Transcript Perspectives on SAR Processing Ian Cumming

Perspectives on SAR Processing
Ian Cumming
Professor Emeritus
Radar Remote Sensing Group
Dept. of Electrical and Computer Engineering
University of British Columbia
Perspectives on SAR Processing
1. Some SAR processing history
2. Review of current SAR proc. algorithms
3. Which algorithms are used today?
4. Some image examples
5. Summary and final thoughts
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Some Processing History
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In 1976, SAR data were processed by coherent optics
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Challenge of digital processing
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only one book available
very hard to understand for a DSP engineer
used laser beams and lenses to focus image
fast, but limited dynamic range
must be a better way – digital processing!
modest computing resources (memory & speed)
processing algorithms not known
received signal model was needed (geometry)
limited satellite orbit knowledge
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Early Airborne Processing
In 1970’s, CCRS obtained an airborne SAR from ERIM
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Installed on a Convair-580
Previous processor was optical
MDA contracted to built an on-board real-time processor
First RTP delivered in 1979 (additional units in 1985)
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Early Airborne Processing
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Processor characteristics
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time-domain correlation
X-band, no RCMC needed
On-board real-time processor
on CCRS Convair-580
built from discrete
components
multiplier-accumulator
chips, small memory
chips
4 giga ops per second in
one chassis
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performed azimuth
compression first, then
range compression
real-time waterfall display
on-board
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Early Satellite Processing
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SEASAT (October 1978)
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developed detailed model of satellite motion
led to received signal model
matched filtering done by FD correlation process
had to separate range and azimuth processing
to fit in to computer resources
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range cell migration correction a challenge
needed interpolator to eliminate paired echos
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First Digital SEASAT Image
Featured in
Aviation Week,
Feb. 26, 1979
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Typical product 1979-1981
• Niagara Falls
• 40 x 40 km scene
• 25 m resolution
• 4 looks
• 40 hours to process
• “big” minicomputer
• corner turning on disc
• needed autofocus for
L-band
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Review of SAR Processing Algorithms
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Sept. 11, 2007
Developed primarily for satellite SAR processing
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Range/Doppler (1978) JPL & MDA
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Time-domain
(1978)
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SPECAN
(1980) MDA
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Omega-K
(1988) Polimi, Italy
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Chirp scaling
(1992) CCRS, DLR , MDA
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SAR Signal
Model
• Range and azimuth extent of
signal received from a point
target (shown in red)
• The signal in the two axes is
not orthogonal because of the
curvature – if it were, the
processing would be quite
simple
•The processing is achieved by
a matched filtering (correlation)
operation, but what to do about
the curvature, if we are restricted
to 1-D operations?
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Range/Doppler Algorithm – 1
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Developed at MDA and JPL
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1977-79 plus later refinements
Used 1-D frequency domain correlation
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processing separated in range and azimuth dimensions
for computing efficiency & memory limitations
Had to deal with range migration
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MDA recognized importance of accurate interpolation
JPL developed secondary range compression to deal with
range-azimuth coupling when migration large – 1984
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Range/Doppler Algorithm – 2
SRC not
shown, as
it can be
applied in
different
places
So named because the key operations of RCMC and azimuth compression
are performed in the range time/azimuth frequency (i.e., Doppler) domain.
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Range/Doppler Algorithm – 3
A one-dimensional interpolator can correct multiple targets in the range/Doppler
domain.
After the interpolation, the locus of energy is corrected, but a phase error persists,
which can defocus the image. SRC was implemented to correct the phase error.
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Range/Doppler Algorithm – 4
When the squint is high or the aperture wide, SRC is needed to ensure accurate
focusing. SRC is mainly a function of azimuth frequency, then range frequency.
Therefore, it is best to apply
SRC in the 2-D frequency
domain.
It is applied efficiently by a
phase multiply after an
azimuth FFT is done.
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Range/Doppler Algorithm – 5
Processing with SRC can be done by modifying the range FM rate in rangcomp.
This is the approximate approach.
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Range/Doppler Algorithm – 6
This slide shows how the target focus deteriorates when the squint angle
is increased.
With no SRC, the
focus deteriorates
quite fast with
squint.
Approx SRC allows
a fair amount of
squint, but the
accurate SRC (not
shown) allows a
large squint.
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Range/Doppler Algorithm – 7
This slide shows the RDA with
1) No SRC
2) Accurate SRC
3) Approximate SRC
The Option 3 is very simple to
apply, and is most frequently
used.
Which one is needed depends
on the squint angle and the
width of the aperture.
Option 3 can be applied when
needed.
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Range/Doppler Algorithm – 8
Advantages
Easy to understand & program
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uses only 1-D operations
Accommodates range-variant
parameters easily (except SRC)
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Doppler centroid
azimuth FM rate
Disadvantages
Interpolator introduces a small
but controllable error
Cannot handle very wide
apertures or high squint
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unless SRC is applied as
needed
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• City of Vancouver
• RDA processing
• RADARSAT-1 FINE mode
• 8 m resolution
October 2007
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Convair-580 Data processed using RDA
October 2007
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Chirp Scaling Algorithm – 1
Want to improve accuracy by replacing the RCMC interpolator with a
more accurate DSP operator
The chirp scaling concept  if a linear FM signal is shifted in
frequency, it will be shifted in time after pulse compression
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same concept that causes a railway train to be imaged off the tracks 
the train’s Doppler shift moves the train in azimuth after azimuth
compression
Chirp scaling is used to perform differential RCMC in the
range/Doppler domain, to equalize the curvature over range
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Chirp Scaling Algorithm – 2
Illustrates how a chirp (Panel 1)
multiplied by a sine wave (Panel 2)
causes a registration shift in the
compressed pulse (Panel 4).
Because the matched filter (not shown)
has a zero centre frequency, the
compressed pulse is registered at the
zero frequency point of the scaled signal
(Panel 3) .
This shift performs RCMC in the
range/Doppler domain, assuming range
compression is not done yet (because
the amount of shift is limited by the
range oversampling, only differential
RCMC is done with chirp scaling).
The sine wave scaling is applied in the
range direction, with a different
frequency at each azimuth frequency.
Note that the resulting shift is constant
with range.
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Chirp Scaling Algorithm – 3
But the shift needs to be varied
with range (as well as with
azimuth frequency).
As the change with range is
small, this can be achieved by
making the frequency of the
scaling function change slowly
with range.
A linear FM scaling function
makes the range shift vary
linear with range (called linear
chirp scaling).
This linear form is adequate for
most satellite SAR parameters.
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Chirp Scaling Algorithm – 4
All operations are FFTs and phase multiplies
Chirp scaling is applied in the RD domain
The 2-D freq domain can be used to perform
the remaining RCMC without an interpolator.
Range compression, SRC also done. SRC is
accurate as it is range and az freq dependent.
Subsequent ops are the same as the RDA
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SRTM Image Processed by CSA
October 2007
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Chirp Scaling Algorithm – 5
Advantages
Disadvantages
More accurate RCMC
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no interpolator
The 2-D frequency domain is
available to apply a more
accurate form of SRC
Requires 2-D processing
Additional complexity if the
Doppler centroid changes
quickly with range
better phase accuracy
Assumes SRC is independent
of range
Can handle slightly wider
apertures and squint angles
Inefficient if range compression
already done
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For many satellite SAR parameters, the advantages over the RDA
tend to outweigh the disadvantages, but RDA preferred for zero
Doppler cases.
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Omega-K Algorithm – 1
If the range equation is hyperbolic (straight line sensor motion), and
the effective radar velocity is independent of range, an “exact”
solution can be obtained
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this assumption is excellent for airborne radars (after mocomp) and
quite good for satellite SARs
Engineers at the Polytechnic of Milano discovered this, adapting an
algorithm known as Stolt interpolation from the seismic field
All processing is done in the 2-D frequency domain
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Omega-K Algorithm – 2
The Stolt interpolation consists of
scaling and shifting operations, as
illustrated by a point target
simulation.
Col 1: phase plot in 2-D FD
Col 2: range slices in 2-D FD
Col 3: data after range IFFT
(2-D energy & 1-D azimuth slice)
The simulation starts with data
after the bulk compression, and
shows the effects of the scaling
and shifting separately.
SRC is done exactly during the
bulk compression – the Stolt
interpolation effectively makes the
signal stationary in range and
azimuth.
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Omega-K Algorithm – 3
• SIVAM X-band airborne radar built by MDA
• Spotlight operation – 1.8 m resolution
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Omega-K Algorithm – 4
Advantages
Most accurate algorithm if
constant-velocity assumption
valid (SRC exact)
Accuracy not affected by wide
aperture and high squint
Disadvantages
An interpolation is needed in
the 2-D FD
Cannot handle large changes
of Doppler frequency with
range efficiently
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Fairly easy to program if
Doppler centroid does not vary
much with range
October 2007
need range block processing
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SPECAN Algorithm
When the signal is a chirp, it can be deramped using a
phase multiply, making it a sine wave
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a single FFT then focuses the data
Single-look version was developed many years ago
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multilook version developed in 1979 under an ESTEC contract
for on-board processing
Has the most efficient computing, but the worst image
quality, notably phase properties
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particularly suited to burst-mode data, such as ScanSAR
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• RADARSAT-1
• ScanSAR Narrow
• 300 km wide
• SPECAN processing
• Vancouver Island
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Doppler Centroid Estimation
Considered a mature technology
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yet processing mistakes are still made
Recommend a new approach
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take a global view
use spatial diversity over a wide area
use quality checks and filtering to eliminate bad regions
use a physical geometry model for overall estimate
use phase increments for primary estimator
Can be used for specialized applications
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such as ocean current estimation
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More details can be
found in our “SAR
Processing” book
Published by
Artech House
January 2005
Also to be published in
Chinese, November 2007
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Summary – 1
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RDA
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CSA
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a little more accurate than the RDA, as long as the Doppler
parameters do not change too quickly with range
WKA
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the most accurate as long as the flight path is linear and the velocity
does not change with range (well suited to airborne applications)
SPECAN
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a well-known algorithm for general use (30 years experience)
except when the aperture is wide and the squint angle is high
needs the least memory and fewest DSP operations
ideal for low-resolution imaging
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Summary – 2
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Many choices of algorithm are available
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Best algorithm for each application depends on
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Most commonly-used algorithm for general
precision processing seems to be the RDA,
followed by WKA and the CSA
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geometry and radar parameters
accuracy required
efficiency required
but a systems study needs to examine the pros and
cons for each radar situation
SPECAN is commonly used for ScanSAR and
quicklook processing
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Current & Future Research
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Many people consider SAR processing for
conventional (remote sensing) SARs to be a
mature subject, not requiring significant further
development
Current work seems to be directed towards
advanced items such as:
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bistatic SAR processing
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ground moving target detection (GMTI)
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information extraction, e.g.
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classification of polarimetric SAR data
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change detection through interferometry
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polarimetric interferometry
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Final Word
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Ever since working with ESTEC in 1979, onboard, real-time, digital SAR processing on a
satellite has been a dream
For various technical, financial and strategic
reasons, it has not been implemented yet
it is becoming feasible now, although its necessity is
not justified
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As a compromise, I would love to build a realtime Doppler estimator
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would give the most accurate antenna steering
possible, and
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be a technical showcase !
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