR region. This result indicates that compared
with other multispectral imagery (e.g., TM), ASTER data can
achieve better lithologic mapping due to more bands in
both SWIR and TIR regions.
Comparison of the Classification Results
The lithologic classes in the study area extracted from
both ASTER and Hyperion imagery are largely identical
(Table 4 and Figure 3). Compared with the geologic map
which is used as a reference, the rocks mc, Tc, and Tbr
cannot be extracted accurately both from ASTER and
Hyperion. On the other hand, Figure 3 shows that the
result from Hyperion looks more homogeneous in a single
rock unit than that from ASTER. Some sub-types of rock
mso were misclassified as Tbr and Tv on the ASTER
imagery. And some Tv or Tvp was also misclassified as
other rock types using the ASTER image data (Figure 3a).
The rock Tc cannot be differentiated from the rock mso at
all on the ASTER image. This indicates that ASTER data has
difficulty in differentiating some rock types with minor
spectral differences. In retrospect, Hyperion hyperspectral
data is more powerful for discriminating similar rock
units with minor spectral difference because it has over
196 unique narrow spectral bands.
In order to further assess the utility of ASTER data for
lithologic information extraction, the Landsat ETM data
was compared with ASTER and Hyperion. The ETM and its
predecessor TM and MSS data were widely-used multispectral imagery for mapping lithologic information and mineral
558
May 2007
Figure 3. The classification results from ASTER (Using 14
bands) (a) and Hyperion (b) imagery.
TABLE 4.
Rock Types
COMPARISON
OF THE CLASSIFICATION
ASTER AND HYPERION DATA
mso
Percentage of overlap (%)
87.3
Overall matching accuracy: 86.3%
RESULTS USING
Tv Tvp
Tvb
gr
Tbr
86.8
95.4
78.2
49.6
assemblages in the last thirty years or so (Gupta, 2003;
Perry, 2004). A subset of an ETM scene covering the same
area as the Hyperion scene was used to extract the lithologic information using the MLC classifier. With the geologic
map used as reference data, the overall accuracy of the
classification is about 51 percent which is much lower than
the classification results from Hyperion and ASTER. The
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rocks gr, Tv Tvp, mso, and Tbr can be extracted from the
ETM data, but their classification accuracy is lower than 70
percent. The errors stem from the misclassification at the
class boundary areas, as well as the misclassification of
subtypes of a same rock type. Most of the rock-forming
minerals are characterized by atomic-molecular vibrational
processes occurring in the SWIR and TIR regions of the
electromagnetic (EM) spectrum. Consequently, ETM data has
inherent difficulty in identifying the lithologic information
due to the lack of sufficient information from SWIR and TIR
wavelength regions.
The overall classification accuracy of ASTER data (including the thermal bands) is about 86.3 percent when the
Hyperion classification result is considered the benchmark
(Table 4). The rock units of mso, Tv, and Tvb extracted from
ASTER and Hyperion images exhibit excellent correlation to
each other with a matching accuracy of over 87 percent. The
rock Tbr extracted from ASTER and Hyperion had a low
matching accuracy. This is due to the fact that only a small
area of Tbr rock was exposed in the study area. Thus, the
misclassification at the boundary of different rocks significantly reduced the classification accuracy of Tbr. However,
the overall performance in the study area indicates that
ASTER imagery is generally useful to extract significant
lithologic types for gold exploration, especially where no
hyperspectral image data is available.
TABLE 5.
AVERAGED
MF
SCORES
ROCKS TYPES
OF THE
Sensors
mso
Tv Tvp
Tvb
gr
mc
Tbr
Hyperion
0.42
0.10
0.05
0.39
0.28
0.15
0.48
0.35
0.23
0.58
0.47
0.07
0.22
0.29
0.15
0.35
0.09
0.11
ASTER
ETM
Figure 4. Average MF score of six rock types extracted
from three different sensors: Hyperion, ASTER, and ETM.
Comparison Using Abundance Images
Further comparison using abundance images was made to
show the different capability of Hyperion, ASTER, and ETM
data for lithologic information extraction in the study area.
First, the matched filtering (MF) detector was used to extract
abundance images from Hyperion, ASTER, and ETM data. The
matched filtering detector performs a partial un-mixing of
spectra to estimate the abundance of user-defined endmembers from a set of reference spectra (Boardman et al. 1995;
RSI, 2003). This technique does not require knowledge of
all the endmembers within an image scene, and can also
be used to identify single feature types. It detects a target
spectral signature against a background of unknown spectra,
and produces a grayscale image (MF scores) showing the
relative abundance of the target material for each image cell.
The MF scores indicate the degree of how well unknown
pixels were matched with the endmember materials. If a
linear mixing process is observed, the MF scores at a pixel
are proportional to the abundances of endmembers (Harsanyi
et al., 1994; Boardman et al., 1995; RSI, 2003). Taking the
geologic map as a reference, the polygon boundaries of the
rock units mso, Tv Tvp, Tvb, gr, mc, and Tbr were created
using the ESRI® ArcGIS™ software system. These polygons
were then used to create masks of the six rock types. Averaged MF score for each rock type was then calculated from
the abundance images extracted from Hyperion, ASTER, and
ETM data, respectively (Table 5 and Figure 4).
The averaged MF scores here were used as an indicator of
how well the extracted rocks were matched with the selected
image endmembers. In other words, a good match between
unknown pixels and the endmember material indicates this
material can be resolved from the image pixels. Thus, a
higher average score of a rock shows that most pixels inside
the image polygonal area of this rock can be identified
accurately. Conversely, a lower averaged score may indicate
that only part of the pixels were recognized as this type of
rock, or that most of the pixels can only be matched with the
known rock type with a low matching degree. The spectral
variations of a rock type and the generalization on geologic
maps may also result in a lower MF score. Table 5 and Figure 4
show that most rocks can be identified from Hyperion data
PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING
with higher average MF scores, ASTER comes in second, and
can only recognize these rocks with quite low average MF
scores. For example, the granitic rocks (gr) were well identified from the Hyperion and ASTER images as the units on the
geologic map, but it was difficult to discriminate granitic
rocks on the ETM image. Thus, the comparison indicates
that ASTER data is much better than ETM data for lithologic
mapping in the study area.
ETM
Discriminating Capability of ASTER and ETM
The TM (and ETM) sensor has two SWIR bands that may be
used to predict alteration mineral associations (Rowan et al.,
1977; Podwysocki et al., 1984; Sultan, 1987; Knepper and
Simpson, 1992; Spatzm and Wilson, 1994; Sabine, 1997).
However, TM SWIR bands have difficulty in differentiating
types of clays, sulfates, and carbonates effectively (Perry,
2004). In contrast, the ASTER instrument offers six SWIR
bands and five thermal bands, which can enhance the
lithologic and mineral information extraction. Few publications exist on ASTER techniques applied to mineral exploration and lithologic mapping at this time. The classification
study described above has proved that ASTER data is more
powerful than ETM/TM for lithologic mapping. This section
will explore the relative utility of ASTER and ETM data for
lithologic information extraction based on separability of
various band selections.
The ASTER image (AST_09) acquired on 03 October
2003 and the ETM image acquired on 05 January 2000,
were selected, geocorrected, and registered. A spatial
subset of the two images which cover the study area was
made for the comparative study. Fourteen ASTER bands
plus eight ETM bands (bands e6 and e7 in Figure 6 are the
low-gain and high-gain calibration of the original ETM
thermal band) were stacked, and a 22-band image stack
was created for the band selection. The Bhattacharyya
distance was used to measure the pairwise class separabilMay 2007
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ity. It assumes that the two classes are Gaussian in nature,
and that the means and covariance matrices can be estimated from the training samples (Jensen, 1996; Hoffbeck
et al., 1996; Lillesand and Keifer, 1999). The five rock types
in the study area, biotite gneiss (mc), granitic rock (gr),
volcanic rocks (Tv), Tertiary basalt flows (Tvb), and Orocopia schist (mso), are used to evaluate which bands of the
image stack can achieve greater separability. The sum of
average and minimum Bhattacharyya distance between
these five classes is used to sort all the possible subsets of
the twenty-two bands. The number of subsets of bands
ranges from 2 to 22. For example, suppose two bands are
selected, then C222 231 combinations need to be evaluated, and the two bands that can produce largest sum of
average and minimum Bhattacharyya distance will be
selected as an optimum combination. Typical samples from
the five rock types were selected for estimating the mean
vector and covariance matrix. This was done with the help
of a 1:125 000 geologic map, field photography, and onemeter, false-color aerial orthophotos. The band selection
process was illustrated using the flowchart in Figure 5.
The band combinations with the largest sum of average
and minimum Bhattacharyya distance between the five rock
types are illustrated in Figure 6. The analysis of the results
suggests the following empirical conclusions: (a) ASTER SWIR
bands are significant for lithologic discrimination in the
study area, as fewer bands were selected; for instance, ASTER
bands 1, 4, 5, and 9 were selected in the case of four bands;
(b) When more than 10 bands were used to calculate the
Bhattacharyya distance, the ETM high-gain thermal band (e7
in Figure 6) and ASTER thermal bands become important for
classifying these five rock types. (This is due to the fact that
many rock-forming mineral groups have diagnostic spectral
features in thermal wavelength regions (Gupta, 2003)); and
(c) The VNIR bands, especially ASTER bands 1 and 2 and
ETM band 1, are also useful for differentiating iron oxide
rock-forming mineral assemblages. Thus, these bands also
contribute to the class pairwise separability. Obviously,
ASTER data is more powerful for discriminating lithologies
than ETM data.
The comparison above indicates that if the same
number of bands (features) is used for lithologic information extraction, ASTER data bands can produce better
separability between the five rock types than the ETM data
Figure 5. Flowchart for the band selection process from
the 22 bands.
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May 2007
Figure 6. Band selection of ASTER and ETM bands for
lithologic discrimination: a1 to a14 – ASTER bands, e1 to
e8 – ETM bands. Bands e6 and e7 are low- and high-gain
calibrated ETM thermal bands. The Bhattacharyya
distance is maximum sum of average and minimum
Bhattacharyya distance between the classes.
in the southeastern Chocolate Mountains area. The results
from maximum likelihood classification, match-filtering
partial unmixing, and separability comparison support one
another.
Concluding Remarks
This study investigated the utility of the Landsat ETM, EOS
and EO-1 Hyperion data for lithologic mapping in
the southeastern Chocolate Mountains area. A comparative
study in the Rainbow Mine area shows that the significant
lithologic groups such as granitic rocks (gr), volcanic
rocks (Tv, Tvb), and the schist (mso) can be extracted well
from both EO-1 Hyperion data and ASTER data using
maximum likelihood classification. When the geologic
map is used as a reference, the overall classification
accuracy of Hyperion data is 86 percent and the Kappa
Coefficient is 0.81. The overall classification accuracy of
ASTER using nine bands (VNIR SWIR) is 76.5 percent and
the Kappa Coefficient is 0.68. If the ASTER thermal bands
are used in the classification, the overall accuracy and
Kappa Coefficient are clearly improved to 85.9 percent
and 0.81, respectively. Compared to hyperspectral sensors
(e.g., Hyperion), ASTER data has difficulty in discriminating some small groups with minor spectral difference. For
example, the gneiss (mc) and sedimentary breccia rocks
(Tbr) in the study area were identified with lower accuracy than that from Hyperion data. The average MF score
index also indicates that the significant rocks in the study
area can be better identified from the Hyperion data than
from the ASTER image.
Both the accuracy of the MLC classification and the
average MF score index of matched filter detection indicate
that the performance of ASTER and Hyperion data is much
better than Landsat ETM. The supervised classification of the
ETM data and the average MF score index indicates that a lot
of pixels in a rock type were misclassified in the ETM data.
The assessment of the separability also indicates that ASTER
bands are more powerful in the discrimination of the five
rock types in the study area. This is due to the fact that the
ASTER instrument provides more SWIR bands and thermal
bands, while Hyperion can provide 196 continuous VNIR and
SWIR bands. The ETM data cannot “see” the minor spectral
difference among the rock units.
ASTER,
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Although the classifications indicate that the Hyperion
data can produce better accuracy than ASTER, the lithologic
information extracted from ASTER image data is mostly
similar with that from Hyperion data. The classification
results of these two data sets have overall matching accuracy of 86.3 percent. In addition, the ASTER sensor has
much better spatial coverage than hyperspectral instruments, and the cost of ASTER data is near-free. Hyperspectral
instruments usually have a narrow swath (e.g., 7.6 km for
Hyperion), and very few areas of the Earth have been
imaged using Hyperion. Thus, ASTER data is suitable for
lithologic and mineralogical mapping in larger and more
diverse areas, especially where Hyperion data or other
airborne hyperspectral data are not available. The study we
have completed using ASTER radiance data (AST_09) to
extract lithologic information in the Paymaster district
(more than 400 square miles), south Chocolate Mountains
area (California), demonstrated the usefulness of ASTER data
for large-area lithologic mapping.
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(Received 29 April 2005; accepted 14 August 2005; revised
07 December 2005)
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