igarss04.hyperspectral
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United
States
Canada
Australia
Argentina
Minerals
Forests
Agriculture
Grasslands GlaciersDeserts
SaharaAntarctica
Hyperspectral Image Processing and Analysis
for Land Cover Characterization
Dr. Jay Pearlman and Dr. Melba CrawfordSeptember 19, 2004
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Course Overview
• Introduction to EO Remote Sensing
– Photon end to end flow
– Alternative sensor configurations
• Hyperion Instrument – design and trades
• Sensor Calibration• Data Processing and Corrections
• Classification of Hyperspectral Data
– Input space reduction• Feature selection and extraction
– Output space reduction
• Multiclassifier Systems
• A View Forward
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EO Remote Sensing Process Overview
0
50
100
150
200
250
400 700 1000 1300 1600 1900 2200 2500
Wavelength (nm)
I r r a d i a n c e ( µ W / c m
2 / n m )
0
0.2
0.4
0.6
0.8
1
400 700 1000 1300 1600 1900 2200 2500
Wavelength (nm)
T r a n s m i t t a n c e
O3
O2H2O
H2O
H2O
CO2
H2O
CO2
H2O
CH4
O2
H2O
Atmospheric
Scattering
O2H2O
0
1
2
3
4
5
6
7
8
400 700 1000 1300 1600 1900 2200 2500
W avelength (nm)
R a d i a n c e ( µ W / c m
2 / n m / s r )
Path Radiance
Solar Irradiance
Sensor Datadownlink
Path Radiance
Surf. Reflectance
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
400.0 700.0 10 00.0 1300.0 1600.0 1900.0 2200.0 2500 .0
Wavelength (nm)
R e f l e c t a n c e
Co ni fer Gr ass
Broad Leaf Sage_Brush
NPV
Calibrated Signal
0
2
4
6
8
1 0
1 2
1 4
1 6
4 0 0 7 0 0 1 0 0 0 1 3 0 0 1 6 0 0 1 9 0 0 2 2 0 0 2 5 0 0
W a v e le n g t h ( n m )
R a d i a n c e ( µ W / c m
2 / n m / s r )
A t m o s p h e r i c O x y g e n
L e a f W a t e r
A t m o s p h e r i c
C a r b o n D i o x i d e L
e a f C h l o r o p h y l l
L e a f S u g a r
a n d C e ll u l o s e
P h o t o s y n t h e s i s
6 H 2 O + 6 C O 2 + p h o t o n = > C 6 O 6 H 1 2 + 6 O 2
Measured signal
0
100
200
300
400
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600
700
800
900
1000
400 700 1000 1300 1600 1900 2200 2500
Wavelength (nm)
R e c o r d e d S i g n a l ( D N )
Signal
0.0
50.0
100.0
150.0
200.0
250.0
300.0
350.0
400.0
450.0
500.0
550.0
600.0
650.0
700.0
400.0 700.0 1000.0 1300.0 1600.0 1900.0 2200.0 2500.0
Wavelength(nm)
6 00 K
8 00 K
1 00 0 K
1 20 0 K
Sun
Emitted Radiance
0.00
0.10
0.20
0.30
0.40
0.50
0.60
0.70
0.80
0.90
1.00
400 600 800 1000 1200 1400 1600 1800 2000 2200 2400
Wavelength (nm)
R e f l e c t a n c e
Atmospheric Correction
to Surface Reflectance
Reflectance
At Sensor Radiance
0
2
4
6
8
10
12
14
16
18
20
400 600 800 1000 1200 1400 1600 1800 2000 2200 2400 2600 2800
Wavelength (nm)
R a d i a n c e ( µ W / c m
2 n m s r )
Upwelling
GroundStation
AtmosphericCorrection
DataExploitation
Atm. Transmittance
CalibrationParameters
Courtesy of R. Green
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Solar Spectrum and Atmospheric Transmission
0
10
20
30
40
50
60
70
400 700 1000 1300 1600 1900 2200 2500
Wavelength (nm)
R a d i a n c e ( µ W
/ c m
2 / n m / s r )
0.0
0.10.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
Atmosphere
Solar w/ 1.0 Reflectance
T r a n s mi t t a n c e
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Solar Irradiance
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Reflection from a Metal Roof
San Francisco: January 17, 2001: Roof TopHypGain_revA
0
20
40
60
80
100
120
140
160
350 850 1350 1850 2350
Wavelength (nm)
R a
d i a n c e ( W / m 2
/ u m / s r )
Oxygen
Line
water
absorption
band
water
absorption
band
water
absorption
band
water absorption
band
VNIR-SWIRSpectral
Overlap C0
non-utilized
spectral
Reflection of Solar Irradiance of aroof top, a relfective surface.
Some atmospheric features are
indicated.
non-utilized
spectral
channels
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Radiance To Reflectance Inversion
0
5
10
15
20
25
400 700 1000 1300 1600 1900 2200 2500
Wavelength (nm)
R a d i a n c e ( u W / c m
2 / n m
/ s r ) S pectrum 1 Spectrum 2
Spectrum 3 Spectrum 4
Spectrum 5
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
400 700 1000 1300 1600 1900 2200 2500
Wavelength (nm)
R e f l e c t a n c e
Grass Pavement
Water Roof
Tree
Lt = µF0ρa/π + µF0TdρsTu/π
Lt is the at sensor radiance
µ is the cosine of the solar zenith angle
F0 is the exo atmospheric irradianceρa is the upward reflectance of the atmosphere
Td is the downward transmittance
ρs is the reflectance of the surface
Tu is the upward transmittance of the atmosphere
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Alternative Sensor Configurations
• Multispectral vs. Hyperspectral
• Whiskbroom vs. Pushbroom
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Hyperspectral and Multispectral Perspectives
Wavelength
M e a s u r e d R e f l e c t a n c e
Band 3
Band 4
Band 2
Band 1
Wavelength
M e a
s u r e d R e f l e c t a n c e
Wavelength
R e f l e c t a n c e
Spectral characteristic of scene
Hyperspectral Imaging
Hundreds of bands
Multispectral Imaging Few bands
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Landsat Picks the Windows – But Misses a Lot
L1 L5 L7
L2 L3 L4
Pan
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Hyperion surface composition map agreeswith known geology of Mt. Fitton in South
Australia
(1) Published Geologic Survey Map
(2) Hyperion three color image (visible) showing regions of interest(3) Hyperion surface composition map using SWIR spectra above
(1) (2) (3)
Hyperion Spectra Reference Spectra
(a) (b)
Hyperion-based apparent
reflectance compares with
library reference spectra
Courtesy of CSIRO, Australia
Hyperion Maps Mt. Fitton Geology
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Mt. Etna Sample Spectra
veg.
lava
smoke
Hyperion Spectra of Mt Etna Scene July 13th 2001
0
20
40
60
80
100
400 600 800 1000 1200 1400 1600 1800 2000 2200 2400
Wavelength
R a d i a n c e ( W / m 2
- u m - s r )
lava smoke vegetation
1639 nm 2226 nm1234 nm
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Hyperspectral Sensor Configurations
Similar Similar Bandwidth
Space and AirborneAirborneApplication domain
Harder EasierCalibration
Harder EasierSpatial uniformity
LongerShorter Relative pixel dwell
time
2-dimensional arrayLinear arrayDetectors
PushbroomWhiskbroomCharacteristic
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Hyperion Configuration
• Pushbroom configuration with common fore optics – 256 field-of-view locations, defines 7.7 km swath width, 30 meters
each
– 6925 frames of data, defines 185 km swath length, 30 meters each.(sample based on length of data), frame rate timed with spacecraft
velocity
Time
6925
frames
field of view256 pixels
spectral range
242 bandsHyperion Data CubeX-swath width
Y-swath length
Z-spectra signature
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The Hyperion Sensor
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Hyperion Imaging Spectrometer
Hyperion is a push-broom
imager
• 220 10nm bands covering 400nm -
2500nm
• 6% absolute rad. accuracy
• Swath width of 7.5 km
• IFOV of 42.4 µradian
• GSD of 30 m
• 12-bit image data• Orbit is 705km alt (16 day repeat)
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Hyperion Technologies
SPECTROMETER
(curved grating)
Pulse Tube
CRYOCOOLER
CALIBRATION
(spectral/
pushbroom)
Reflecting
TELESCOPE
DATARATES
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Hyperion Sensor Assembly
Baffle /
CALIBRATION
Reflecting
TELESCOPE
Signalprocessor
Spectro-
meter
Pulse Tube
CRYOCOOLER
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Hyperion Optical System
Three mirror anastigmat (TMA) foreoptic
Grating
Spectrometer FPA
Entrance slit
Focal
Plane
Array
Earth’s
Surface
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Hyperion Subassemblies
Hyperion
Electronics
Assembly
(HEA)
Cryocooler Electronics
Assembly
(CEA) Hyperion Sensor Assembly (HSA)
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Hyperion Block Diagram
VNIRGIS
SWIRFPA &FPE
VNIRCCD &
FPE
VNIR ASP
SWIR ASP
Radiator
Cryo-cooler
Cooler Radiator
Cryo-cooler Electronics
(CEA)
(HEA)
Processor,Telemetry
SignalConditioning & Acquisition,
Data Formatter,
& Power Conversion
Hyperion Instrument Electronics
1773Command &Telemetry
SWIRImage DataRS-422
VNIRImage DataRS-422
S/C 28 VDC_1
S/C 28 VDC_1
Heater S/C 28 VDC_ Htr
SWIRGIS
Thermally Connected to S/CThermally Isolated from S/C
Al Honeycomb StructureHeater &
Temp Sensor
Hyperion Sensor
Fore-optics
In-flightCalibration
Source
Aperture Cover
TemperatureSensors (4)
To Spacecraft
Heater Power
Radiator
RS-422
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Hyperion Key Characteristics
Characteristic Pre-launch On-orbitGSD (m) 29.88 30.38
Swath (km) 7.5 7.75VNIR MTF @ 630nm 0.22-0.28 0.23-0.27
SWIR MTF @ 1650nm 0.25-0.27 0.28
VNIR X-trk Spec. Error 2.8nm@655nm 2.2nm
SWIR X-trk Spec. Error 0.6nm@1700nm 0.58
Abs. Radiometry(1Sigma) <6% 3.40%
VNIR SNR (550-700nm) 144-161 140-190
SWIR SNR (~1225nm) 110 96
SWIR SNR (~2125nm) 40 38
No. of Spectral Channels 220 200 (L1)VNIR Bandwidth (nm) 10.19-10.21 **
SWIR Bandwidth (nm) 10.08-10.09 **
** not measured
O P T I C A L
R A D I O -
M E T R I C
S P E C T R A L
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Hyperion SNR
0
50
100
150
200
400 700 1000 1300 1600 1900 2200 2500Wavelength (nm)
S N R
Measured
VNIR ModelSWIR Model
Radiometric performance model based
on 60o Solar zenith angle, 30% albedo,
standard scene
550 nm 650 nm 700 nm 1025 nm 1225 nm 1575 nm 2125 nm161 144 147 90 110 89 40
Hyperion Measured SNR
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Spectrometer Overlays
V
SLevel 1 data: 438-926nm and 892-2406nm
Bands 9-57 and 75 - 225;
SWIR is West of VNIR and rotated CCW by one pixel
S W I R
V N I R
Band Center(nm) FWHM(nm)
50 854.66 11.27
51 864.83 11.2752 875 11.28
53 885.17 11.29
54 895.34 11.3
55 905.51 11.31
56 915.68 11.31
57 925.85 11.31
58 936.02 11.31
59 946.19 11.31
71 852 11.17
72 862.09 11.17
73 872.18 11.17
74 882.27 11.17
75 892.35 11.17
76 902.44 11.17
77 912.53 11.17
78 922.62 11.17
79 932.72 11.1780 942.81 11.17
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Polarization - VNIR
AVERAGED OVER FOVIn Units of Percent Polarization
Polarizer Angle [degrees]
W a v e l e n g t h
[ n m ]
0 50 100 150 200 250 300 350
500
550
600
650
700
750
800
850
900
950
1000
1050 -5
-4
-3
-2
-1
0
1
2
3
4
5
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Polarization - SWIR
Polarizer angle [degrees]
S p e c t r a l C h a
n n e l
0 50 100 150 200 250 300
20
40
60
80
100
120
140
160
-5
-4
-3
-2
-1
0
1
2
3
4
5
AVERAGED OVER FOVIn Units of Percent Polarization
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Echo Variation Map
• Ratio images for each intensity
level were interpolated to create
maps of echo variation across
the entire FPA.
• Based on this map a technique
for echo removal was
implemented.
• The removal algorithm consists
of subtracting a shifted andscaled version of each frame
from itself. Scaling is a
multiplication of the image by
the echo variation map.
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Profiles Before(red)/After(blue) Echo Removal
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Hyperion Images Before / After Echo Removal
A l Pi l C ll
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Anomalous Pixel Collage – Possibilities including striping
Band Swath Pix Band Swath Pix Band Swath Pix Band Swath Pix Band Swath Pix
5 114 12 6 32 114 99 92 168 256
5 141 12 114 33 114 116 127 168 256
6 6 13 114 34 114 116 127 169 12
6 68 14 114 39 177 116 138 169 12
6 172 14 247 40 13 116 138 169 23
7 7 15 114 48 20 119 240 169 23
7 68 16 114 49 20 119 240 169 23
7 179 17 114 50 20 120 240 190 113
7 185 18 114 51 20 120 240 190 113
8 7 19 114 52 13 121 196 190 113
8 12 20 114 52 33 125 54 200 8
8 68 21 114 53 33 125 115 200 88 114 22 114 57 13 125 160 200 8
8 121 23 114 61 93 125 164 201 8
9 6 24 114 72 95 127 40 201 8
9 68 25 114 72 95 127 66 201 8
9 114 26 114 75 2 127 213 203 104
10 6 27 47 94 82 128 30 203 10410 114 27 114 94 82 128 96 203 115
10 131 28 47 94 93 128 126 203 115
10 199 28 114 94 93 165 148 203 116
11 6 29 114 99 81 165 248 222 98
11 114 30 114 99 81 168 245 225 186
11 199 31 114 99 92 168 245
Bad Pix 3 - L1B1; CSIRO – Jupp; Dark Image – Pearlman; PFC - Han
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Hyperion Nominal Data Modes
Mode Cover
Position
Data collect Comment
Standby Closed None Default mode for active stateDark
calibration
Closed Minimum 100 frames Performed as close as possible to
imaging, before and after
Lamp
calibration
Closed Minimum 100 frames Performed after second dark
calibration; two radiance levels
Solar calibration
Open 37degrees
Minimum 1 second Nominal 1 cube
Performed over North Pole only tokeep cover out of ALI keep-out zone;
yaw maneuvers required
Lunar
calibration
Fully open
(135o)
Minimum: 1 second
Nominal: 1 cube
Performed on dark side of earth; off-
track spacecraft pointing required
Groundcalibration
Fully open(135o)
Minimum 1 second Nominal 1 cube
Ground target selected
Imaging Fully open
(135o)
Minimum 1 second
Nominal 9 cubes
Nominal data collect is equivalent to
Landsat scene, and takes 27 seconds.
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Performance Tradeoffs
• Spatial Resolution
• Spectral resolution
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Landsat
SAC-C
MODIS
MODIS
250m
1000m
Coleambally Image Collection – 30m to 1km
C i f H l I
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Comparison of Hyperspectral Instruments
Parameter Lewis HSI Hyperion
Volume (L x W x H, cm) 43x69x94 39x75x66
Weight (Kg) 39.5 49
Avg Power (W) 66 51
Peak Power (W) 126
Aperture (cm) 12 12
IFOV (mrad) 0.057 0.043
Crosstrack FOV (deg) 0.84 0.63
Wavelength Range (nm) 380 - 2450 400 - 2450
Spectral Resolution (nm) 5.1/6.45 10
No. Spectral Bands 384 220
Digitization 12 12
Frame Rate (Hz) 237 225
Typical SNR 100 - 200 65 - 130
Spectral Calibration (nm) 1 1
Radiometric Calibration <6% <6%
Hyperion Calibration
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Hyperion CalibrationGround Test and On-Orbit Validation
• System performance assessment strategy
• Present pre-flight and on-orbit measurement techniques
– Absolute Radiometric Calibration
– Spectral Calibration
– Image Quality Characterization
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Strategy
• Pre-Flight:
– Establish fundamental characteristics of the instrument and
assess requirement compliance
– Establish instrument performance through the build,environmental test and spacecraft integration phases
– Provide solid foundation for on-orbit comparison
• On-orbit:
– Determine on-orbit performance and compare with pre-flight
performance
– Define data collects that can be used to assess identified performance parameters
– Acquire and analyze data collections; and assess accuracy of
technique
– Compare on-orbit results with pre-flight
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Special Targets for Characterization
Searchlights
-California
Planets
-Venus
90 deg
Yaw
Gas Flares
-Moomba
D t Sit d f Vi i C lib ti
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Desert Sites used for Vicarious Calibration
Lake Frome RR Valley Arizaro/Barreal Blanco
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the good
the bad
and
the gotchas
The world of real life operations
Images of Lake Frome
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Images of Lake Frome
The Salt Surface
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The Salt Surface
Impact of Moisture on Salt Signature
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Lake Frome SamplesLab Spectra - Dry with increasing amounts of water
0
0.2
0.4
0.6
0.8
1
1.2
1.4
400 600 800 1000 1200 1400 1600 1800 2000 2200 2400
Wavelength (nm)
R e l a t i v e R e f l e c t a n c e
dry 10ml 15ml 20ml 25ml saturated
Dry
Increasing Water
Content
Approximate lab analysis indicates
changes of spectral signature
based on amount of water added
Impact of Moisture on Salt Signature
The Locusts That Did Not Make It Over
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The Locusts That Did Not Make It Over
Calibration signature
Or
New culinary delicacy?
Arizaro Dry Lake bed
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Arizaro Dry Lake bed
Ground Calibration
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Ground Calibration
Coleambally Collection History
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Coleambally Collection History
Date(GMT)
Julian
Day(GMT)
Path/
Row
X-track
Position
In-track
Position Comments
23-Dec-00 358 93/84 100 3148 Cum(South);VNIR
1-Jan-01 001 92/84 82 2131 Clear
9-Jan-01 009 93/84 - - UARDRY
17-Jan-01 017 92/84 - - Cloudy
25-Jan-01 025 93/84 95** 2481** High Clouds(North)
2-Feb-01 034 92/84 36 2599 Clear
9-Feb-01 041 93/84 - - Cloudy
18-Feb-01 049 92/84 41 3782 Shadows(East)
25-Feb-01 056 93/84 - -
6-Mar-01 065 92/84 95 3977 Clear
13-Mar-01 072 93/84 104 2926 Clear
22-Mar-01 082 92/84 - - Cloudy
ARIZARO, Argentina
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g
High altitude (~12,000ft) dry salt
lakebed.
Surface: extremely rough, locally
uniform, and bright.
Some years no rain at all!
It rained the day we arrived
The good news is that conditions
were good for the experiment on the
7th of February 2001.
Calibration & Validation Can Be Fun
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Calibration & Validation Can Be Fun
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Let’s Address Calibration
System Performance Verification Strategy
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y gy
VNIR/SWIR
Spatial
Coregistration
GSD MTF
Geometric
Bandwidth
Center
Wavelength
Spectral
Vicarious
Calibration
Repeatability
Applying
Calibration
Solar
Cal.
Artifact
Removal
Dark
Removal
Defining
Calibration
Absolute
Radiometry
PerformanceVerification
(end-to-end)
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Absolute Radiometric Calibration
Key Factors Impacting Calibration
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y p g
No intermediate
properties.
Constant
Spacecraft scans
moon. Relative
moon, sun, sat
angle
noneBased on Lunar
models
Lunar
Calibration
User oriented
effort
Depends on
surface
Atmospheric
effects must be
modeled
Based on ground
truth
measurements
Lake Frome
(vicarious)
Uniform acrossfield-of-view
Constant
Critical tomodeling
intermediate
properties
Diffuse reflectanceof Hyperion cover
Models avail tocommunity
VNIR more
accurate then
SWIR
SolarCalibration
StrengthsSpacecraft
Pointing
Intermediate
Properties
Absolute
Knowledge
On-Orbit Radiometric Calibration
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• Solar Calibration
– Absolute Comparison: VNIR within 2%, SWIR 5-8% low; SWIR has
larger uncertainty due to solar model and BRDF model of cover surface
– Used to correct for pixel-to-pixel corrections
– Included in repeatability assessment 0.6% for VNIR, 1.6% for SWIR
– Used to define noise level as a function of signal level to determinedSNR
• Lunar Calibration
– Used to eliminate artifacts of diffuser
• Vicarious Calibration and Cross Calibration
– Calibration accuracy in 4-10% range
Solar Calibration Trending
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History of VNIR Response to Solar Calibration
0.95
0.96
0.97
0.98
0.99
1
1.01
1.02
1.03
1.04
1.05
40 60 80 100 120 140 160
Solar Cal Day of Year 2001
R a t i o t o S o l a r - 0 4 7
, W i t h D i s t . C o r r e c t i o
n
550 nm 651 nm 753 nm 855 nm
History of SWIR Response to Solar Calibration
0.95
0.96
0.97
0.98
0.99
1
1.01
1.02
1.03
1.04
1.05
40 60 80 100 120 140 160
Solar Cal Day of Year 2001
R a t i o t o S o l a r - 0 4 7 , W i t h
D i s t . C o r r e c t i o n
1044 nm 1346 nm 1649 nm 2153 nm
Lunar Calibration Trending
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Day 038 Day 069 Day 097 Day 128 Day 156
Ratio of Profiles to Lunar 128 for the Different Collects
0.9
1
1.1
1.2
1.3
1.4
1.5
400 900 1400 1900 2400
Wavelength (nm)
R
a t i o
Lunar 38 Lunar 69 Lunar 128 Lunar 97 Lunar 156
Intensity Dependent on
Moon-Sun-Spacecraft
Relative Angles
VNIR and SWIR track
intensity changes
Description of the 90 Degree Yaw Collect
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Gr o un d T r a c k
1 2256
3 4
123
4
256
4
3
2
1
T=0; Frame#1
Frame#256
4
3
2
1
4
3
2
1
T=0;
Frame#1
Spacecraft rotates such
that the ifov direction is
parallel with the flight path
-90deg
The target spot is
viewed by pixel 256
during frame 1, and
then by pixel 255
during frame 2, and
so on…
255
2 5 5
Frame 1Frame 2
2 5 6
IFOV
Analysis for Yaw Data
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Level 1R Transposed 45 Deg FlatfieldedFlatfielded(using 2001197 coefficients)
Differences between the sets of correction
coefficients for the entire vnir focal plane are
within ±2.5%
Hyperion Band 20 (VNIR)
Analysis for Yaw Data
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Transposed 45 Deg FlatfieldedFlatfielded(using 2001197 coefficients)
Hyperion Band 91 (SWIR)
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Spectral Calibration
Pre-Flight Spectral Calibration
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Pre Flight Spectral Calibration
Spectral response modeled as agaussian with a center
wavelength and full width half
max
Monochrometer profiles usedto define center wavelength
and bandwidth at discrete
locations
Pre-Flight Spectral Calibration
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g p
• Center wavelength and bandwidth
– Measured at discrete locations 20 VNIR locations, 25 SWIR locations.
– Used to define the center wavelength and bandwidth for every VNIR andSWIR pixel, 256 field-of-view locations and 242 spectral bands.
• Dispersion (nm/pixel) – Spacing of spectral channels, Hyperion dispersion (~10nm/pixel) closely matches
the bandwidth (10 nm)
• Cross-track spectral difference – Maximum wavelength difference across field-of-view for a single spectral
channel,
– VNIR = 2.6-3.6 nm – SWIR = 0.40- 0.97 nm.
Pre-Flight Calibration Algorithm Development
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620.000
0.100
0.200
0.300
0.4000.500
0.600
300 500 700 900 1100
VNIR Measurement
Model calculation (LLS fit with 4 parameters)
R e l a t i v e S e n s o r R e s p o n s e
Reflectance Spectra of Doped Spectralon
S p e c t r a l o n R e f l e c t i v i t y
0.4
0.6
0.8
1.0
1.2
1.4
1.6
200 400 600 800 1000 1200 1400 1600 1800 2000Wavelength (nm)
Erbium (offset by 0.5)
Holmium
Wavelength (nm)
High resolution scans of Holmium and Erbium Oxide doped Spectralon
Process:1. Image contains reference
spectra uniform across the
field of view. (pre-flight:
doped spectralon)
2. High resolution reference
spectra convolved with
sensor spectral response
function
3. Resulting reference spectraaligned with Hyperion
measured spectra to
determine spectral
calibration.
On-Orbit Spectral Calibration
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Early solar cal, sun is rising through earth’slimb during collect.
View sun, off cover, through atmosphere
Extension of Pre-flight algorithm development
Spectral Calibration
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800 1000 1200 1400 1600 1800 2000 2200 2400 260010
-1
10 0
10
1
10 2
10 3
Identification of Spectral Features
Wavelength (nm)
S
C
AL
E
D
S
PE
C
T
R
UM
Hyperion Spectra – red
Atmospheric Reference – black
Diffuse Reflectance of cover – blue
Process:
1.) Create Pseudo-Hyperion Spectra
from reference: Modtran-3 for
atmosphere, and Cary 5 & FTS
measurements for diffuse
reflectance of the cover
2.) Correlate Spectral Features:
band number units of Hyperionmax/min correlated with reference
wavelength of max/min
3.) Calculate Band to Wavelength
map: apply low order polynomial
to fit the data over the entire SWIR regime
SWIR
VNIR Spectral calibration based on two lines:
one solar line (520 nm) and an oxygen line (762.5nm)
Spectral-Spatial-Uniformity
Cross Track Sample
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W a v e l e n
g t h
Requirement Failure by Frown(aka smile)
Depiction- Grids are the detectors
- Spots are the IFOV centers
- Colors are the wavelengths
Failure by Twist Failure by Spectral-IFOV-Shift
VNIR Spectral Variation Across the Field of View
Characteristics of Spectral Calibration
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SWIR Spectral Variation Across the Field of View
-3.5
-3.0
-2.5
-2.0
-1.5
-1.0
-0.5
0.0
0.5
0 50 100 150 200 250
Pixel Field of View
W
a v e l e n g t h R e l a t i v e t o
F O V 1 2 8 ( n m )
SWIR Band 75: 892.35 nm SWIR Band 150: 1648.96 nm SWIR Band 225: 2405.63 nm
-3.5
-3.0
-2.5
-2.0
-1.5
-1.0
-0.5
0.0
0.5
0 50 100 150 200 250
Pixel Field of View
W a v e l e n g t h R e l a t i v e t o F O V 1 2 8 ( n m )
VNIR Band 10: 447.892 VNIR Band 30: 651.278 VNIR Band 55: 905.511
2.6 to 3.6 nm
.40 to .97 nm
Understanding Frown
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• Why is this important if it is only a few nm? – Some spectral effects such as movement of the chlorophyll edge
due to stress is of the same order
– Reproducibility of results due to variations in pointing
The Oxygen A Bands at 1 cm-1 & 1 nm Steps
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Different Answers – Continuum?
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Oxygen Line from Lake Frome Image
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Image Quality
Image QualityCape Canaveral
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p
Jan. 13 2001• Modulation Transfer Function
– Pre-flight used knife edge and slit to measure Cross track
direction, Along-track was Cross-Track*2/pi
– On-orbit used Ice Shelf & Lunar Limb (knife edge) and bridge (slit) to measure Cross-Track and Along-Track
directly.
• Co-registration of VNIR and SWIR
– Pre-flight used test bed to project a slit with a broadspectrum at multiple locations
– On-orbit used combination of edges (Lunar, Ross), point
sources (clouds, flares), ground control points
• Ground Sample Distance – Pre-flight measured IFOV using test bed
– On-orbit triangulated marked features in well mapped scene
MTF Approach
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• Calculate cross-track and in-track MTF using a stepresponse and impulse response example
• Results of on-orbit analysis give good agreement with the
pre-launch laboratory measurements
VNIR Imaging of “Starburst Pattern”
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Pattern used to validate
optics performance
and level 1 processing
Level 0 data Level 1 data
MTF Example: Cross-track Bridge
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Dec 24, 2000. Bridge is the Mid-bay bridge near Destin, Florida.
Bridge width (13.02 m) acquired and utilized in the MTF processing.
Bridge angle small, every 5th line used to develop high resolution bridge image.MTF result at Nyquist is between 0.39 to 0.42; pre-flight measurement was 0.42.
-10 -5 0 5 100
50
100
150
200
250
Interlaced bridge images and curve-fit from band 30
line 220line 225line 230line 235line 240Curve-Fit
Cross-track pixel
1 0 * R a d i a n c e
Image Quality Example
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Vertical and Horizontal MTF can be calculated from diagonal edge
MTF Calculation Process Summary
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• MTF measurement on-orbit requires collection of scenes
– step response: Ross Ice Shelf, Lunar Collect
– impulse response: Bridge (Eglin, Cape Canaveral)
• Data Processing steps (simplified):
1.) Define Edge Spread Function (ESF): Interlace adjacent lines from an
object that is at a slight angle to the spacecraft motion
- allows over sampling (sub-pixel) sampling
2.) Calculate Line Spread Function (LSF):
- edge technique: calculate an error function curve-fit to the ESF to derive the
LSF, OR take the band-limited derivative of the ESF with a Tukey window.
- slit technique: LSF is obtained from interlacing adjacent lines and de-
convolving the profile with the slit width (in the frequency domain)
3.) MTF is the Fourier Transform of the LSF
What’s Next
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• We have looked at instrument characteristics and calibration
• Now what do we do to “correct” the data?
– Radiometric
– Geometric – Atmospheric
– Uniformity (striping)
– Other nuances
• How well can all this work?
Hyperion Data Flow
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Science Data: Level 0 or
Level 1 (radiometrically
corrected) data products
with VNIR and SWIR data
frames combined.
Includes solar, lunar
calibrations, earth
images, dark and light
calibrations
Metadata: Data about thescience data. Information
to support higher level
processing, e.g., pre-flight
characterization data
Ancillary Data:
Supporting data derived
from spacecraft telemetry
during image collection
Ancillary data in
engineering units
Final product:level 1 data,
metadata file
attached
L0 Science data
Level 1
Science data
L0 Proc.
METADATA
Level 1 Data Processing Flow
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Background removal:
>Subtract interpolated dark
file from echo and smear
corrected image file
Echo removal
> Remove echo from smear
corrected files
> Both dark and image files
> Log file generated (MD8)
Average dark files
> Average frames of echo
and smear corrected pre-
and post-image dark files.
Provide averaged pre-
image (MD3A) and post-
image (MD3B) dark files> Compute average value
of corrected pre- and post-
image dark files. Log file
generated reporting
average values (MD5A for
pre-image and 5B for post-
image)
Smear correction
> Apply smear correction to SWIR data
> Both dark and image files
> Log file generated (MD9)
.L1_A
data
Step 0
Step 1
Step 2
Apply calibration:
> Multiply dark subtracted
image by lab gain file (MD7) to
obtain radiometrically corrected
(Level 1) data
>Multiply VNIR radiance by 40
>Multiply SWIR radiance by 80
>Log file generated (MD2)
Fixstripes :
> Repair known bad pixels and
output level 1_A file in signed
integer format (.L1_A)
> Log file generated (MD10)
Step 6
Step 5
Step 4
Step 3
Flag pixels >4095:
> Pre- & post-image darks> Image
> Output to log file (MD15)
Dark cal interpolation:
> Linearly interpolate
between preimgdk .avg (MD3A)
and postimgdk .avg files (MD3B)
QA .L1_A image:
> Display image in ENVI
> Complete QA form during
evaluation (MD11)
> Add center wavelengths to
ENVI header (. hdr ) fi le (MD12)
Step 7
L0 data
Hyperion Processing: One Step Further
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User Receive
‘L1A’
VNIR bands 1:70
divide by 40.0
SWIR bands 71:242
divide by 80.0
Untar tapePerform Quick
Checks and Subset
the Data
L1A subset
Return to Absolute
Radiance, float
Align VNIR to SWIR
Using swir_to_vnir.pts
Or -1 cross track
And 0-1 along track 0-1
Recombine Datafiles
‘L1P’
Subset data file, absoluteradiance in float, SWIR
aligned to VNIR, in a
single file SWIR aligned to
VNIR, absolute
radiance, float
Data Processing Levels
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Level 2G:
Geometric and
VNIR/SWIR merge
Level 1A:
delivered data
Level 1P:
Radiometric and
VNIR/SWIR overlay
Data Evolution - Processing L1A –> L2G
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Level 1A
Level 1P
Level 2G
Level 1A:
delivered data
Level 1P:
Radiometric andVNIR/SWIR overlay
Level 2G: Geometric
and VNIR/SWIR mergeVNIR/SWIR merge:
Bands 9-55 & 77-220
Geo-Correction: L1A Æ L2G
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Methods / Geo-correction
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A time series of polynomials allowing for geo-correction were developed by
simultaneously fitting Ground Control Points (GCPs) collected in multiple
images (date and sensor) using the MOSMOD module of microBRIAN.
These polynomials allow the images to be resampled to geographiccoordinates.
Fifty GCPs were collected between the base Aerial photography, an ETM
image acquired 02-January-2001 (ETMjan), Hyperion VNIR, acquired 02-January-2001, 03-February-2001, 07-March-2001 (HypVNIRjan,
HypVNIRfeb, HypVNIRmar), and Hyperion SWIR for those same dates
(HypSWIRjan, HypSWIRfeb, HypSWIRmar).
MOSMOD both optimizes the images to geographic coordinates and the
images internal registration.
GIS Data
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High resolution (2m) digital aerialphotographs acquired January 2001
used for creating positionally accurate
field and rice bay GIS datasets.
Over 466km of linear road network
and 129 well-defined points digitisedwith a Differential Global Positioning
System (DGPS). These datasets can
be used for geo-referencing Hyperion
time series
Coleambally Geo - Correction
Si H i ‘i ’ (VNIR d SWIR f 02 J 03 F b
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• Six Hyperion ‘images’ (VNIR and SWIR for 02 Jan; 03 Feb;and 07 Mar 2001); GCPs collected in each Hyperion ‘image’
and 2m air photos
• Prior to fitting, outlier GCPs were identified and redefined; a
linear polynomial was selected and a transitive chain method
(MOSMOD) was used
Pixel Size
Avg X = 30.77m; Avg RMS error = 2.52 m
Avg Y = 30.49m; Avg RMS error = 6.87 mRegistration
Cross-track 12.9m (SD 0.6m)
Along-track 11.6m (SD 2.1m)
Presented at IGARSS 01
Preliminary Results / Geo-correction
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Aligning the VNIR and SWIR
VNIR d SWIR i d d t t t
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• VNIR and SWIR are independent spectrometers
and focal plane arrays
• For the user to align the SWIR to the VNIR apply- constant cross track shift of –1 pixel
- linear along track shift of 0 pixels at field-of-view 0 and 1 pixel at
field of view 256.
VNIR SWIR
SWIR
VNIR
Reflectance ProcessingHyperion Band 223 ( 2385 nm )
Input Radiance Image
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2 . A t m
o s p h
e r i c C o r r e
c t i o n
( F L A A
S H - b a
s e d )
3. MNF Smoothing
Output Reflectance Image
1. De-striping
Should Atmospheric Correction be Performed?
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• Hyperspectral remote sensing has effects of atmosphere aswell as earth materials
• The result is easily recognized and its worst effects can beavoided
• Should you atmospherically correct? – Some applications do not need it (classification, MNF, CVA,
exploration)
– Some can benefit from it (standardization) – Some need it (modeling)
• The data are there to use – do not be put off nor wait until
all the “problems” have been solved!
A Simple Atmospheric Model
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1
1
:
:
t t g p
t
t t
t t
L E T L s
odelling L
Atmospheric Correction L
ρ
π ρ
ρ
ρ
= +−
→
→
Irradiance
Transmittance
Path Radiance
Atmosphere
Reflectance
Ground Reflectance
At-Sensor
Radiance
Not so frightening after all?! (DLBJ)
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Chlorophyll
Liquid Water
Clay
Red Edge
Atmospheric Correction Codes
Hatch FLAASH ACORN
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Hatch, FLAASH, ACORN
Atmospheric Correction Codes
Hatch FLAASH ACORN
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Hatch, FLAASH, ACORN
FLAASH – ASD Comparison
Soil1 Means
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Soil1 Means
0.00
0.05
0.10
0.15
0.20
0.25
0.30
350.00 850.00 1350.00 1850.00 2350.00
Wavelength (nm)
R e f l e c t a n c e
Soil1.mn
f_Soil1.mn Stubble1 Means
0.00
0.05
0.10
0.15
0.20
0.25
0.30
0.35
0.40
0.45
0.50
350.00 850.00 1350.00 1850.00 2350.00
Wavelength (nm)
R e f l e c t a n c e
Stubb1.mn
f_Stubb1.mn
Soil
Stubble
Atmospheric Avoidance
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“Stable” set of bands can be considered if application permits
Bands Wavelength (nm)
10-57 448-92681-97 953-1114
101-119 1155-1336
134-164 1488-1790
182-221 1972-2365
Yerington, NV: pixel 62, 35
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990.4 0.9 1.4 1.9 2.4Wavelength (µm)
R e f l e
c t a n c e
HATCHATREM
.
10
12
14
16
n m / s r )
Cirrus Cloud Detection Over Mojave Desert
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100
0
2
4
6
8
400 700 1000 1300 1600 1900 2200 2500
Wavelength (nm)
R a d i a n c e ( µ W / c m ^ 2 / n
0 m altitude
3000 m altitude
10000 m altitude
Visible Image Image from 1380 nm
Accounting For Water Vapor
18 0
20.0
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101
0.0
2.0
4.0
6.0
8.0
10.0
12.0
14.0
16.0
18.0
400.0 700.0 1000.0 1300.0 1600.0 1900.0 2200.0 2500.0
Wavelength (nm)
R a d i a n c
e ( µ W / c m ^ 2 / n m / s r )
0.00 mm PW
0.10 mm PW
1.00 mm PW
10.0 mm PW
50.0 mm PW
ModeledMeasured
-1
0
1
2
3
4
5
6
7
8
9
1 0
8 40 86 0 8 80 900 92 0 9 40 9 60 9 8 0 1 000 1 02 0 1 0 40 1 06 0
W aveleng th (nm )
R a d i a n c e ( µ W / c m
2 / n m / s r )
M easu red
M odele d
R e s id u a l
15 .92 p rec ip i tab le mm
Water Vapor Parameter map
Yerington, NV: Water Vapor Images
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ATREM HATCH
0.68 cm 1.45 cm 0.60 cm 1.10 cm
Stripes in the Image
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• Stripes may not
impact analyses
• Three spatial
frequency scales
occur:
•High
•Medium
•Low
• Is there a
temporal effect?
RGB (42, 21,15)
Approaches for De-Striping
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1. Set a column mean (intensity) equal to the global mean
2. Set the column mean and standard deviation equal to theglobal mean and standard deviation
3. Use a locally-based mean and standard deviation in spatialdimensions
4. Use a locally-based mean and standard deviation in spatialand spectral dimensions
Effects of ‘Global’ and ‘Local’ De-Striping
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•Typical spatial scales are pixel, surface features and swath
•‘Global’ removes streaks and low-frequency effects (but‘Global’ can bias the results) – it depends on the surface
cover characteristics along the column
•‘Local’ removes spikes – especially in the VNIR but leaves
in the low frequency effects such as the smile radiance effect.
•VNIR and SWIR noise are different. Should be treated
separately.
Striping
VNIR SWIR
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De-streaking Approach
( , )ij ijm s are column averages for sample i band j
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( , )
( , )
ij ij
ij ij
ijk ij ijk ij
ij
ij ij ij ij ij
ij
m s are column averages for sample i band j
m s are reference values for them
x x sample i band j line k
sm m
s
α β
α β α
→ +
= = −
Selection of reference values determines whether
the de-striping is local or global
Global De-striping MNF1 and 15
Original Data De-Streaked Data
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MNF 1 Radiance MNF 15 Radiance MNF 15 RadianceMNF 1 Radiance
Effects of ‘Global’ and ‘Local’ De-Striping
Change Analysis•Global De-streaked minus Original Radiance Local De-streaked minus Original Radiance
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Global De streaked minus Original Radiance Local De streaked minus Original Radiance
•MNF Band 1 MNF Band 5 MNF Band 1 MNF Band 5
•Global De-streaking removes ‘smile’ effect and streaks Local de-streaking removes stripes but leaves
•But also alters mid frequency spatial effects in the data the ‘smile’ effect in
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Other Thoughts
Other Effects - Directional Illumination
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111Midday
Early Morning
Through the Glass Darkly – or “no free lunch”
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• Hyperspectral data is fighting for photons• Small pixels & many narrow bands lead to lower SNR
• The technology is good but there is a limit
• Landsat FWHM ranges from 70 in VNIR to 300-400 in the
SWIR
• Hyperion Bands have an FWHM and step of 10 nm
Sensor Characteristics
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SAC-C
CWL
(nm)
FWHM
(nm) ETM
CWL
(nm)
FWHM
(nm) ALI CWL (nm)
FWHM
(nm)
MODIS
Land
CWL
(nm)
FWHM
(nm)
Band 1p 441.6 18.9
Band 1 490.5 19.3 Band 1 478.7 67.2 Band 1p 484.8 52.6 Band 3 465.7 17.6
Band 2 548.8 19.2 Band 2 561.0 77.6 Band 2 567.2 69.7 Band 4 553.7 19.7
Band 3 656.6 58.0 Band 3 661.4 60.0 Band 3 660.0 55.8 Band 1 646.3 41.9
Band 4 812.9 34.2 Band 4 834.6 120.8 Band 4 790.0 31.0 Band 2 856.5 39.2
Band 4p 865.6 44.0
Band 5p 1244.4 88.1 Band 5 1242.3 24.7
Band 5 1598.5 106.6 Band 5 1650.2 190.5 Band 5 1640.1 171.2 Band 6 1629.4 29.7
Band 7 2208.1 251.3 Band 7 2225.7 272.5 Band 7 2114.2 52.2
Pan 719.9 319.5 Pan 591.6 144.3
SAC-C ETM+ ALI MODIS
SAC-C VNIR Bands vs ALI, ETM+
ALI ETM
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ALI ETM+
Red: SAC-C
Blue: Other Instrument
Binned Hyperion vs SAC-C
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Hyperspectral ImagingApplications & Benefits
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Existing Satellite
Capabilities (SPOT,
Application LandSat) Hyperion Capability Perceived Benefits
Mining/Geology Land cover classification Detailed mineral Accurate remote mineral
mapping exploration
Forestry Land cover classification Species ID Forest health/infestations
Detail stand mapping Forest productivity/yieldFoliar chemistry analysis
Tree stress Forest inventory/harvest
planning
Agriculture Land cover classification Crop differentiation Yield prediction/commodities
Limited crop mapping Crop stress crop health/vigor Soil mapping
Environmental Resource meeting Chemical/mineral Contaminant Mapping
Management Land use monitoring mapping & analysis Vegetation Stress
Hyperspectral Image Provides Forestry Detail
LandSat Analysis Hyperspectral Analysis
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Open field
No Data
Red Maple
Red Oak
MixedHardwood
Legend
Hardwood/Conifer Mix
White Pine
Hemlock/
Hardwood Mix
Mixed Conifer
Norway Spruce
Red Pine
Spruce Swamp
Hardwood Bog
Analysis by Mary Martin University of New Hampshire
N o D a t a
H a r d w o o d
S o f t w o o d
G r a s s / F i e ld s
L e g e n d
Hyperspectral Image Provides Geological Data
GEOTHERMAL AREA(no specific mineral information)
CALCITE(gold bearing quartz)
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Red: Hydro-thermal alterationMULTISPECTRAL ANALYSIS
Red: Calcite
Blue and Green: MuscoviteHYPERSPECTRAL ANALYSIS Analysis courtesy AIG Limited Liability Company
Analysis of Red Edge Shift- sample process
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Now, Let’s Classify the Data!
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Pixel-Based Supervised Classification Methods
for Hyperspectral Data
Important Issues for Classification of Hyperspectral
Data
• Impact of noise and calibration
L SNR h di l i l d
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– Lower SNR than corresponding multispectral data
– Striping in data acquired by pushbroom sensors
– Data susceptible to atmospheric artifacts and sensor anomalies
• Large input space
– Enormous number of parameters to estimate – Sparse data in hyperspectral domain
– Adjacent bands are often highly correlated
– Quantity of training data typically limited
• Potentially large output space
– Possible overlapping spectral signatures
Approaches to Resolve Issues
• High dimensional input spaces
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– Simplify or reduce dimension of input space via extraction
• Subset feature selection
• Projection of original features on a lower dimensional space
– Regularize covariance matrix of input space
– Utilize unlabelled samples via semi-supervised learning
– Develop ensembles of classifiers
• High dimensional output spaces
– Output space decomposition
• Utilization of nonparametric classifiers
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Dealing with High Dimensional Input Spaces
Feature Selection/Extraction
G l
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• Goals
– Reduce no. observations required to train the classifier
– Reduce computational complexity
– Improve classification accuracy
• Desired Properties of Extractors – Class dependent
– Exploit band ordering
– Transformations should maximize discrimination among classes
• Selection vs extraction
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• Selection vs extraction
– Selection of subset of original features
• Less redundancy, but potentially some information loss
• Better interpretability (domain knowledge)
• Possibly improved generalization
– Extraction
• Typically computationally superior to selection
• No loss of information as all features retained
• Resultant features unrelated to physical phenomena
Approaches to Feature Extraction
• Principal Component Transform [Anderson (1984)] – Orthogonal linear combinations of the p original bands with maximumvariance. At pixel x,
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p ,
– Formulated as an optimization problem
– Solution is a subset of successively ordered eigenvalues of thecovariance matrix Σ; are corresponding eigenvectors
( ) ( )
subject to
1, 1,....
0 , 1,...
t
i i
t
i i
t t
i j
Max a a
a a i p
E a x x a i j j i
= =
= ∀ ≠ =
Σ
Z Ζ
( ) ( ) 1t
i x Z x , i ,...p= = Z
*
ia
– Characteristics of Principal Component Transform
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Characteristics of Principal Component Transform
• Developed to maintain variance of data in smaller set of orthogonal inputs
• Order of components not necessarily related to data quality• Components have no physical relationship to the targets
• PCT data are image content dependent (poor generalization)
• Does not exploit the adjacency of correlated bands
• Maximum variability has no inherent relationship to criterion for
classification
• Sensitive to outliers
Principal Components of AVIRIS Data
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• Segmented Principal Component Transformations [Jia andRichards (1999)]
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– Detect boundaries in correlation matrix,
compute PCT’s and select subset. Further prune using Bhattacharya distance
– Characteristics of SPCT
• Restricted to two-class problems• Utilizes adjacent band correlation structure
• Not related to discrimination measures
• Sensitive to outliers
• Maximum Noise Fraction Transform [Green, Berman, Switzer,
and Craig (1988)]
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g ( )]
– Uncorrelated linear combinations of bands that maximize “noise
fraction” for each band.
– Define “noise fraction” at each pixel x
( ) ( ) ( ) ( )1
where
known
t
i
Z S N Z N
x Z x , i ,...p x x
,
Ν
Σ Σ Σ Σ Σ
= = = +
= +
Z S
( ) ( ) ( ) 1i i i NF x Var N x Var Z x , i ,...p= =
– Formulated as an optimization problem similar to PCT, except that
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orthogonal linear combinations are sought to maximize the Noise
Fraction
– Optimal weights:
( )
( ) ( )
subject to
1 1,...
0 , 1,...
t t
i i N i i Z i
t
i Z i
t t
i j
Max NF x a a a a
a a i p
E a Z x Z x a i j j i
= Σ Σ
Σ = =
= ∀ ≠ =
* 1eignvectors of i N
a −= Σ Σ
– Characteristics of MNF Transform
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• Developed to filter noisy bands, then transform data back to original domain
• Ordering related to quality of tranformed data• Invariant under data rescaling
• Does not exploit the adjacency of correlated bands or class specific
information
• Resultant bands of transformed data may be related subjectively to phenomena, but are not related to discrimination between classes
MNF Bands for AVIRIS Data
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• Fisher’s Linear Discriminant [Anderson (1984)]
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– Seeks projection matrix A* to maximize separation between classes
, 1,..i i C ω Ω = =
( )( ) ( )( )
where
generalized eigenvectors of (dim 1)
t
t
t t
x X
Max
X x x
C
ω
ω ω ω ω ω
ω ω
µ µ µ µ µ µ ∈Ω ∈Ω ∈
= − − = − −
= ≤ −
∑ ∑ ∑-1
A BA
A WA
B W
A* W B
– Characteristics of Fisher’s Linear Discriminant
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• Requires a trained classifier
• Poor discrimination well between classes with similar means• Classes with “different” variance dominate the combination
• Ignores band ordering
• Problematic for hyperspectral data with small training sample due to
required matrix inversion
• Decision Boundary Feature Extraction [Lee and Landgrebe(1997)]– Computes a decision boundary that determines the projection of the
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Computes a decision boundary that determines the projection of thehyperspectral space for a two-class problem
• C -class problem solved using wtd sum of 2-class decision boundaryfeature matrices
– Requires trained classifier
– Ignores band ordering
• Projection Pursuit [Jimenez and Landgrebe (1999)] – Compute projection for two classes based on Bhattacharya distance
– Subsets constrained to be smaller groups of adjacent bands – One set of features selected for all classes
– C-class problem uses sum of discriminatory power for pairs(problematic for large no. of classes)
• Best Bases Band Aggregation [Kumar et al. (2002); Morgan etal. (2004)]
Adj b d d b d l i ( d f
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– Adjacent bands merged based on correlation measure (or product of
correlation and Fisher discriminant) – Implemented in Top-Down and Bottom-Up approaches
– Characteristics
• Exploits band adjacent correlations, maintains unique individual bands
• Features are class specific
• Affects signal to noise ratio in a non-uniform way
• Mitigates impact of small training sample problem
• Polyline Feature Extraction [Henneguelle et al. (2003)] – Approximate mean spectrum via piece-wise polynomials (usually linear).
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– Select break points via top-down or bottom-up methods and determine parameter values (Linear: slope, midpoint of segment)
– Parameters of piece-wise functions become input features to classifier
– Characteristics
E l it b d dj
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• Exploits band adjacency
• Features are class dependent• Computationally efficient
• Provides approximation to derivatives
• Features robust for generalization
• Sensitive to stopping criterion (no. of bands vs error in
approximation)
• Discrete Fourier Transform
D ib b d i t i t i f ti
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– Describes band sequence via trigonometric functions
– Subset of modes selected to represent features – Characteristics
• Multiscale representation of spectral signature
• Difficult to determine optimal modes to represent original problem
• Computation unrelated to class discrimination
• Modes may contain domain knowledge
• Discrete Wavelet Transform [Bruce et al. (2001, 2002);
Kaewpijit et al (2003)]
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Kaewpijit et al. (2003)]
– Multiscale method that accounts for both frequency and position – Describes band sequence via discrete wavelet transform
– Implemented in conjunction with a selection method
– Characteristics• Provides robust features
• No. of inputs must be a power of 2
– Accomplished via padding or interpolation
• Criterion unrelated to class discrimination
Approaches to Feature Selection
• Selection techniques can be implemented individually, or inconjunction with extraction methods
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j
• Methods that guarantee optimal solutions – Exhaustive search
– Branch & bound (only if objective function is monotonic)
• Heuristic methods – Characteristics
• Typically produce sub-optimal solutions• Developed to reduce computational complexity
• Traditionally implemented via sequential forward selection or backwardelimination
Heuristic Feature Selection Methods
• Sequential Floating Search Methods [Pudil et al. (1994);Serpico and Bruzzone (2001)]
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– Evaluate a number of features for elimination/forward selection for each feature (group) added/eliminated
• Tabu Search [(Zhang and Sun (2002); Korycinski et al.
(2003)] – Developed for solution of combinatorial optimization problems
– Non-greedy heuristic that adaptively and reactively adjusts searchspace, flexible in number of features evaluated at each iteration
• Various simulated annealing and genetic algorithms [Siedleckiand Sklansky (1988, 1989)]
Regularization of Covariance Matrix
• Methods to regularize estimated covariance matrix
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– Pseudo-inverse [Fukanaga (1990); Skurichina and Duin (1996);
Raudys and Duin (1998)]
• Poor performance when ratio of amount of training data to input
dimension is small
• Exhibits “peaking” effect at
– Shrinking and pooling covariance matrices [Tadjudin and Landgrebe
(1999)]
• Reduce variance of estimates, but introduce bias
2 X d =
Incorporation of Unlabeled Samples
• Semi-supervised learning
A gment e isting training data ith nlabeled samples [Jeon and
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– Augment existing training data with unlabeled samples [Jeon and
Landgrebe (1999); Tadjudin and Landgrebe (2000); Jackson andLandgrebe (2001)]
• Classify unlabeled observations and update estimates, usually via EM
algorithm
• Alternatively perturb sample data
– Effective for generalization and knowledge re-use
– Sensitive to original training samples, impacted by outliers
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Dealing with Large Output Spaces
Multiclassifier Systems
• Multiclassifier Systems – Ensembles of base classifiers that are learned individually to produce anaggregated predictor
– CharacteristicsC l ifi f k
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• Component classifiers often weak
• Decision boundaries for individual classifiers typically simple• Generalization typically superior, if classifiers are diverse
• Goals – Improve classification accuracy and generalization
– Reduce complexity and improve interpretation – Enhance transferability of classifiers to new problems or beyond spatial
extent of training data – Mitigate the impact of sensor artifacts
– Improved utilization of small quantity of training data• Challenges
– Achieving highly diverse, but relevant classifiers – Maintaining interpretability
Multiclassifiers from Input Space Decomposition
• Selecting sub-samples of original data, creating classifiers,and developing a classifier for each sample. Combine
lt f i di id l l ifi
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results from individual classifiers – Perform poorly for extremely small sample sizes as ensemble
methods cannot overcome lack of diversity
• Bagging [Brieman (1996)]
– Create multiple samples with replacement; develop individualclassifiers
• Arcing – Adaptively reweighting and combining (e.g. boosting) [Freund and
Schapire (1996); Dietterich (2000)]
– Simple random sub-sampling [Ho (1998); Skurichina and Duin(2002)] (See previous slides for details)
• Random subspace selection methods [Ho (1998); Skurichinaand Duin (2002)] – Utilized in conjunction with multiclassifier systems
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• Randomly sample input features for each classifier • Combine outputs of multiple classifiers via voting or maximizingaposteriori probabilities
– Characteristics
• Provides diversity and robust classifiers with good generalization
• Reduces band redundancy, but gnores band adjacency, unless implementedwith band aggregation (which reduces diversity)
• Mitigates impact of small sample size problems and stabilizes parameter estimates
• Computationally expensive
• Does not provide domain knowledge on feature characteristics
• Mitigates the impact of anomalous sensor behavior (striping)
Multiclassifier Systems: Output SpaceDecomposition
• C one-vs-rest problems [Anand et al. (1995)] – Classify individual classes vs the mixture of the “rest” – Can result unbalanced priors for Bayesian methods if sample sizes
for some classes extremely small
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for some classes extremely small
– Does not exploit class affinity, so convergence can be very slow
• Pairwise classifiers [Crawford et al.(1999);
Kumar et al. (2001); Furnkranz (2002)]
– Selects best set of features for class
pairs, performs classification, then combines
results for C-class problem via voting or
maximum aposteriori rule – Can incorporate pairwise feature extraction
– Yields classifiers
– Potential coupling of outputs
2
C
• Error correcting output codes [Dietterich and Bakiri (1995);Rajan and Ghosh (2004)] – C -class problem encoded as binary classifiers which
bi d b ti
C C >>
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are combined by voting
– Each class assigned according to code book
– Observations labeled as class with code closest to code formed byoutputs of classifiers
– Characteristics• Implemented with user-selected classifiers
• Feature selection/extraction tunable to
pairwise classifiers
• Output dependent on classifier separation• Reduces impact of small sample sizes
• Does not exploit natural class affinities
C C ×
C
• Support vector machines [Vapnik (1995); Hsu and Lin (2002)]
– Maximize the margin between two class samples instead of accuracies
Characteristics
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– Characteristics
• Formulated as an optimization problem for a binary classifier
• Nonparametric
• Potentially high dimensional decision boundary
• Classification accuracies dependenton typically slow tuning
• Tends not to overtrain, which leads to
high classification accuracies and
good generalization
Support Vector Machine
Margin
Support Vectors
wx+b=0
wx+b<=-1
wx+b>=1
x2
Support Vector Machine
Margin
Support Vectors
wx+b=0
wx+b<=-1
wx+b>=1
x2
• Hierarchical Decomposition Methods
– Characteristics
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• Adopt a sequential “divide and conquer” approach involvingdecomposition based on input or output spaces
• Maintain natural groupings with useful domain knowlege
– Hierarchical Decision Trees
• Tree constructed sequentially based on series of binary questions and
splitting rule which maximizes decrease in impurity of parent and
child nodes
– CART: Best split based on linear combinations of input features; Gini
impurity index, post pruning [Brieman et al. (1984)] – C4.5: Impurity index based on entropy [Quinlan (1986)]
– Binary Hierarchical Classifier [Kumar et al. (2002); Morgan et al.(2004)]
• Output space decomposition approach
– C leaf nodes, C-1 internal nodes
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• Top down tree constructed via deterministic simulated annealing• Feature extraction/selection tunable to each internal node
• Classifier specific to each internal node (Fisher discriminant,
SVM)
• Mitigates small sample problems
• Exploits natural class affinities
4A+3 C Ω = 4A+2 3Ω =
2A+1 3,C Ω =
2A 6Ω =
12 5Ω = 13 9Ω =
5 1C Ω = − 4 1Ω =
6 5,9Ω =
3 2, , 2,C C Ω = −K
21, 1C Ω = −
1
Leaf node
2
C-11
3
6
95
2A+1
C 3
1 1, ,C Ω = K
A
A 3,6,C Ω =
6
Internal node
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Let’s look at an example
Study Site: Okavango Delta, Botswana
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Classification Results: Okavango Delta
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Classification scheme consists
of 14 landcover classes selectedusing IKONOS imagery,
aerial photography, and field
campaigns
Hyperion Hyperspectral Data Inputs
• Hyperion hyperspectral data- Acquired by Earth Observer-1 Satellite May 31, 2001
- Converted to reflectances, destriped, georeferenced
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15995exposed soils14
268mixed mopane13
181short mopane12
305acacia grasslands11
248acacia shrublands10314acacia woodlands9
203island interior 8
259firescar 7
269riparian6
269reeds5 215floodplain grasses24
251floodplain grasses13
101hippo grass2
270water 1
Sample SizeClass NameClass #
Design of Experiment for Random Forests
• Create 10 independent stratified samples of training/test data – Select 75%:25% of training data for training and test
– Subsample from 75% to achieve 50, 15% sample size; test remains
constant
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constant
– Select spatially removed test sample
• Create BHC tree
– Select bagged sample
– Randomly sample input features
• (D/5) or min(D/5, 20)
– Develop tree
• Grow BHC Random Forest
• Compare to BB-BHC, RS-BHC, BB RS BHC, RF CART
Adequate Destriping is Critical for Classification
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Classification Results with Poor Destriping
Classification Results: BHC AlgorithmOkavango Hyperion
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(a) Subset of Hyperion data
(Bands 51, 149, 31),(b) Classified image of Hyperion data
Classification Accuracy, Ind Test Set (%)
66
67
68
69
70
71
72
73
A c c u r a c y
Classification Accuracies, Independent Test Set
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Std. Deviation, Classification Accuracy
0
0.5
1
1.5
2
2.5
3
3.5
4
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8
Training Sample Fraction
64
65
66
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8
Training Sample Fraction
BB RS BHC RS BHC BB BHC
RF BHC1 RF BHC2 RF CART
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Next Steps - an interactive discussion
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