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International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B7, 2012
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia
first-principle method. Its model principle process is shown in
Figure l(c: ITT Visual Information Solutions (ITT VIS),
*ENVI User's Guide, Version 4.8").
HIS or —> Bright
MSI data spectral
filter
Y
: Y
Determina
tion of Average of
baseline end member
(Dark
channel/ Y
offset) Determin Average
Y ation of |g of
gain reference
Baseline end
deduction member
Y
A ; Recovery of reflection
Vegetation data by using gain and
filter offset
Determination
of end member
Figure 1. Flow chart of QUAC model
QUAC also needs the elevation angle of the sun and center
wavelength. If the sensor has no correct radiation or
wavelength calibration, or the sun light intensity (when there is
cloud deck) is unknown, correction can still be made with this
method within the allowed accuracy scope.
3.3 Analysis of effect of atmospheric correction
In order to evaluate and verify the effects of atmospheric
correction which Model FLAASH and Model QUAC have on
SPOT-5 remote sensing image, a contrastive analysis shall be
made respectively for the images of both models after and
before the correction in the aspect of sight and spectral curve of
surface features reflectance. The images (geometric correction
has been finished) before and after the atmospheric correction
need geographical link first to ensure that the images of various
scenes correspond to the same pixel in the same area when
making the contrastive analysis.
3.3.1 Analysis of visual contrast before and after correction:
In Figure 2, a, b, c is respectively the image in the same region
after twice magnification before the atmospheric correction,
after the FLAASH atmospheric correction and the QUAC
atmospheric correction, and band RGB combination is 4, 3, 2.
We can see that there are obvious changes in visual effects of
the image before and after the correction, darker for a on the
whole, because the presence of the atmosphere will reduce the
difference between light and dark of surface features to reduce
contrast ratio of the images; visual effect of image after the
correction has been improved significantly, brighter and clearer,
contrast ratio also increased and image quality improved,
indicating that atmospheric correction has effectively
eliminated the effect of atmospheric aerosols, water vapor and
other atmospheric factors. In contrast, image quality of b
(through FLAASH atmospheric correction) is slightly better
than c (through QUAC atmospheric correction) due to more
abundant information.
a: b e
Figure 2. Visual analysis of atmospheric correction
3.3.2 Contrastive analysis of reflectance spectral curve:
Characteristics of reflectance spectrum curve are an important
means for the recognition of remote sensing image surface
features. From the visual angle, only a rough evaluation can be
made on the effects of the two atmospheric correction models,
and making a contrast analysis of reflectance spectral curve of
its corresponding typical surface features can reflect the effect
of atmospheric correction better.
The study area of this trial is typically hilly area in southern
China; data acquisition time is in November; the image
presented mainly small ponds, the harvested farmlands and
several scattered hills; and most of the surface is soil. Therefore,
soil, vegetation, water body (pond water) and asphalt road are
selected respectively as the surface type of pixel. As shown in
Figure 3, a, b, c, d are respectively the figures of soil,
vegetation, water bodies and asphalt road, four typical surface
features, after FLAASH atmospheric correction, after QUAC
atmospheric correction and of actual measurement reflectance
curve.
0.32 T
—+— QUAC :
03¢ FLA ASH ES ey t tt n -
Measured :
0.281 r$ Reflectance SE nase
5 Bo ee ; ae ; RANE ei ]
$ 024| AL AE AE p^ us En Bees
Gt À en ES : mae Lo]
05 1 15 2
Wavelength/um
a. Soil
