In this paper we present a methodology for simulating multispectral imagery (MSI) using hyperspectral imagery (HSI), and present a validation of the technique using one nearly coincident Landsat TM and AVIRIS data set. Generation of MSI from HSI supports several investigations including selection of multispectral sensor band edges, and engineering trade studies related to on-board or ground-based aggregation of HSI to simulate MSI. In addition, the utility of this technique as a potential procedure for monitoring calibration changes in spaceborne instrument is also addressed

Kalman, Linda S.

Peltzer, Gerard R.

English

NASA Technical Reports Server

N95- 23869
SIMULATION OF LANDSAT THEMATIC MAPPER IMAGERY
USING AVIRIS HYPERSPECTRAL IMAGERY
Linda S. Kalman and Gerard R. Peltzer
The Aerospace Corporation
P.O. Box 92957
Los Angeles, CA 90009-2957
I. INTRODUCTION
In this paper we present a methodology for simulating multispectral imagery
(MSI) using hyperspectrai imgery (HS[), and present a validation of the technique using
one nearly coincident Landsat TM and AVIRIS data set. Generation of MSl from HSI
supports several investigations including selection of multispectral sensor band edges, and
engineering trade studies related to on-board or ground-based aggregation of HSI to
simulate MSI. In addition, the utility of this technique as a potential procedure for
monitoring calibration changes in spaceborne instruments is also addressed.
2. SIMULATION METHODOLOGY
The signal, Se, (in electrons) in a selected waveband of a multispectral sensor is
given by :
Se= K _ R(_,) F(k) k/hc dk (1)
where R(Z) represents the sepctral radiance distribution at the sensor aperture, F(_.) is
the combined spectral responsivitiy of the sensor (including spectral filter response,
detector spectral responsivity, etc.), and K represents an assortment of wavelength
independent components including sensor aperture solid angle, integration time, detector
size, etc. The conversion between photons and electrons is represented by the term h/hc.
The integration is performed over the spectral bandpass of the band in question.
The process of inverting equation (1), eg. the estimation of R(k) from a
measured signal does not have a unique solution. Instead, one may estimate the
equivalent radiance, Re, ie. the spectrally flat radiance distribution which when used in
equation (1) would produce the same measured signal:
Se= K.f Re F(k) _,/hc dk - K Re _ F(_.) _,/hcdk (2)
Equating (1) and (2) we have a definition of this equivalent radiance:
Re = .[ R(k) F(k) _.d_, / _ F(_,) k d_. (3)
The equivalent radiance, Re, when multiplied by the nominal sensor bandpass, Ak, is
commonly referred to as the "inband" radiance. The relationship between this inband
radiance and Landsat digital values, Dn, is defined through the calibration information
supplied with Landsat data :
Re A_, = Gain * Dn + Offset (4)
Simulation of Landsat digital data can therefore be accomplished through computation of
the inband radiance, Re A_,, followed by application of the appropriate band calibration
coefficients.
Calibrated imagery, such as that produced by the AVIRIS sensor is an ideal
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https://ntrs.nasa.gov/search.jsp?R=19950017449 2020-06-16T08:30:43+00:00Z
candidatefor estimation of R(k). Given n hypersperspectral bands spanning the bandpass
of the multispectral sensor band being simulated, equation (3) can be approximated as :
n
Re = _ RA(i) F(k i) _,iA_,i / Z F(_, i) _'i AZ'i (5)
i=l
where RA(i) is the calibrated spectral radiance measured in AVIRIS band i, _'i is the
center wavelength of band i, A_, i is the bandpass of band i, and F(_, i) is the spectral
response value of the sensor being simulated at wavelength _'i" The fidelity with which
Re can be generated depends on the calibration accuracy of the hyperspectral imagery and
also improves as the number and spectral resolution of the hyperspectral bands covering
the bandpass in question increases.
3. EXPERIMENT AND RESULTS
We have investigated the merits of this simulation technique using an AVIRIS
scene collected nearly coincident with a Landsat TM scene. The AVIRIS scene obtained
on 10/08/90 at 1041 Z, and covers the area surrounding Corvallis Oregon. A Landsat 5
TM scene (geometrically corrected, P type data) was collected on the same day at
approximately 1745 Z. Weather reports generated at Eugene (50 miles away) as well as
the general appearance of both images indicate a relatively clear, cloud free atmosphere
during the acquisitions. The FWHM and center wavelength files accompanying the
calibrated AVIRIS imagery were used in equation (3). Preflight measurements of the
Landsat sensor spectral response function (Engel, 1990) were used as a source for F( ki ).
The six reflective bands of the l_andsat 5 TM instrument were simulated using equations
(3) and (5) along with calibration data found in the TM data header file. (A multiplicative
sun angle correction based on the change in solar zenith angle between the two collections
was applied to the simulated TM radiances prior to conversion to digital counts.)
3.1 Evaluation of Results
A portion of the TM scene was geometrically warped to overlay the AVIRIS
data. A common sub area of the two images was then extracted for statistical analysis.
Color composites of the simulated and actual TM scenes are provided in Slide 3.
Histograms of the simulated and actual TM bands were computed for the spatially
registered areas. A few of the histograms are displayed in Figure 1 and a summary of the
scene statistics presented in Table 1. The agreement between simulated and actual scene
statistics and histograms is exceptionally good. Both band 4 and band 5 histogram
comparisons show the simulated image histograms to be slightly compressed. To
investigate the source of this effect we computed statistics for several homogeneous sub
areas in the simulated TM imagery and corresponding areas in the _ original TM
image. Samples of these results are plotted in Figure 2. Linear relationships between
simulated Dn and actual TM Dn were found to exist for each band. The linear regression
data are shown in Table 2. Several investigators (including Thome et al, 1993) have
observed a decrease in TM response in each band over the course of the years. Since
EOSAT has not changed calibration coefficients since 1986, we would expect that use of
those coefficients to compute Dn from the radiance derived using the AVIRIS data would
yield ]aig]lg,Lvalues than the actual digital values observed in the TM scene.
Unfortunately, this is not what we observed. This discrepancy suggests that perhaps the
AVIRIS calibration for this data set is erroneously low. With only one data set it is
difficult to draw decisive conclusions regarding the source of these discrepancies.
In this analysis we have focussed upon the radiometric fidelity of the simulation
procedure. A rigorous simulation of multispectral imagery would include an accurate
98
modelingofelectronicandsignaldependentnoise in the image, as well as modification of
the MTF characteristics as appropriate. The spectral band aggregation generally produces
the desirable effect of decreasing the noise level in the simulated image. The user is then
flee to add back in the desired amount of noise expected in the sensor product so long as
the individual spectrometer band noise levels are comparable to those of the sensor to be
simulated. Furthermore, the aggregation procedure increases the effective bit-resolution
of the simulated data, allowing the user to explore design tradeoffs related to bit allocation
and digitization schemes. Finally, the accuracy of the simulation is dependent upon the
spectral resolution of the HSI. The number of bands actually required will most likely
depend on the shape and complexity of the sensor response function being simulated.
3.2 Potential applications of the simulation technique.
Discrepancies between the simulated TM data and the actual TM data can be
attributed to calibration errors in either instrument, as well as to the coarseness arising
from the limited number of AVIRIS spectral bands covering any given TM band,
particularly in the shorter wavelengths. Changes in responsivity for the TM instrument
throughout the years have been observed by investigators including Thome et al. (1993).
We performed an experiment in which we shifted the long wavelength edge of the TM2
spectral response curve; in effect, changing the effective f'dter passband. The long edge
was shifted from -10 nm to +10 nm from its nominal position. The statistics derived
from these experiments are shown in Table 3. It is clear that a change in filter response
similar to this could be detected and quantified using imagery simulated from underflights
with a hyperspectral sensor such as AVIRIS. We are currently performing analyses to
determine if this procedure can be used to identify the exact nature of the change in filter
response : eg. to distinguish between shifts at the low or high wavelength end, or both,
as well as changes in transmittance due to contamination of the optical surfaces or
changes in detector responsivity over time.
4.0 SUMMARY
We have presented a procedure for simulating multispectral imagery using
hyperspectral data. The simulation technique is straightforward and has been shown to
produce encouraging results in the one case studied, although with only one data set it is
impossible to distinguish errors in the AVIRIS calibration or the TM calibration. We are
currently in the process of obtaining additional data sets to use in further validation of the
procedure. It is clear however, that reasonable simulations of multispectml imagery can
be produced using this technique. A simulation technique such as this provides the
capability to perform engineering design trades for future multispectral sensors related to
determination of spectral band placement. In addition, the analysis technique may be
useful as a tool for monitoring changes in calibration of space sensors using aircraft
underflights. The sensitivity of this technique for detecting changes in sensor
characteristics is currently under study.
5.0 ACKNOWLEDGEMENTS
The authors wish to thank Dr. P. Slater (Univ. of Arizona, Tucson) for his
helpful comments and suggestions regarding the course of this work. This work was
supported by the Aerospace Sponsored Research program.
6.0 REFERENCES
Engel, J., SBRC (Private Communication).
Thome, K.J., D. I. Gellman, R.J. Parada, S. F. Biggar, P.N. Slater, and
M.S. Moran, 1993. "In-flight Radiometric Calibration of Landsat-5
Thematic Mapper from 1984 to Present", Voi. 1938-14, Proc. SPIE,
Orlando, Fla. April, 1993.
99
Table 1. Comparison of Simulated Scene Statistics]
with Actual Scene Statistics
Band Simulated Mean Actual Mean
Dn Dn
1 54.3 54.0
2 22.2 22.0
3 24.1 24.0
4 40.3 41.0
5 55.3 57.9
7 24.5 25.8
_LD*t0 4 ......... I ......... I ......... J ......... I .........
2_0-10 4
1_5_t0 4
1.0-10 4
5.0.t0 3
3O
2.0-10 4
/ i
,, \ band i
o
"rid I_l
J _
1__10 4
1.0.10 4
5.0,10 3
0
0
IOOIX
0
band 4
s'- "_
• i
i
TM_
J
I t
,,_ band 5
TMI_
Figure 1. Histogram Plots of Simulated (dotted line)
and actual (solid line) TM scenes.
8O
70
I,-
-o 60
o
_, so
40
40
Band 1
i
_5_ 8060 TM Dn 70
5O
4O
=E
l,-
•o 30
o
g
"5
_e 20
b')
10
Band 4 /"
• I • i • I • i '
10 20TM Dn30 40 50
Figure 2. Simulated Dn plotted against actual Dn for
corresponding subareas (bands I and 4).
Table 2. Equation coefficients describing the
relationship between simulated and actual TM values.
Simulated CY) = Gain * Actual (X) + Offset
Band Gain Offset
.77 1.69
.83 4.58
.81 4.94
.75 8.56
.82 5.65
.91 2.31
Table 3. Statistics Derived from Shifting Upper
Edge of TM Band 2 response function.
[_ad Shift (am) Simulated Scene
Mean Dn
-10 20.5
- 4 21.9
0 22.9
+ 4 23.8
+10 25.3
100


Simulation of landsat thematic mapper imagery using AVIRIS hyperspectral imagery

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