Transcript Slide 1

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Mary Pagnutti
Robert E. Ryan
Kara Holekamp
Innovative Imaging and Research
Building 1103 Suite 140 C
Stennis Space Center, MS 39529
18th William T. Pecora Memorial Remote Sensing Symposium
Herndon, Virginia
November 16, 2011
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Spatial Resolution is the minimum distance
between two adjacent features or the
minimum size of a feature, that can be
detected by a remote sensing system.
(www.geocomm.com)
Spatial Resolution is not simply ground
sample distance
 Also depends on how well a system is focused
 Point Spread Function (PSF)
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PSF describes the response that an electro-optical
system has to a point source
 The sharper the function, the sharper the object will appear in
the system output image
 In practice, directly estimating PSF can be challenging due to
sampling and SNR issues
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0.8
0.6
0.4
FWHM
0.2
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0
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-2
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-2
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-4
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Sampling
GSD 1 m
Blurred Image
20 m x 20 m Target
Input Image
20 m x 20 m Target
GSD 2 m
+
PSF
4 m FWHM
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GSD 4 m
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Sampling
Input Image
20 m x 20 m Target
Blurred Image
20 m x 20 m Target
GSD 1 m
GSD 2 m
GSD 2 m
+
PSF
1 m FWHM
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GSD 4 m
GSD 4 m
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 Measures of Merit
 Point Spread Function (PSF)
 Modulation Transfer Function (MTF) at Nyquist
Frequency
 Relative Edge Response (RER)
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MTF is a parameter described in the spatial frequency
domain
 Mathematically allows you to model the imaging process by
multiplication instead of convolution
 Not physically intuitive
 Evaluated in two separate orthogonal directions consistent with
the along track and cross track of the image
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MTF is defined as the magnitude of the OTF (Optical
Transfer Function)
 OTF is defined as the Fourier Transform of the PSF
OTF ( u , v ) 
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


PSF ( x , y ) exp[  i 2  ( u  v )] dxdy
MTF ( u )  OTF ( u )
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OTF ( 0 )
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Edge Response
Modulation Transfer Function
MTF
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DN
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Distance / GSD
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Nyquist frequency
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0.4
0.2
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0.2
0.4
0.6
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Normalized spatial frequency
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Line Spread Function
Differentiate
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Line Spread Function
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0.8
Fourier
Transform
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-2
-1
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Distance / GSD
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DN
3 examples of
undersampled
edge responses
measured
across the
tilted edge
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x
– edge tilt angle
– pixel index
Pixels
– pixel’s distance from edge (in GSD)
Solution: Image tilted edge
to improve sampling
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Problem: Digital cameras
undersample edge target
Superposition
of 24 edge
responses
shifted to
compensate
for the tilt
March 8, 2006
Distance/GSD
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Ringing Overshoot
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1.0
Region
where
mean
slope is
estimated
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Ringing Undershoot
-2.0
-1.0
0.0
1.0
2.0
Pixels
RER 
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Another measure of spatial
resolution is a difference of
normalized edge response
values at points distanced
from the edge by -0.5 and 0.5
GSD
Relative Edge Response is one
of the engineering parameters
used in the General Image
Quality Equation to provide
predictions of imaging system
performance expressed in
terms of the National Imagery
Interpretability Rating Scale
[ ER X ( 0 . 5 )  ER X (  0 . 5 )][ ER Y ( 0 . 5 )  ER Y (  0 . 5 )]
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
LT 
Part of radiance that originates in
the pixel area is given by:
RER = 0.50
  PSF ( x , y ) L ( x , y ) dxdy
 0 .5  0 .5
L P / LT  RER
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Line Spread Function
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0.2
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Distance / GSD
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-2
-1
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Distance / GSD
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0.75
0.5
0.25
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RER2 = 0.25 means that 25%
of information collected with
the pixel PSF (blue square)
comes from the actual pixel
area (shadowed square)
&
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-3
0 .5
Relative Edge Response squared
(RER2) can be used to assess the
percentage of the measured pixel
radiance that actually originates
from the Earth’s surface area
represented by the pixel:
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ER(0.5) - ER(-0.5) =
0.75 - 0.25 = 0.50
( x , y ) L ( x , y ) dxdy
 
LP 
A simple example:
Box PSF
Width = 2 GSD

  PSF
0 .5
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Normalized Edge Response
Radiance measured for each pixel
is assumed to come from the
Earth’s surface area represented by
that pixel. However, because of
many factors, actual measurements
integrate radiance L from the
entire surface with a weighting
function provided by a system’s
point spread function (PSF):
GSD
Source: Blonski, S., 2005. Spatial resolution characterization for QuickBird image
esearch
products:
2003-2004 season. In Proceedings of the 2004 High Spatial
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Resolution Commercial Imagery Workshop, USGS, Reston, VA, Nov 8–10, 2004
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MTF and RER can be related to each other
through Fourier analysis
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Canon EOS Rebel 8 Megapixel Camera
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Max resolution 3456 x 2304
CMOS Bayer Array
Manual mode
Raw data
Pixel size 6.3 micron
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Measured building GSD = 1.2 cm
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e.g. AVIRIS, Hyperion etc.
High SNR
High Spatial Res.
Hyperspectral
Imagery
Spectral Band
Synthesis
High SNR, High
Spatial Resolution
Multispectral
Imagery
Band-to-Band
• Simulations based on spectral and
spatial degradation of higherresolution hyperspectral images
acquired with existing remote
sensing instruments
Registration
MTF Transfer
Function
Resampling
e.g. LDCM, Sentinel-2
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Coarser Spatial
Resolution
Multispectral
Imagery
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Data
Quantization
Noise
Simulation
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MTF = 0.3
GSD = 4.8 cm
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GSD = 12 cm
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GSD = 18 cm
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MTF = 0.5
MTF = 0.1
GSD = 4.8 cm
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MTF = 0.5
MTF = 0.1
GSD = 12 cm
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MTF = 0.5
MTF = 0.1
GSD = 18 cm
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MTF = 0.5
MTF = 0.4
MTF = 0.3
GSD = 4.8 cm
GSD = 12 cm
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GSD = 18 cm
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MTF = 0.2
MTF = 0.1
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MTF = 0.5
MTF = 0.4
MTF = 0.3
GSD = 4.8 cm
GSD = 12 cm
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GSD = 18 cm
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MTF = 0.2
MTF = 0.1
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Laboratory measurements
 Vendor provided prior to delivery
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Operational field measurements
 Validate image quality over life of instrument
 Typically require engineered targets whose contrast should strive
to maximize the dynamic range of the sensor being evaluated
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Point source targets
Edge targets
Pulse targets
Contrast transfer targets (tri-bars and radial targets)
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Validation of spatial resolution is typically performed using
specially designed edge targets
 Deployable: Radiometric tarp edges
 Permanent: Painted concrete edge targets
3.7 deg
QuickBird Imagery
Panchromatic
Imagery
Feb 17 2002
Tarp Edge
Concrete Edge
20 m
Concrete Edge
10 m
10 m
20 m
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QuickBird Imagery
Panchromatic
Imagery
Nov 14 2002
National Aeronautics and Space
Administration
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These types of targets however, will not generally be available in
the imagery to validate spatial resolution
Deployable targets at South
Dakota State University
Pong Hu, Taiwan
Fort Huachuka
tri-bar target
Finnish Geodetic Institute Sjökulla Site
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Causeway bridge over Lake
Pontchartrain
Digital Globe provided satellite imagery
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Most commonly used spatial resolution
estimation techniques require engineered
targets (deployed or fixed), which are not
always available or convenient
Target size scales with GSD
 Edge targets are typically uniform edges 10-20
pixels long and ~10 pixels tilted a few degrees
relative to pixel grid (improve sampling)
 Increasing GSD increases difficulty
 Moderate resolution systems such as Landsat use
pulse targets
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Exploit edge features in nominal imagery
 Edge response estimation is performed without dedicated
engineered targets
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Appropriate for high spatial resolution Imagery
Automated processes exist that can
 Identify edges and screen them
 Construct resulting edge response
 Calculate MTF and RER
Rooflines
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Building Shadows
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Spatial Resolution is the minimum distance between two
adjacent features or the minimum size of a feature, that can
be detected by a remote sensing system
In addition to GSD, spatial resolution depends on a system’s
PSF (blur)
Spatial Resolution is often quantified/specified in terms of
MTF at Nyquist or RER
Spatial Resolution should be monitored throughout a system’s
lifetime
For more information…
Pagnutti, M., S. Blonski, M. Cramer, D. Helder, K. Holekamp, E.
Honkavaara, R.E. Ryan. 2010. Targets, methods, and sites for assessing
the in-flight spatial resolution of electro-optical data products,
Canadian Journal of Remote Sensing, 36:(5) 583-601.
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