QUICK ATMOSPHERIC CORRECTION (QUAC) OF WORLDVIEW-3
MULTISPECTRAL IMAGERY – A COMPARISON TO HYPERSPECTRAL IMAGERY
RESULTS
Stephen B. Carr
Defence Science & Technology Group, Adelaide, Australia
Email: [email protected]
KEY WORDS: AVIRIS, Cuprite, SWIR, DigitalGlobe, Reflectance
ABSTRACT: The Quick Atmospheric Correction (QUAC) algorithm is applied to an Airborne Visible Infrared
Imaging Spectrometer (AVIRIS) hyperspectral image (obtained from the AVIRIS website courtesy
NASA/JPL-Caltech) and a simulated WorldView-3 (WV-3) image derived from it, and the results compared. The
reflectance spectra agree to within a few percent where WV-3 bands overlap with the hyperspectral bands. Both
QUAC results are compared with an AVIRIS reflectance image also obtained from the AVIRIS website courtesy
NASA/JPL-Caltech. The reflectance spectra are plotted in a number of charts and the results show qualitative
similarity in spectral shape with some clear differences in magnitude. QUAC parameters are altered in an attempt to
improve agreement and the results are discussed. Following this analysis QUAC is applied to an actual WV-3 image
of Cuprite, Nevada, USA and the reflectance compared with a reflectance image obtained from DigitalGlobe Inc.
(Longmont, Colorado, USA) using empirical line correction (ELC). The reflectance spectra agreement is within 10%
percent for each band. There are some subtle differences in the reflectance obtained in the shortwave infrared (SWIR)
bands, which could be important for identification of specific mineral features. Finally, a side by side comparison is
made of classification images using the unsupervised ISODATA algorithm in ENVI 5.2. Despite the differences in
the reflectance the images compare well visually. A comparison is also made with the classified atmospherically
uncorrected radiance image showing some noticeable difference implying atmospherically correcting to reflectance
does impact classification performance. Based on these results, QUAC can be successfully used on WV-3 imagery for
atmospheric correction but it is important to apply QUAC to additional 16-band WV-3 images to fully assess the
applicability of QUAC for different scenes.
1. INTRODUCTION
The WorldView 3 (WV-3) multispectral and short wave infrared (SWIR) sensors developed by DigitalGlobe Inc.
(Longmont, Colorado, USA) provide a unique capability from space for material identification to support scene
classification. Together they provide 16 spectral bands (8 in the Visible/near infrared (VNIR) and 8 SWIR bands)
with a relatively small ground sample distance (GSD) of 1.24 m and 3.7 m respectively. The WV-3 imagery currently
provided by DigitalGlobe (DG) is spatially resampled to 7.5 m in the SWIR. The capability to generate data of this
type was typically previously only provided by airborne hyperspectral imaging (HSI) sensors.
A critical step in processing of WV-3 imagery is to convert from “at sensor” or “top of the atmosphere” (TOA)
radiance to surface reflectance, as classification is often undertaken “at source”. The process is called atmospheric
correction (AC) and a number of algorithms exist to perform this task, ranging from first principles physics based
algorithms to various in scene based techniques. The QUick Atmospheric Correction (QUAC) algorithm (Bernstein et
al., 2012) is an example of an in-scene technique that was developed to provide a fast and relatively robust method of
undertaking AC for both hyperspectral and multispectral sensors. The latest version of QUAC is used in this analysis
and was obtained from Spectral Sciences Incorporated (SSI, Burlington, MA, USA).
A question of interest is how well does the WV-3 multispectral imaging (MSI) sensor compare with an airborne HSI
sensor where typically 100’s of contiguous bands of spectral information are collected at a relatively small GSD of the
order of meters. QUAC is applied to both an airborne HSI image collected by the Airborne Visible Infrared Imaging
Spectrometer (AVIRIS), (obtained from the AVIRIS website courtesy NASA/JPL-Caltech), and a simulated WV-3
image derived from the HSI image, and the results compared. These results are then compared to an AVIRIS
reflectance image also obtained from the AVIRIS website courtesy NASA/JPL-Caltech.
The simulated WV-3 imagery provides some insight into QUAC performance but it is derived from HSI imagery so
signal to noise will not be the same. WV-3 imagery of Cuprite, Nevada, USA is available (courtesy DigitalGlobe Inc)
and has been studied for the suitability of SWIR band performance for mineral mapping (Kruse et al., 2015). The
QUAC results are compared with a reflectance image obtained from DigitalGlobe where empirical line correction
(ELC) was used. Results comparing QUAC applied separately to the SWIR bands versus the full 16 bands are also
presented. In addition, a comparison is made between classified images derived from the QUAC and DG reflectance
images and the original atmospherically uncorrected radiance image, using the ISODATA unsupervised classification
algorithm in ENVI version 5.2 (Exelis Visual Information Solutions, Boulder Colorado).
2. QUICK ATMOSPHERIC CORRECTION (QUAC) OVERVIEW
A brief overview of the QUAC algorithm is provided (see Bernstein et al., 2012 for details). The basic physics behind
atmospheric correction is depicted in figure 1 a). The observed spectral radiance, Lobs, for a pixel with surface
reflectance, sur, is the sum of the three paths illustrated,
(1)
The components in (A+Cave) are grouped together because they tend to be approximately constant over an image,
and thus, can be considered as an offset common to all the image pixels. This simple linear relationship can be
re-arranged to express the retrieved surface reflectance in terms of the observed signal and derived atmospheric
parameters,
(2)
where the Gain=1/B, and the Offset=(A+Cave). For a physics-based approach, A, B, and C are retrieved by
comparison of certain spectral features to those predicted by radiative transfer calculations. For QUAC, these
parameters are determined directly from the in-scene spectral data and a key underlying assumption.
Figure 1 a) shows the three types of paths, A, B, and C, that solar photons can travel on their way to a remotely located
observer, where sur is the intrinsic reflectance of the observed surface pixel, ave denotes the spatially-averaged
reflectance of the surrounding pixels, and Lobs is the at-sensor radiance corresponding to the observed surface pixel. b)
shows the sensitivity of the “universal” scaling curve to differences in the subset of bands used for endmember
selection (i.e., AVIRIS vs. WorldView2), and on the number of endmembers used in the average. The peak value of
each curve has been normalized to 1.0.
The key QUAC assumption, which empirically holds for most scenes, is that the average of diverse endmember
reflectance spectra, excluding highly structured materials (i.e., vegetation, shallow water, mud), is always the same.
More specifically, every image is assumed to contain at least a handful (~10 or more) of spectrally diverse materials
whose average reflectance spectrum can be taken as a “universal” reference. The materials may include both natural
and manmade materials, such as dirt field, a water body, rocks, cars, roofs, roads, etc from the entire scene. It is
considered unusual if this material diversity condition is not met, but it can occur, for example, in some all-water or
all-desert scenes, etc. However, such imagery is typically of much less interest for remote sensing.
For QUAC, the Gain and Offset are given by,
Gain 
 end lib
, and
 ( Lobs  Cave )end 
(3)
Offset min( pixel value for each band),
(4)
where <end>lib is the average of the endmember spectra representing a reference library of material reflectance
spectra, and <(Lobs-Cave)end> is the average of a collection of endmembers retrieved from the observed, in-scene
pixel spectra. An endmember represents a unique spectrum from a collection of spectra. In most cases, linear
combinations of a small number (i.e., ~10-100) of endmember spectra can accurately represent a large number (i.e.,
>10,000) of spectra associated with a spectral library or image. In QUAC, we use the SMACC (Sequential Maximum
Angle Convex Cone) code to find endmembers (Gruninger et al., 2003).
Figure 2 Intermediate output plots corresponding to key QUAC processing steps for AVIRIS imagery. Simulated
WV-3 outputs are also shown in 2a), 2d) and 2e) (green crosses): (a) first the Offset/Baseline is found based on the
smallest channel values (i.e. the darkest values); (b) then ~20 endmembers are found for each of the 50 spatial chunks
the cube is divided up into, resulting in 1000 endmembers for the final endmember selection; (c) next the selection of
the final ~50 endmembers is made; (d) the Gain curve is determined based on the ratio of the data endmember average
to the library endmember average; (e) the Gain is normalized to strong vegetation in the scene, assuming 0.4
reflectance at ~850 nm ( if not enough strong vegetation then a normalization of 0.4 at ~1000 nm is assumed), and; (f)
the Offset and Gain are applied to the endmembers and full data cube yielding the corrected endmembers shown and
the final reflectance image.
The "universal" scaling curve has a slight dependence on the number and location of the spectral channels used in the
selection of endmembers from the reference material library, as shown in figure 1 b). An illustration of the key QUAC
processing steps, based on the Oakville North scene, are shown and described in figure 2. In order to obtain a good
reflectance spectrum using QUAC it is important to filter out potential endmembers with "too much" spectral
structure. A commonly occurring example of such a spectrum is that for vegetation (Figure 2 e)). If vegetation is not
filtered out, then the resulting Gain curve and reflectance spectra would be contaminated with an undesirable, strong
red edge feature around 750 nm and they would be highly spectrally distorted because of the large differences
between the vegetation visible (VIS) and near infrared (NIR)/Shortwave Infrared spectral reflectance. It is
straightforward to filter out such spectra based on a normalized difference spectral index (NDSI) of the form,
(5)
where the spectral channels,
and
, are selected to be on either side of the spectral edge/discontinuity. In
addition to a normalized difference vegetation index (NDVI), QUAC allows for the user to specify two additional
NDSI's. A structured spectrum is filtered out by specifying an NDSI threshold, in which spectra exhibiting an index
above the threshold are discarded. While the threshold depends somewhat on the spectra to be filtered out and the
available sensor channels, it is typically in the range of ~0.2-0.3.
2.1 Extending QUAC to WorldView-3 Imagery
QUAC has recently been extended and applied to an HSI image of a coastal scene, where it was compared with
reflectance obtained with first principles physics based atmospheric correction algorithms (Carr et al., 2015). This
work extends QUAC to WV-3 imagery. The first step was to add an option for the WV-3 sensor to QUAC. The four
bands chosen for end member selection were NIR2 (950 nm), SWIR-1 (1210 nm), SWIR-3 (1660 nm) and SWIR-5
(2150 nm). The vegetation filter band pair was selected to be the same as WorldView 2 (660 nm, 832.5 nm). A
number of key QUAC outputs for WV-3 are shown in figures 2 a), d) and e). After some initial processing of Cuprite
imagery some further modification of the QUAC settings was undertaken for the Cuprite scene. This was focused on
the SWIR bands due primarily to the lack of strong features in the VNIR bands (no vegetation, no water bodies). The
four bands chosen for end member selection were altered to SWIR-1 (1210 nm), SWIR-2 (1570 nm), SWIR-4 (1730
nm) and SWIR-6 (2205 nm). The vegetation filter band pair was modified to (1584 nm, 1285 nm) based on features in
the SWIR and hence technically is no longer a vegetation filter. The ‘mud filter’ bands were also altered to use bands
in the SWIR part of the spectrum, due to the absence of soil and water (i.e. mud). In addition the solar blackbody
temperature (Tsol) was set to 5000 Kelvin which gave the best reflectance in the VNIR bands when compared with
the DG reflectance (Figure 8).
A qualitative indication that QUAC will generate a good gain curve and hence accurate atmospheric correction is the
appearance of endmembers that are uniformly distributed with wavelength and reflectance (Figure 3). Further
refinement can be undertaken such as attempting to modify the filter band pairs to further reduce any structured
endmembers (e.g. the orange curve in figure 3 a)) that could affect the gain calculation.
Figure 3 QUAC corrected endmembers for the two WV-3 images processed. In both cases there are a relatively
uniformly distributed number of end members both spectrally and in reflectance.
3. A QUAC REFLECTANCE COMPARISON OF SIMULATED WORLDVIEW-3 IMAGERY WITH
AVIRIS HYPERSPECTRAL IMAGERY
Samples of WV-3 imagery are available at no cost but the majority is only of 8 VNIR bands, with only a few collects
including the 8 SWIR bands, such as Cuprite (Section 4). To assess QUAC suitability for WV-3, a simulated WV-3
image cube was derived from an AVIRIS HSI image. The criteria used to select an AVIRIS HSI image were: there
should be an available reflectance product associated with the radiance product, a GSD of ideally 1.2 m (the GSD of
WV-3 VNIR bands) or less, or failing that, 3.7 m (the GSD of WV-3 SWIR bands) or less, and that the scene should
be a relatively uncomplicated urban, rural or semi-rural location for this initial analysis. The need to have a
reflectance product was deemed important as this provides for an independent means of evaluating the QUAC results.
An image with a GSD of 3.7 m was available that met these criteria. The scene is predominantly rural farm land in
Oakville North (Figure 4) California, USA. The description accompanying this image indicates the conditions were
clear with a small amount of cirrus cloud. The AVIRIS reflectance image had a 0 to 10000 scale and was rescaled to
0 to 1 before comparing with the QUAC results. The AVIRIS radiance and reflectance products were freely available
from the AVIRIS website. To generate the simulated WV-3 image of Oakville North the spectral resampling function
in ENVI 5.2 was used along with the WV-3 band responses.
The AVIRIS image was used without undertaking any further processing to remove noisy bands or sensor artefacts
that may be present in the image. This partly demonstrated the capability of QUAC to work with less than optimal
sensor data and also enabled a comparison with the AVIRIS reflectance image.
3.1 QUAC Results and Analysis
Before processing with QUAC, external border pixels were removed that were present following othrorectification.
This was done by cropping out a region of the full scene as shown in figure 4. The default AVIRIS setting in QUAC
was selected and applied to the AVIRS HSI image. QUAC was then modified to add an option for the WV-3 sensor.
The starting point was to take WV-3 bands as close to the chosen AVIRIS bands (Section 2.1). The main objective of
this analysis was to ascertain whether the 16 bands of the WV-3 image enabled QUAC to determine a spectral
atmospheric gain and offset coefficient comparable (or the same) as that obtained from the AVIRIS HSI image (where
the bands overlap). Comparing the reflectance is the most straightforward way of assessing this. The results presented
here are for a GSD of 3.7 m and 7.5 m enabling an assessment of the impact of GSD on QUAC performance. It was
decided not to resample to the VNIR GSD of 1.2 m for the current analysis as this will only introduce additional
spatial content through pixel replication.
Figure 4 AVIRIS RGB image showing the location of the regions of interest (ROIs) (blue = water body, green = green
field, brown = brown field, and the solid black inside the black rectangle = road). Courtesy NASA/JPL-Caltech.
To compare reflectance, separate ROIs were created, each encompassing 10’s of pixels, for different regions of the
scene including: brown field, green field, a water body and road (Figure 4). Focusing on the red curves and green
crosses in figure 5 it is apparent that the agreement between the QUAC corrected AVIRIS and WV-3 reflectance for
these regions, where the bands overlap, is within a few percent. Small differences are present in the NIR and some
SWIR bands but are not significant in percentage terms.
Figure 5 Reflectance of regions of the Oakville North scene comparing the reflectance of the AVIRIS reflectance
image (AVIRIS) and QUAC applied to the AVIRIS HSI image (QUAC AVIRIS) and simulated Worldview-3 image
(QUAC WV-3). Note the different scales on the vertical axis.
Visually comparing the AVIRIS reflectance (AVIRIS) and the QUAC reflectance the shape of the spectral reflectance
is qualitatively similar overall but with some noticeable differences particularly in the SWIR end (red and black
curves of figure 5). There is an obvious magnitude difference, in particular for the VNIR region where the QUAC
reflectance is consistently higher than the AVIRIS result. The difference in reflectance in the VNIR band can be as
high as ~ 25%. There are some clear atmospheric residuals present in the AVIRS reflectance result. Most of these
correspond to the three water bands around 820, 940 and 1130 nm and the carbon dioxide band around 2000 nm.
Assuming the AVIRIS reflectance cube is the truth (an assumption for this analysis) further analysis of the QUAC
results is warranted to attempt to account for the observed difference in the magnitude of the reflectance.
In addition to the reflectance comparison some intermediate outputs of QUAC are displayed in figure 2. The QUAC
gain and offsets are shown for both the AVIRS HSI image and the simulated WV-3 image (Figures 2 a) and d)). The
offsets agree as expected, due to the fact that the same sources of dark pixels are found. The agreement between the
gains is within a few percent leading to the good agreement in the reflectance (Figure 5). Further refinement of the
settings in QUAC for WV-3 could be made in an attempt to get closer agreement with the AVIRIS HSI image gains.
Focus was initially on refining the QUAC results for the AVIRIS HSI image.
3.1.1 Adjusting QUAC Parameters Parameters that can affect the endmember selection are Tsol and the median
threshold parameter (medthrsh) used to filter out bright or saturated endmembers using a multiple of the median as the
cut off per channel. Tsol is typically set around 4500 Kelvin rather than the actual solar blackbody temperature as this
tends to give better results. It is used to calculate the solar spectral radiance using Planks equation. The purpose of
dividing the spectral radiance by the solar spectral radiance is to provide for a better scale for endmember filtering
(apparent reflectance) (Bernstein et al., 2012). Varying Tsol between 4000 and 5500 Kelvin had only a small effect on
the reflectance, primarily in the VNIR bands. Medthrsh was adjusted from 2.25 to 3.0 and the corrected endmembers
were noticeably different. However, for all but the brightest pixels in the scene (buildings), the reflectance was almost
identical. In the case of the brightest pixels the reflectance was reduced by around 10% from the original value in the
visible bands.
As discussed in detail in section 4.2 terrain relief can have an impact on QUAC performance. To assess this, part of
the image that contains the terrain relief was removed (the region to the right of the red dashed line in figure 4) and the
image reprocessed using QUAC. The results are given in figure 6 a) showing closer agreement with the AVIRIS
reflectance in the VNIR bands.
3.1.2 QUAC GSD Comparison Worldview-3 collects SWIR imagery with a GSD of 3.7 m. This is currently
degraded by DigitalGlobe to 7.5 m before being publically released. The analysis to this point has focused on the
combined VNIR and SWIR bands with a GSD of 3.7m. To test the impact that the larger 7.5 m GSD may have on
QUAC performance, the WV-3 image is spatially resampled to a GSD of 7.5 meters in ENVI 5.2 before applying
QUAC. For the sake of brevity only the green field result is presented and compared with the 3.7 m GSD result in
figure 6 b). There is a similar level of agreement for the other regions.
Figure 6 a) shows the impact of scene content on QUAC with the purple curve showing closer agreement in
magnitude with the AVIRIS result in the VNIR bands. b) shows a QUAC reflectance comparison for WV-3 imagery
with a GSD = 3.7 m (QUAC WV-3) and WV-3 imagery resampled to a GSD = 7.5 m (QUAC WV-3 GSD=7.5 m).
Agreement is to within a few percent showing no significant impact of the two different GSDs for regions of the scene
with sufficient spatial uniformity. It is unclear why the largest difference (still small overall) occurs for the SWIR
bands 2, 3 and 4. Further comparison of the impact of GSD on QUAC WV-3 performance should be undertaken when
more 16-band WV-3 imagery becomes available.
3.1.3 Summary These results show that WV-3 bands provide a QUAC reflectance that agrees within a few percent of
the AVIRIS HSI (Figure 5) and both QUAC results appear qualitatively similar in shape but with up to an ~25%
difference in the magnitude of the reflectance compared to the AVIRIS reflectance (Figure 5), for some selected
regions in the scene. Further assessment of these results should focus on classification which is often the end result of
MSI/HSI processing with atmospheric correction being one step in the processing chain. Despite differences in the
reflectance, if classification can be undertaken and the results agree to within accuracy requirements (i.e. if the results
are fit for purpose), then these differences turn out not to be operationally significant. It may also be worth comparing
the apparent reflectance to see if this provides for a level of classification similar to the atmospherically corrected
results.
4. ASSESSING THE PERFORMANCE OF QUAC WHEN APPLIED TO A WORLDVIEW-3 IMAGE OF
CUPRITE NEVADA
WV-3 imagery of Cuprite, NV was obtained from DigitalGlobe. This included raw 8 band multispectral imagery at
1.2 m GSD, 8 band SWIR imagery at 7.5 m GSD (resampled from 3.7 m GSD imagery) and 16-band imagery
resampled to 1.6 m GSD using nearest neighbor sampling and then atmospherically compensated using empirical line
correction. The raw image cubes were converted to radiance using the ENVI radiometric correction tool (with
metadata contained in the auxiliary files provided). In addition a 16 band radiance cube with a 7.5 m GSD was
generated using the layerstack function in ENVI. To reduce processing time a smaller region was chipped out from
these cubes covering a region approximately 10 km x 10 km (Figure 7). Because QUAC depends to some extent on
scene content, a comparison was made between the chip and the full image results with no significant differences
being observed (results not shown).
4.1 Cuprite, NV
The Cuprite scene is devoid of vegetation, composed mainly of different mineralogical formations. As a result the
focus of the analysis was on the SWIR bands, where the majority of spectral differences are present for this scene. The
scene is particularly stressing for QUAC due to the fact there is not a wide range of spectra in the scene. The other
main challenge is the significant amount of terrain relief (Figure 7). The latter implies that on average the QUAC gain
may be accurately compensating for the illumination conditions and atmospheric paths to the sensor, but that for any
specific pixel or region there may be significant error in the gain due to the varying terrain height, slope and
orientation with respect to the sun (Lach, et al. 2008). This will be true for other algorithms such as ELC that use
spectra from specific features in the scene to derive gain and offset coefficients. The gain and offset will be exact for
similar height terrain with similar look angles to the sensor (and similar atmospheric path lengths) in the scene to
those associated with the bright and dark regions used to derive the gain and offset coefficients. The errors can be
significant for other parts of the scene with considerably different terrain relief. The other challenge of the Cuprite
scene is the apparent lack of dark pixels which are required by QUAC to derive an offset i.e. an estimate of path
radiance (atmosphere scattered radiance contributing to the pixel). This is also likely to affect the ELC, as it is
preferable to have the two points spread further apart when fitting the regression line in order to estimate a gain and
offset.
Figure 7 RGB image of the WV-3 chip (~ 10 km x 10 km) that was processed with QUAC. The small coloured
rectangles are the ROIs created in ENVI and used to compare between QUAC and DG reflectances. Blue – region 1,
green – region 2, magenta – region 3 and red– region 4.
Despite these limitations and the challenges associated with atmospherically correcting the Cuprite scene, QUAC still
produces results that appear satisfactory in terms of differentiating between different parts of the scene (Figure 8). An
unsupervised classification analysis is undertaken and a visual comparison is made between the classified QUAC
reflectance and DG reflectance images (Figure 9).
4.2 QUAC Results
As an independent verification of the QUAC reflectance results, a comparison is made to the DG reflectance obtained
using the empirical line correction (Figure 8). To obtain the ELC result, DG used an AVIRIS HSI image
atmospherically corrected with the FLAASH algorithm in ENVI and selected the larger basalt outcrop and the playa
materials as the light and dark target spectra respectively. To undertake this comparison the DG reflectance image
was resampled using nearest neighbor interpolation to a 7.5 m GSD using ENVI. Regions were chosen that
approximately corresponded to specific mineral formations: region 1 ~ Alunite, region 2 ~ Calcite, region 3 ~
Kaolinite and region 4 ~ Muscovite based on analysis from (Kruse et al., 2015). Small ROIs were created (Figure 7)
and mean spectra calculated. The agreement between the QUAC and DG reflectance are within 10% percent for each
of these regions (Figure 8). Examining closely the important SWIR bands there are some differences which may be
important for subtle mineral identification. In particular the relative difference in the SWIR6 and SWIR7 bands alters
the ‘shape’ of the reflectance curve over this part of the spectrum when compared with the DG result.
Figure 8 Reflectance comparison for WV-3 imagery with a GSD = 7.5 m for QUAC and DG reflectance imagery.
However in terms of identifying the main classes in the scene the QUAC and DG reflectance provide similar levels of
performance as indicated by the side by side comparison of classified images in figure 9. These classification images
were obtained using the unsupervised ISODATA algorithm in ENVI 5.2 for 5 classes, 10 iterations and a 2%
threshold. Shown for comparison is the classified atmospherically uncorrected radiance image which has some
noticeable differences compared with the classified reflectance images (Figure 9). Additional classification work is
encouraged using supervised classification techniques such as support vector machine. It is also important to compare
against any ground measurements obtained from Cuprite (particularly of the important mineralogy).
Figure 9 Classified image comparisons using the unsupervised ISODATA algorithm in ENVI 5.2 applied to the
QUAC and DG reflectance images and the original atmospherically uncorrected radiance image.
A question of interest for the Cuprite scene, which is dominated by materials with features in the SWIR bands, is does
QUAC perform better when the VNIR and SWIR cubes are processed separately? In the case of the SWIR bands, the
results are very similar (Figure 10), providing no advantage in just processing the SWIR bands. The caveat is that
sensible choices are made for key parameters in QUAC such as Tsol, the bands for endmember selection and the band
pairs and thresholds for the various filters used to remove less desirable endmembers from the calculation of the gain.
Figure 10 A comparison between QUAC reflectance of WV-3 imagery calculated using the full 16-bands (QUAC)
and just the 8 SWIR bands (QUAC SWIR).
QUAC and ELC are both likely to be impacted by terrain relief. To assess the overall accuracy of QUAC a sensible
next step would be to compare the results with ground measurements from Cuprite or, failing that, atmospherically
corrected reflectance obtained from a HSI sensor such as AVIRIS. Preferably applying first principles physics based
atmospheric correction algorithm ideally using inputs derived from atmospheric measurements (or a cloud, aerosol,
vapour (water), ice and snow (CAVIS) bands sensor).
5. DISCUSSION AND CONCLUSIONS
QUAC was successfully applied to AVIRIS HSI imagery, simulated WV-3 imagery and actual WV-3 imagery. The
agreement between the AVIRIS HSI and simulated WV-3 reflectance was within a few percent in regions where the
bands overlap (Figure 5). When both were compared with an AVIRS reflectance image the shapes of the spectral
reflectance curves were qualitatively similar but there was an obvious difference in magnitude. A number of key
QUAC parameters we adjusted and the impact on the reflectance assessed. None of these adjustments altered the
reflectance significantly. The next step would be to undertake atmospheric correction of WV-3 imagery collected
simultaneously (or close to the prevailing conditions) with HSI imagery and ground measurements.
The reflectance obtained with QUAC for WV-3 imagery of Cuprite, NV agreed to within approximately 10% for each
band with the reflectance obtained by DigitalGobe using ELC. There were some differences in the SWIR bands that
might be significant for mineral identification. However, on comparing unsupervised classification images derived
from both reflectance results the differentiation between classes appears visually similar in each image (Figure 9).
Both classified reflectance images show noticeable differences with the classified atmospherically uncorrected
radiance image. This indicates that atmospheric correction, by altering the results of classification for this simple
unsupervised algorithm, is a factor in the exploitation of WV-3 imagery and needs to be assessed for its importance.
There was no advantage in processing the VNIR and SWIR bands separately with QUAC as far as the SWIR band
reflectance was concerned. On comparing processing the full 16-bands versus just the 8 SWIR bands the reflectance
was within a few percent in the SWIR bands (Figure 10). This is not to say that further refinement could not be
undertaken that might improve QUAC for both the full 16 bands and just the 8 SWIR bands.
When QUAC is applied to a new environment or a new sensor it should be assessed against ground measurements or
reflectance obtained independently via other means if possible. Once the key parameters are configured in QUAC, it
will typically perform adequately producing reflectance that enables classification to be undertaken. Now that QUAC
has been demonstrated to be applicable to WV-3 imagery it is important to assess QUAC against more WV-3 imagery
over the full 16 bands. Additional classification analysis using a number of supervised methods (such as maximum
likelihood or support vector machine) as the next step in processing WV-3 reflectance data is encouraged. By doing
so it will be possible to understand how effective the 16 bands of WV-3, with a relatively small GSD, are at
undertaking tasks primarily done (up to now) using airborne HSI sensors.
When the 12 CAVIS bands of WV-3 become available this will provide a significant amount of atmospheric and
surface measurements in carefully selected bands to support removal of atmospheric features affecting WV-3
imagery. Reflectance images obtained utilising data from the CAVIS bands should provide a good source of data
against which to compare QUAC results. It may be that for many applications the reflectance product provided by DG
will be sufficient and the need for the user to undertake atmospheric correction reduced or eliminated.
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