There are two basic methods for testing the quality of an algorithm to minimize atmospheric effects on LANDSAT imagery: (1) test the results a posteriori, using ground truth or control points; (2) use a method based on image data plus estimation of additional ground and/or atmospheric parameters. A procedure based on the second method is described. In order to select the parameters, initially the image contrast is examined for a series of parameter combinations. The contrast improves for better corrections. In addition the correlation coefficient between two subimages, taken at different times, of the same scene is used for parameter's selection. The regions to be correlated should not have changed considerably in time. A few examples using this proposed procedure are presented

Dias, L. A. V.

Neto, G. C.

Vijaykumar, N. L.

English

NASA Technical Reports Server

SECRETARIA DE PLANEJAMENTO DA PRESIDENCY DA REPUBLICA
CONSELHO NACIONAL OE DESENVOLVIMENTO CIENTIFICO E TECNOLOGICO
<E83- 10218) & PROCEDURE FOE TESTING THE
QUALITY OF LANDSAT ATMOSPHERIC CORRECTION
ALGORITHMS (Institute de Pesguxsas
RECEIVED BY
NASA ST1 FACILITY
DATEi
DCAF NO./
i /PROCESGLD BY
gf NASA STI J-KC
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https://ntrs.nasa.gov/search.jsp?R=19830013188 2020-03-21T03:38:00+00:00Z
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1. Publication N9
INPE-2563-PRE/213
2. Version 3. Date
Nov., 1982
4. Origin
DIN/DPD/DTI
Program
INFORMATICA
5. Distribution
Q Internal 03 External
D Restricted
6. Key words - selected by the author(s)
ATMOSPHERIC CORRECTION
IMAGE PROCESSING
7. U.D.C.: 621.376.5:551.507.362.2
8. Title INPE-2563-PRE/213
A PROCEDURE FOR TESTING THE QUALITY OF LANDSAT
ATMOSPHERIC CORRECTION ALGORITHMS
9. Authorship Luiz Alberto Vieira Lias
Nandamudi L. Vijaykumar
Gilberto Camara Neto
Responsible author - (A'/--- J?--
10. N9 of pages: 09
11. Last page: 08
Jose Antonio G. Pereira
13. Authorized by
Nelson de Jesus Parada
Director
14. Abstract/Notes
There are two basic methods for testing the quality of an
algorithm to minimize atmospheric effects on LANDSAT imagery: a)test
the results a posteriori, using ground truth or control points, b)use
a method based on image data plus estimation of additional ground
and/or atmospheric parameters. This paper suggests a procedure based
on the second method. In order to select the parameters, initially
the image contrast is examined for a series of parameter combinations.
The contrast improves for better corrections. In addition the
correlation coefficient between two subimages, taken at different
times, of the same scene is used for parameters' selection. The regions
to be correlated should not have changed considerably in time. A few
examples using this proposed procedure are presented.
15. Remarks
This paper will be presented at the Remote Sensing Symposium
to be held at the University of Liverpool, Liverpool, England.
ORIGINAL PAGE IS
A PROCEDURE FOR TESTING THE QUALITY OF OF POOR QUALITY
LANDSAT ATMOSPHERIC CORRECTION ALGORITHMS
Luiz Alberto Vieira Dias
Nandamudi Lankalapalli Vijaykumar
Gilberto Camara-Neto
Institute de Pesquisas Espaciais - INPE
Conselho Nacional de Desenvolvimento Cientifico e Tecnologico - CNPq
P.O.Box 515
12200 - Sao Jose dos Campos
Sao Paulo, Brazil
Abstract: There are two basic methods for testing
the quality of an algorithm to minimize atmospheric
effects on LANDSAT imagery: a) test the results a
posteriori, using ground truth or control points,
b) use a method based on image data plus estimation
of additional ground and/or atmospheric parameters.
This paper suggests a procedure based on the second
method. In order to select the parameters, initially
the image contrast is examined for a series of
parameter combinations. The contrast improves for
better corrections. In addition the correlation
coefficient between two subimages, taken at different
times, of the same scene is used for paramters'
selection. The regions to be correlated should not
have changed considerably in time. A few examples
using this proposed procedure are presented.
1. INTRODUCTION
The presence of. the terrestrial atmospheric presents a problem to
classify satellite images, because it tends to blur the original scene. In
order to minimize this effect, it is possible to simulate the atmospheric
influence on the satellite image, by means of'a computer program, and
subtract it from the original digitized scene.
Turner and Spencer (1972), O'Neil et al. (1978) have developed some
methods to minimize the effect of the atmosphere. Due to the need of
having such a system at INPE, a model based on O'Neil's . method was
selected. This method underwent extensive modifications in order to better
suit our atmospheric conditions, and to become faster and interactive,
besides producing graphic outputs (Dias et al., 1981; Vijaykumar and Dias,
1982).
It is important to test quantitatively the quality of the atmospheric
ORIGINAL PAGE IS
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correction models, in order to improve the image classification. This
paper suggests a procedure to select a better correction in specific
conditions.
2. SUGGESTED METHOD
Due to the high cost and associated delays in obtainning ground truth
and/or control points the suggested method uses, as inputs, only image
data plus some estimated parameters. The estimated parameters are known
to belong to well accepted intervals. They can be changed on these
intervals. As output, the image contrast and correlation coefficient of
two time spaced images are analysed.
INPE's correction algorithm is composed at two parts. Initially,
based on image data an atmospheric transmittance program is used to
calculate the total transmittance (aerosol and molecular scattering). It
uses the LOWTRAN 4 packets. These packets are well known (Selby et al.,
1978), but were modified in order to become faster interactive,
user-friendly and with optional graphic output. The input parameters are:
a) atmospheric model (standard US, midlatitude summer, midlatitude minter,
tropical, user-defided, b) visibility (0 to 23 km), c) altitude at ground
level, d) initial frquency (for LANDSAT channels), e) final frequency (for
LANDSAT channels), and f) channel width. Optionally, radiosonde data can be
entered as a new atmospheric model.
All parameters one obtained without ground truth except the visibility
and the model that may be estimated based on image data, and latitude (for '
example, midlatitude summer model, visibility = 15 km). An alternative is
the use of radiosonde data to determine the atmospheric model (a very
useful and recommended procedure when data is available). Unfortunately
this type of data is not always available, for Brazilian images.
The second part is the procedure itself, it uses a program on the GE
Iraage-100 (Multispectral Image Analyzer attached to a PDP-11/45) to treat
the digitized image directly. This interactive program called ATMCOR is
very fast (about 10 seconds for the controle image of 512 x 512 pixels),
and even faster for a correction on a cursor in a small subimage. The
inputs are: a) Landsat channel, b) ground albedo, c) solar zenith angle,
d) aerosol optical depth, and e) transmittance. ATMCOR calculates the
radiance path based on O'Neil et al. (1978) tables with a spline
interpolation for zenith angles, and aerosol optical depths.
The input parameters that have some uncertainties are the albedo and
the aerosol optical depth. The proposed procedure is the following: a)
get an image from a desired scene; b) correct it with estimated aerosol
optical depth and albedo; c) check the image contrast for selected points;
d) repeat the procedure with new input data; e) check the new image
contrast for the same selected points comparing it with the result on item
c; f) repe'at c to e until the contrast has reached a maximum. The idea is
that a sharp contrast is obtained when the atmospheric effect is minimized,
for the atmosphere tends to blur an image, thus reducing its contrast.
Care should be taken, for a sharp contrast alone is not by itself
sufficient. A second criterion is then employed to decide which correction
is best suited for the prevailing conditions. Using two images of the same
scene, separated on time, the correlation coefficient between two
ORIGINAL PAGE IS
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selected subimages are obtained, A good correction (near 1) between two
different atmospheric corrected subimages, that did not change in time,
suggests that the atmospheric effect is minimum and the correction adequate.
On the other hand, a poor correlation (near zero) indicates a strong
atmospheric interference (correction model not suitable) or a poorly
chosen subimage. Of course, the subimages to be correlated must be chosen
such that no change with time have occurred in them. For example, a crop
on different stages is not acceptable, but an airport runway is an
excelletn choice.
3. EXPERIMENTAL RESULTS
In order to test the suggested model it was chosen a Paraiba Valley
scene (orbit 150, point 28) of the Landsat satellite. Two images widely
Spaced on time were selected; July, 11tn, 1973 and January^ 31st> 1978,
with a cloud cover at less than 10%. Table 1 indicates the image data.
The transmittance for both images was calculated by LOWTRAN 4, using
aidlatitude/winter for * 1, and midlatitude/summer for * 2. The visibility
was estimated at 15 km. Table 2 shows the computed transmittances.
Initially the contrast is studied. It is defined for this work
purposes as
Ce [It - Im|
TD
where It is the maximum value in the target pixel area, and I , the maximum
pixel value in the neighbourhood. The image * 2 was selected, and the
chosen points were: a) Airport at Sao Jose dos Campos, A; b) Forest area
in Sao Jose dos Campos County, F; c) City of Taubate, T; and d) water
reservoir in Jacarei County, J. The scale used was 1:250,000 for contrast
studies. In this case, channels 4, 5 and 7 were used, and the albedo was
estimated at 0.2. The target area was 4x4 pixels. The neighbourhood
was 32 x 32 pixels around the target area.
On Table 3 the contrasts for the corrected images in the four target
areas in the three selected channels are shown, as well as the contrasts in
the original images.
It should be noted that the high contrast on the original images is
due to the fact that the atmospheric correction tends to increase all pixel
values, thus giving an apparent high contrast to the original scene. For
comparisson only the three corrected images (0.12, 0.24 and 0.36 aerosol
optical depth) should be considered. At the areas A (Airport), and
T (Taubate) the aerosol should be 0.24. On the forest area, F, it should
be 0.36, and at the Jacarei water reservoir, J, it is undecided, since for
each channel there is a different result. The results are the expected
ones, since it was reasonable to have a higher aerosol optical depth due to
a large aerosol concentration on the atmosphere over a forest at this time
of the year than over a city (T) or over an airport (A).
As far as the correlation coefficients were considered, the channels
5 and 7 using target areas of 16 x 16 pixels were used. The four target
areas are shown on Table 4.
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The images for 1973 and 1978 were registered nanually before the tests.
Making all possible combinations, it was noted that area * 1 (Oil
refinery) was poorly correlated on channels 5 and 7 for all at them. This
result was expected, and this area was included for testing the method.
The subimages should be poorly correlated because in 1973 the oil refinery
area was a pasture, with complets different spectral characteristics. The
highest correlation coefficient was obtained for aerosol optical depth, Ajj,
equal to 0.12 on both images.
Area * 2 (city at Jacaref) presented the highest correlation
coefficient, for channels 5 and 7, at 0.315 and 0.454 respectively. This
corresponds on channel 5 to A^ = 0.24 on both images. This area is not
very suitable for this kind of test, because in five years the area of
16 x 16 pixels chosen changed a lot.
Another area that should present some problems is area * 4 (forest
area in Sao Jose dos Campos County). The two subimages are five years
apart and one is for winter and the other is for summer. It was expected
that the correlation coefficient would be smaller than for area * 2, but
higher than for area * 3, .and indeed it occurred . The actual
results were, for channels 5 and 7, respectively, 0.123, and 0.172, the
A^'s for channel 5 were 0.12 x 0.12, while for channel 7 were, 0.36 x 0.36.
The airport (area * 3) was expected to yield the larger coefficient of
correlation. Indeed, the results are the following: Channel 5, A^'s
0.24 x 0.24, correlation coefficient = 0.946; Channel 7, Ad's 0.12 x 0.12 -
and 0.12 x 0.36, correlation coefficient = 0.534.
4. CONCLUSIONS
Summing up the data above, it is possible to suggest that according to
contrast studies and correlation coefficients, the best results for the
AjS are the ones shown on Table 5, below.
From Table 5 it is seen that the best correction for the airport area
should be with Ad e 0.24 and for the forest area A^ = 0.36. It should be
pointed out that the. correlation results for the forest area should be
taken with reserve, due to the fact that the two subimages are five years
apart and are for different seasons.
The inclusion of areas like the oil refinery, water reservoir, Taubate
city and Jacaref city had the purpose of checking the behevior of the
suggested procedure.
The results and conclusions here presented are far from complete, and
much more study and effort should be employed in the future, the aim of this
work being to show that it is possible, in principle, to test quantitatively
the atmospheric correction models based only on image data, and the
estimation of parameters within a well defined range. Of course, it was
possible to have other parameters changed, such as the albedo and even the
transmittance. This has not been done yet, but is planned for the near
future.
ORIGINAL PAGE IS
5. ACKNOWLEDGEMENTS OF POOR QUAUTV
The authors would like to thank the Institute de Pesquisas Espaciais -
INPE/Conselho National de Desenvolvimento Cientifico e Tecnologico - CNPq,
especially our Director Dr. Nelson de Jesus Parada, whose support was
decisive.
«
6. REFERENCES
DIAS, L.A.V., PEREIRA, A.E.G., CAMARA-NETO, G. , BENTANCOURT, J.J.V. (1981):
Correction of Atmospheric Effects in Satellite Images. Presented at IV
National Congress of Computer and Applied Mathematics, Federal University
of Rio de Janeiro, Brazil.
O'NEIL, N., MILLER, J.R., AHERN, F.J. (1978): Radiative transfer
calculation for remote sensing applications. In: Canadian Syposium on
Remote Sensing, S. Victoria, Canada, 1978. Proceedings Ottawa, Canadian
Remote Sensing Society, pg. 572-578.
SELBY, J.E.A., KNEIZYS, F.X., CHETWIND JR., J.H., McCLATEREY, R.A. (1978):
Atmospheric transmittance/radiance: Computer code LOWTRAN 4. Air Force
Geophysical Laboratories, Hanscon, AFB, MS. (AFGL-TR-78-0053).
TURNER, R., SPENCER, M. (.1972): Atmospheric model for correction of
spacecraft data. In: International Symposium on Remote Sensing of
Environment, 8. Ann Arbor, MI. Proceedings Ann Arbor, MI, ERIM, 845-947.
VIJAYKUMAR, N.L.; DIAS, L.A.V. .(1982): An interactive procedure to calculate
atmospheric transmittance. Presented at the II Brazilian Symposium on
Remote Sensing. Brasilia, DF, Brazil. Published in the Proceedings, INPE,
.Sao Jose dos Campos, Sao Paulo, Brazil.
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43


A procedure for testing the quality of LANDSAT atmospheric correction algorithms

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