Download Extended Hyperspectral Imaging System Modeling and Implementation for Subpixel Target Detection

Extended Hyperspectral Imaging System Modeling and
Implementation for Subpixel Target Detection
Bo Ding and John P. Kerekes
Chester F. Carlson Center for Imaging Science
Rochester Institute of Technology
54 Lomb Memorial Drive, Rochester, NY 14623 USA
ABSTRACT
For hyperspectral imaging system design and parameter trade-off research, an analytical model to simulate the
remote sensing system has been developed and is in progress to be made available to the community. The
analytical model includes scene, sensor and target characteristics, and also atmospheric features, background
spectral statistics, sensor specifications and target spectral statistics. The model is being implemented as a
web-based application through an RIT-hosted website. Predicting system performance has been verified by real
world data collected during the RIT SHARE 2012 collection and the data shows consistency with the simulated
results on calibration tarps and grass. Also, subpixel target spectral statistics are predicted by this model. Some
parameter trade-off examples are given and analyzed to explain the utility of this model.
Keywords: hyperspectral imaging, remote sensing system modeling, subpixel target detection, prediction model
1. INTRODUCTION
In support of hyperspectral imaging system design and parameter trade-off research, an analytical end-to-end
model to simulate the remote sensing system pipeline and forecast remote sensing system performance has been
developed1 and extended2 . Now the model is being implemented and will be made available to the remote sensing community through an RIT-hosted website in the near future. Users will be able to forecast hyperspectral
imaging system performance by defining an observational scenario along with imaging system parameters. The
implemented analytical model includes scene, sensor and target characteristics as well as atmospheric features,
background spectral reflectance statistics, sensor specifications and target class reflectance statistics. For target
detection applications, common data processing algorithms are also implemented. Predictions of system performance have been verified by comparing the forecast results to ones obtained using real world data collected
during the RIT SHARE 2012 data collection.
In this paper, first the analytical model is introduced, and then the web-based application to model a remote
sensing system is described and a typical application is presented. Sensor modeling has been extended to include
the airborne ProspecTIR instrument. Subpixel target spectral radiance statistics are predicted by the model with
given scene information, pre-measured background, target spectral reflectance statistics, and also the atmospheric
radiative transfer model MODTRAN. The predicted spectral radiance statistics are transformed into a feature
space and used with target detection algorithms to generate probability of detection versus false alarm curves
to evaluate system performance and parameter trade-offs. The validation data from the SHARE 2012 collection
show consistency with the simulated result on calibration tarps in the same system setting. Additionally, some
examples of parameter trade-offs are given and analyzed to explain the utility of this model for hyperspectral
imaging system design and research.
Further author information: John P. Kerekes: E-mail: [email protected]
Imaging Spectrometry XVIII, edited by Pantazis Mouroulis, Thomas S. Pagano, Proc. of SPIE
Vol. 8870, 88700P · © 2013 SPIE · CCC code: 0277-786X/13/$18 · doi: 10.1117/12.2022688
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2. HYPERSPECTRAL IMAGING SYSTEM MODELING
A novel systematic approach to analyze hyperspectral imaging system was introduced in 19913 . That work
serves as a cornerstone in remote sensing system modeling research based on input statistics data. Then the
forecasting and analysis of spectroradiometric system performance (FASSP) model1 was presented based on
the first paper extended for subpixel target detection. A more clear and straightforward system structure was
brought up and the whole process developed into separated function blocks, as is shown in Fig. 1 .
Sensor
Parameter
Files
Reflectance
Statistics
Library
Scene
Model
Spectral
Radiance
Statistics
Sensor
Model
Processing
Algorithm
Description
Spectral
Signal
Statistics
Processing
Model
Performance
Metrics
User
Inputs
Scene
Description
Figure 1. FASSP framework model2
This model is statistical data based, which means the inputs are mean and covariance statistics of the
reflectance data. This system analysis method may not be as intuitive as a physics-based image simulation
such as the DIRSIG system,4 which uses a 3D CAD model input to generate predicted image output, but it
reduces the computational cost and sometimes reduces the unnecessary system modeling complexity for specific
applications.
In a subpixel target detection scenario with multiple background scenes, the background scene reflectance is
modeled by a weighted combination of different backgrounds. The subpixel target is similar, which is a weighted
combination of the target itself and the background it resides in5 . Using the modeled statistics, MODerate
resolution atmospheric TRANsmission(MODTRAN)6 is applied to generate sensor reaching radiance.
For the sensor modeling, the channel response modeling is first applied to map the input dimension to the
sensor dimension, and then different kinds of noises and data link errors are calculated to fully model the sensor
behavior.2
The processing model first converts the radiance data back to reflectance by the empirical line method(ELM)7
. Then the result is usually transformed into some specified feature space, like simply choosing spectral band or
performing PCA to generate data for the target detection algorithm. Then a ROC curve can be generated to
evaluate the system performance and target detectability.
3. MODEL IMPLEMENTATION
Based on the analytical model described above, the end-to-end model is implemented and will be made available
to the hyperspectral imaging research community through the RIT-CIS website working as a web application.
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Hyperspectral Imaging System
Prediction Model
HTML
JavaScript
CSS
/startpage/
/customize/
/paraselection/
Users select sensor,
target, background
pre-fill text blocks
Initialize selectables
/error/
/result/
Users make changes
to parameters
Users redraw plots
FRONT END
BACK END
urls.py, forms.py
Y
main.py ->startpage()
main.py ->customize()
Y
main.py ->
paraselection()
“main”goes
wrong?
executable file
“main”
Radiance
SNR
ROC
text file
N
executable file
“initialize”
user
Customizing?
N
ModTran
pathconfig.ini
main.py ->results()
Python
C++
Figure 2. System Working Flow
It will allow users to select different sensors, targets and backgrounds for system modeling or choose some predefined scenarios with fixed settings. Also, for different modules in the model, the user could also adjust some
parameters to meet their specific needs, including the scene, sensor, and target parameters. The end-to-end
modeling system is divided into two parts in the implementation: the front end, which is the user interface
providing an interaction with the system parameters; and the back end, which is the algorithm computational
core doing all the calculation and data recording.
Sensor selection: Hymap
Target selection: Green BMW
Background Selection:
Background to Choose:
TREE
Old GRASS
ROAD
Avon Grass
White Tarp
Black Tarp
Bushes
Reflectance 10%
Reflectance 30%
Reflectance 50%
Background Chosen:
A
Soil 1
Snil
m
l77
[ add all )
Submit I
Figure 3. Sensor, Background and Target Selection
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For the application, the user can choose predefined scenarios to start with, or choose a customized scenario to
specify every parameter manually. Fig.3 shows the sensor, background and target selection user interface after
choosing ”customized scenario”. The background selection freedom is broad, and any combination is available.
Fig.4 shows the parameter selection interface. The pre-filled values in parameter selection forms are dynamically generated based on the sensor, background and target selection.
Hyperspectral Imaging System Prediction Model
Leave your parameters
PARAMETER HINT, \\I-LEN MODIFYING
Scene
Sensor
Target
Atmospheric Haze:
Noise Factor:
Target file Name:
2
1.0
Ground Altitude:
vgrnbmw. ref
Gain Factor:
Target Scale:
Rltv Calibration Error( %):
Solar Angle:
Atmospheric Model:
Integration Time(s):
2
Target Percentage(%):
950824_05_Tree1 _R. ref
950824_05 Grassl_R.rf
urban road.ref
Target
Inwhichbkg:
urban_road.ref
0.0001
Bkg Scale:
1.00
25
1.0
LO
Bkg file Name:
1.00
1.0
0
Background
Bkg Percentage(%):
50,40,10
Sensor Altitude(km):
Cloud Index:
1.0
0
Meteorological Range:
10
Wavelength To Choose:
453.8
467.4
481.9
496.9
511.7
526.5
541.6
556.5
571.2
585.9
600.7
Wavelength Chosen:
A
F1g
add all
Figure 4. Parameter Selection
4. MODEL VERIFICATION AND APPLICATION
For model verification, real world data collected from the RIT SHARE 2012 data collection campaign8 is used.
In order to validate the new implemented model for subpixel target detection, first the system is tested in the
scenario of a large uniform target and predicted spectral radiance output is compared with the collected radiance.
The pre-measured spectral reflectance data of targets are used as the input for the prediction model, and the
atmospheric parameters are adjusted according to the real collection. Fig.5(a) and Fig.5(b) show consistency
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Average Collected Radiance of Black Tarp
Predicted Radiance of Black Tarp
Average Collected Radlance of White Tarp
Predicted Radiance of White Tarp
(a) White Tarp
(b) Black Tarp
Figure 5. Tarp Validation
12
14
Predicted Radiance for Grass
Collected Radiance for Grass
12
10
Ê
y
Collected Grass Sample Eigenvalues
Predicted Grass Sample Eigenvalues
10
8
E
3
8
E
6
N
4
2
2
0
400
600
800
1000 1200 1400 1600
Wavelength(nm)
1800 2000 2200 2400
(a) Grass Radiance
2
3
4
5
Eigenvalue Rank
6
7
8
(b) Grass Covariance Matrix Eigen Values
Figure 6. Grass Validation
between collected and predicted data on white and black tarps. It is worth mentioning the noticeable difference
of the black tarp in the near-infrared band is as a result of the vegetation in the adjacent part of the scene, which
contributes a lot to the near-infrared region, especially since the total radiance is low for the black tarp.
Then the grass class is also used to validate the model. Fig.6(a) shows very little difference between collected
and predicted radiance data for grass, which means the prediction model works well. Also, Fig.6(b) shows
consistency between the principle eigenvalues of covariance matrices predicted by the model and estimated by
the collected data. So the model is developed and implemented well, and the result is reliable.
5. SUBPIXEL TARGET DETECTION TRADEOFF ANALYSIS
In addition to the calibration target validation, it is very important for end-to-end model validation to be
investigated at the output, which in a subpixel target detection scenario is to predict the detection probability
at a specified false alarm rate. The receiver operating characteristic (ROC) curve characterizes the tradeoff
between the detection probability and false alarm rate. The detection probability is related to target pixel fill
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factor, meteorological range, sensor relative calibration error, specified false alarm rate, etc. This prediction
model could be used to analyze the tradeoff between those parameters.
A typical scenario is defined in Table 1. This is a multi-background scene with a real subpixel target and it is
pre-defined in our implemented model. In this setting, several parameters are variables to analyze the trade-off
between these and probability of detection.
Table 1. Parameter Table
Parameter
Value
Atmospheric Haze
Rural Hazy
Ground Altitude
0km
Solar Angle
60
Atmospheric Model
Mid Latitude Summer
Meteorological Range
5km - 30km
Sensor
HyMap
Sensor Intergration Time
0.001s
Sensor Altitude
1km
Sensor Channels
126
Sensor Relative Calibration Error
1% - 4%
Target Profile
Green BMW Car
Target Percentage
5% - 30%
Target in Background
Road
Background File
Tree, Grass, Road
Background Percentage
50% Tree, 40% Grass, 10% Road
The scenario in the table above defines a typical scene of vehicle detection when the vehicle is on the road
in an open vegetation area. Fig.7(a) shows the relationship among pixel fill factor, probability of detection and
meteorological range at a fixed false alarm rate, which is 10−5 in this scene, and 1% relative calibration error.
Meteorological range will affect the radiance signal even though the system includes an atmospheric compensation
step. The figure shows better detection performance with larger meteorological range and larger pixel fill factor.
When the meteorological range is larger than 10km, it shows only minor effect to the system performance.
Fig.7(b) shows the relationship among pixel fill factor, probability of detection and relative calibration error
at the fixed 10−5 false alarm rate and 20km meteorological range. This calibration error may come from
many sources in a real remote sensing system, such as non-uniformity correction or errors in the atmospheric
compensation. The figure shows a relatively uniform relationship between this error and the system performance.
6. SUMMARY
This paper presents a hyperspectral imaging system analytical model including its implementation, validation
and its application to subpixel target detection. The sensor model has been extended to include the airborne
ProspecTIR instrument, and the data from this instrument have been used to validate the system. The model
implementation is presented by introducing its workflow and interface. Then the system is verified by comparing
the collected real world data from the RIT SHARE 2012 with the model predicted data. The predicted radiance
data of relatively simple scenario such as single background calibration tarps and grass show good consistency
with the collected data from ProspecTIR instrument. Subpixel target spectral radiance statistics are predicted
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1
1
0.9 -
0.9 -
0.8 -
0.8 -
= 0.7 -0
= 0.7 -0
d 0.6 -
d
d 0.6 0
ó 0.5
d
0
ó 0.5
11 0.4
0.4
0
0.3-
0.3-
0.2 -
0.2 -
0.1 -
0.1 -
0
0
0
5
10
15
20
25
30
10
Pixel Fill Factor( %)
15
20
25
30
Pixel Fill Factor( %)
(a) Sensitivity of Detection to Meteorological Range
(b) Sensitivity of Detection to Relative Calibration Error
Figure 7. Trade-off Analysis
by this model with pre-measured background, target spectral reflectance statistics, and pre-defined varying scene
parameters. The example of parameter trade-offs such as meteorological range, relative calibration and pixel
fill factor with system detection performance are given and analyzed to explain the utility of this model for
hyperspectral imaging system design and research.
ACKNOWLEDGMENTS
The authors gratefully acknowledge the work and support from Robert Krzaczek in establishing the website
hosting our implemented model and the support and advice from Dr. David Messinger, Director of Digital
Imaging Remote Sensing Laboratory, RIT, Dr. Jeff Pelz, Director of Visual Perception Lab, RIT, also the other
graduate students’ help in the research.
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