Imaging Spectrometer Stray Spectral Response: In-Flight ... · 18/10/2017 · Imaging Spectrometer...
Transcript of Imaging Spectrometer Stray Spectral Response: In-Flight ... · 18/10/2017 · Imaging Spectrometer...
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Jet Propulsion LaboratoryCalifornia Institute of Technology
David R. Thompson, [email protected]!Joseph W. Boardman*!Michael Eastwood!Robert O. Green!Justin Haag!Pantazis Mouroulis!Byron Van Gorp!!
Jet Propulsion Laboratory, California Institute of Technology!*Analytical Imaging and Geophysics, Inc. Boulder, CO!!Copyright 2017 California Institute of Technology. All Rights Reserved. US Government Support Acknowledged.!!!
Imaging Spectrometer Stray Spectral Response: In-Flight Characterization and Correction!
11/1/17! [email protected]! 1
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Motivation!• Non-Gaussian tails of spectral response functions
can be difficult to characterize in the laboratory!• Calibration can shift during deployment!• Small SSRF contributions can damage downstream
atmospheric correction!• In-flight techniques are useful for validating and
updating laboratory measurements. !
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-20 -15 -10 -5 0 5 10 15 20Channel
10-4
10-3
10-2
10-1
Res
pons
e
Nominal SRFStray SRFActual SRF
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Method!
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• Sequential estimation of Nominal and Stray SRF parameters. !
• Exploit predictable changes in the shape of the A band across varying surface elevation.!
• Diverse scene content provides numerical leverage to characterize spectral response tails!
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Estimation of nominal SRF[Thompson et al., Atmos. Meas. Tech 2015]!
Optimize a wavelength shift to match high-contrast atmospheric absorption features!
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Estimation of nominal SRF[Thompson et al., Atmos. Meas. Tech 2015]!
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-0.0003!
-0.0002!
-0.0001!
0!
0.0001!
0.0002!
0.0003!
0! 100! 200! 300! 400! 500! 600! 700!
Wav
elen
gth
shift
(mic
ron)!
Sample!
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Stray SRF Measurement model Adapted from [Zhong et al., 2006]!
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StrayRadiance
NominalRadiance
MeasurementNoise
MeasuredRadiance
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Stray SRF Measurement model Adapted from [Zhong et al., 2006!
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StrayRadiance
NominalRadiance
MeasurementNoise
MeasuredRadiance
GHLA HLA 𝜖LMRadianceatsensorNominalSRFStraySRF
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StrayRadiance
NominalRadiance
MeasurementNoise
MeasuredRadiance
RadianceatsensorNominalSRFStraySRF
Stray SRF Measurement model Adapted from [Zhong et al., 2006]!
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A Linear SRF Correction Matrix!
Calculate a Moore-Penrose Pseudoinverse:!!!!This estimates the nominal SRF:!!!!!!A similar correction fixes cross-track stray light !
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CorrectedRadiance
Correc=onmatrix
DistortedMeasurement
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Retrieve Stray SRF from a “Calibration Scene”!Death Valley Transect, 2014 (visible RGB)!
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Predict A band radiances using a Digital Elevation Model!
Nonlinear least squares optimization finds SSRF parameters!
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Estimation accuracy for Gaussian SSRF (simulated)!
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0.02 0.04 0.06 0.08 0.1�
4
5
6
7
Gau
ssia
n �
SNR 400SNR 200
(Straylightfrac=on)
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Estimation accuracy for Lorentz SSRF (simulated)!
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0.02 0.04 0.06 0.08 0.1�
4.71
5.8875
7.065
8.2425
Lore
ntz
half
wid
th
SNR 400SNR 200
(Straylightfrac=on)
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Fit error for candidate SSRF shapes!
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bestfit
-30 -20 -10 0 10 20 30Channel
10-6
10-4
10-2
100
Response
gaussianlorentzpareto
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Improvement in O2 A band fit!
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745 750 755 760 765 770 775Wavelength (nm)
3
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6R
adia
nce
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nm
-1 s
r-1 c
m-2
)
NominalMeasuredCorrected
745 750 755 760 765 770 775Wavelength (nm, with offset for clarity)
0
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Rad
ianc
e sq
uare
d er
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MeasuredCorrected
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Correction fixes a bias in pressure altitude estimates!
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0 0.5 1 1.5 2 2.5 3Elevation, km ASL
0
0.5
1
1.5
2
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3
Estim
ated
Pre
ssur
e Al
titud
e, k
m A
SL
1.372
0 0.5 1 1.5 2 2.5 3Elevation, km ASL
0
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1
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2
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Estim
ated
Pre
ssur
e Al
titud
e, k
m A
SL0.721
Beforecorrec6on A8ercorrec6on
Ivanpahvalida6onsite
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Reflectance validation!
400 600 800 1000 1200 1400 1600 1800 2000 2200 2400Wavelength (nm)
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lect
ance
400 600 800 1000 1200 1400 1600 1800 2000 2200 2400Wavelength (nm)
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lect
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950 1000 1050 1100 1150 1200Wavelength (nm)
0.28
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Ref
lect
ance
950 1000 1050 1100 1150 1200Wavelength (nm)
0.25
0.3
0.35
0.4
Ref
lect
ance
test interval
Reference interval
q(x) = 0.0045
q(x) = 0.0032
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Reflectancequalitymetric:
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India Validation Results!
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• 26of37flightdaysshowsignificantimprovements(p<0.001)• Typicalimprovementis20-35%• Noflightdayshowsasta=s=callysignificantaccuracyreduc=on
Frac=onalimprovementfor277scenes
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Agreement with laboratory data!
-10 -8 -6 -4 -2 0 2 4 6 8 10Channel
10-4
10-2
100
Relat
ive re
spon
se
Laboratory measurementAtmospheric fitNominal SRF
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Spatial dimension!
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• ExploitNear-Infrared(NIR)oceanreflectance• Useahaze-freedaytoconstrainpathradianceandadjacencyeffects• Useawind-freedaywithnadirobserva=onstolimitglint• DarkwatershouldbehighlyabsorbantinNIR• Dataset:2015Greenlandiceflow
500 1000 1500 2000 2500Wavelength (nm)
0
0.1
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Ref
lect
ance
Sea!
Ice!
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“Halo” reduction!
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Original RGB! 612 nm, equalization stretch!(0-3 uW nm-1 sr-1 cm-2)!
612 nm, after CRF correction!
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Discussion!• Can leverage scene invariant properties to fit PSFs!• Some advantages to using separable functions!
– Numerical stability, fairly easy to prevent ringing & overcorrection!– Can model CRF or SRF or both, and fit them independently!
• Positive results on held-out validations!– Appears to fix our pressure altitude bias!– Improves H2O residuals!– Improves spatial halos!
• Implemented in latest India release, and all AVIRIS-NG datasets starting from 2016!
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Thanks!!NASA Earth Science!The AVIRIS-NG Team, including Sarah Lundeen, Brian Bue, Winston Olson-Duvall, Ian McCubbin, Mark Helmlinger, and others!
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