PNNL-13501 - Pacific Northwest National Laboratory

Diagnostics Landsat 7
Sensor Performance
Using Ratios
Lo w Gain
(Corrected and ratioed to High Gain)
Area 1 model
Low_gain= a * High_gain + b
applied to area 2 for error diagnostics
Scatter within the same image pair Error due to change
in bright ness.
1.04
Low er brigh tness areas have biggest errors.
1.03
1.0 3
1.02
1.0 2
1.01
1.01
1.00
1.0 0
0.99
70
Area 2 high gain
Show s calibration error
Area 1 high gain
(Base line)
U.S. Department of Energy Pacific Northw est National Labora tory
9/ 5/0 0 15
Figure 2. LANDSAT 7 thermal channel calibration error
diagnostics using ARCA and CIT methods and technology
ratio was paired with the actual high-gain measurement as
input for the ratio-binning plot. The low intensity area
was processed in the same manner, with the same
regression coefficients and method for calculating ratio
and plot as used in higher digital number area. The
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0.9 9
382 FY 2000 Laboratory Directed Research and Development Annual Report
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240
comparison between the two different regions shows at
least 1% difference in ratio with respect to ~70 digital
number difference between the selected high and low
intensity areas. The error budget for the system should be
bigger than 1% given the difference of 1% for a 70 digital
number. This dependency will create hidden problems
for any automation process if not correctly diagnosed and
calibrated. The use of this technology and its
fundamental predecessors provides the foundation for
error modeling, diagnostics, and production of accurately
registered and calibrated images.
Summary and Conclusions
The human brain is our best image-processing tool. As a
result, image processing has traditionally required a large
amount of manual labor. The automated advanced
registration and calibration agent technique developed
during this project better employs and optimizes the
human element in the process, while still achieving a high
level of precision and accuracy.