IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 40, NO. 9, SEPTEMBER 2002
1917
Burst-Mode and ScanSAR Interferometry
Jürgen Holzner, Student Member, IEEE, and Richard Bamler, Senior Member, IEEE
Abstract—ScanSAR interferometry is an attractive option for
efficient topographic mapping of large areas and for monitoring
of large-scale motions. Only ScanSAR interferometry made it possible to map almost the entire landmass of the earth in the 11-day
Shuttle Radar Topography Mission. Also the operational satellites
RADARSAT and ENVISAT offer ScanSAR imaging modes and
thus allow for repeat-pass ScanSAR interferometry. This paper
gives a complete description of ScanSAR and burst-mode interferometric signal properties and compares different processing
algorithms. The problems addressed are azimuth scanning pattern
synchronization, spectral shift filtering in the presence of high
squint, Doppler centroid estimation, different phase-preserving
ScanSAR processing algorithms, ScanSAR interferogram formation, coregistration, and beam alignment. Interferograms and
digital elevation models from RADARSAT ScanSAR Narrow
modes are presented. The novel “pack-and-go” algorithm for
efficient burst-mode range processing and a new time-variant fast
interpolator for interferometric coregistration are introduced.
Index Terms—Burst-mode, ENVISAT, pack-and-go algorithm,
RADARSAT, ScanSAR, SRTM, synthetic aperture radar interferometry.
I. INTRODUCTION
S
CANSAR uses the burst-mode technique to image large
swaths at the expense of azimuth resolution [1], [2]. Typical
swath widths of RADARSAT and ENVISAT/ASAR ScanSAR
images are 300–500 km, and azimuth resolution is in the order
of 50 m–1 km. ENVISAT/ASAR uses burst-mode imaging also
for its alternating polarization mode [3].
Interferometric (InSAR) applications can benefit from the
increased swath width of ScanSAR data or the increased information content in alternating polarization mode data. Some
attractive properties of wide swaths are more economic use of
ground control points for calibration of the imaging geometry,
reduced need for postprocessing to mosaic large digital elevation models (DEMs), and more frequent coverage for differential InSAR applications. Only ScanSAR interferometry made it
possible to map 80% of the earth’s landmass during the 11-day
Shuttle Radar Topography Mission (SRTM).
In all of these cases, and in contrast to the traditional
strip-map modes, the raw data consist of bursts of radar echoes
with azimuth extent shorter than the synthetic aperture length.
This acquisition strategy is the reason for the azimuth-variant
Manuscript received December 18, 2001; revised June 20, 2002. This work
was carried out in the context of RADARSAT NASA-ADRO Project 500. The
RADARSAT data are copyright Canadian Space Agency 1996, 1997, and 1998.
DLR’s research and development activities on RADARSAT ScanSAR interferometry were supported in part by the former DARA in the framework of the
GEMSAR project and by the German Canadian Scientific Technical Cooperation Programme.
The authors are with the German Aerospace Center (DLR), Oberpfaffenhofen, D-82234 Wessling, Germany (e-mail: juergen.holzner@dlr.de).
Digital Object Identifier 10.1109/TGRS.2002.803848
spectral properties of the signal (Section III) and complicates
synthetic aperture radar (SAR) signal processing and interferometric processing.
Since the ScanSAR modes of current satellites were motivated by radiometric imaging rather than by interferometry,
the development of phase-preserving and interferometric
burst-mode processors is far behind the established processing
chains for strip-map mode data. There is, for example, no
complex ScanSAR product in the official RADARSAT or
ENVISAT/ASAR product lists. Several algorithms have been
proposed in the scientific literature [4]–[7], and an operational
dedicated system for the single-pass SRTM interferometer has
been built up by the Jet Propulsion Laboratory recently [8]. The
purpose of this paper is to give a description of burst-mode and
ScanSAR interferometry, including signal properties and necessary processing steps. Several algorithms are compared with
respect to their expected accuracy, throughput, implementation
effort, and reusability of software from strip-mode processors.
The repeat-pass case is considered throughout the paper,
and the theoretical findings are illustrated by a RADARSAT
ScanSAR example.
The paper is organized as follows. Section II describes
the RADARSAT modes and the dataset used in the paper.
Section III reviews the particular properties of burst-mode
SAR data. Section IV discusses important preprocessing steps.
In Section V several phase-preserving burst-mode processing
algorithms are compared, while Section VI discusses two
interferometric processing schemes. Section VII finally gives
an example for a first RADARSAT ScanSAR DEM.
II. INTERFEROMETRIC RADARSAT DATASETS
The RADARSAT SAR instrument provides various imaging
modes, such as the strip-map mode with standard (S1–S7), wide
(W1–W3), and fine (F1–F5) beams as well as various ScanSAR
modes [9], [10]. The so-called ScanSAR Narrow modes combine beams W1 and W2 for the “ScanSAR Narrow Near,” or
W2, S5, and S6 for the “ScanSAR Narrow Far” modes, respectively. Both narrow modes cover a swath width of about 300 km.
The ScanSAR wide modes combine beams W1, W2, W3, and
S7, or W1, W2, S5, and S6, providing swath widths of 500
and 450 km, respectively. RADARSAT is the first spaceborne
system to operate in ScanSAR mode that is also able to provide
suitable data for ScanSAR interferometry.
RADARSAT was not designed originally for repeat-pass
InSAR. Both the orbit accuracy and the repeat cycle of 24
days are not favorable in this respect. On the other hand,
RADARSAT’s 30-MHz fine-resolution mode combined with
49 ) provides an interferometric
large incidence angles (
mode with a critical baseline as large as 6 km. First results on interferometry and differential interferometry using
0196-2892/02$17.00 © 2002 IEEE
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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 40, NO. 9, SEPTEMBER 2002
TABLE I
RADARSAT DATASETS USED FOR SCANSAR INTERFEROMETRY IN THIS
PAPER. WE DENOTE VARIATION FROM NEAR TO FAR RANGE BY “…”
Fig. 1. Critical baseline of various RADARSAT modes and ERS as a function
of incidence angle. The bandwidths of the SAR modes are 11.6, 17.3, 30.0, and
15.5 MHz for wide, standard, fine beam mode, and ERS, respectively.
RADARSAT’s strip-map modes have been reported in [11].
In ScanSAR modes, however, the range bandwidth is as low
as 11.6 MHz, giving a critical baseline in the order of 600 m
(Fig. 1).
RADARSAT cannot synchronize its azimuth burst cycles at
different acquisitions. In order to maximize coherence, a step
called azimuth scanning pattern synchronization (ASPS) is required for preprocessing the datasets. The aim is to preserve
only those raw data lines of companion bursts that image the
same area on ground (Section IV). Since the chance of obtaining overlapping raw data lines decreases with the number of
beams, only ScanSAR Narrow mode can be considered suitable.
We have ordered data acquisitions based on RADARSAT’s predicted orbit drifts. Among the 12 ScanSAR datasets, two pairs
could be formed with baselines shorter than the critical one. In
the following, we will use the pair with the better azimuth scanning pattern overlap. The properties of this data pair are summarized in Table I.
III. BURST-MODE SIGNAL AND SPECTRAL PROPERTIES
This section reviews the unique properties of burst-mode
data. In Section III-A we recall some important basics of SAR
processing in order to lay a common ground concerning the
terminology used in the paper.
SAR processing algorithms must consider the hyperbolic form
of the range history, while for our derivations the quadratic
Taylor series approximation of (1) is sufficient and will be used
throughout the paper.
Let the SAR periodically transmit pulses of the form
, where [s] denotes the fast (range) time
unique to each pulse with origin at the particular transmit event
and where [Hz] is the radar carrier frequency. In the case of
is a complex-valued chirp.
a chirped pulse, the function
All received pulses are replicas of the transmitted pulse delayed
, where [m/s]
by the round-trip delay time
represents the velocity of light, with amplitude given by the
radiometric radar equation. The only radiometric component
relevant for this paper is the two-way azimuth amplitude
(scaled to the azimuth time coordinate).
antenna pattern
After coherent demodulation, i.e., removal of the radar carrier
frequency, the ensemble of received pulses is given by
A. SAR Signal Processing Preliminaries
Since SAR data acquisition and processing are linear operations, it is sufficient to consider how a point-like scatterer is
imaged. Let a (strip-map) SAR fly along a straight path with
[m/s] and illuminate a point scatterer at a distance
velocity
[m] to the flight path. The position of the scatterer is further
determined by the time [s], when SAR and scatterer have a
minimum distance (point of closest approach or zero-Doppler
and reference the scatterer position in a
position). Then
cylindrical radar coordinate system (the third coordinate—the
look angle—is only accessible by interferometry).
The instantaneous distance of the SAR to the scatterer at azimuth time [s] is
(1)
(2)
This point response shows a range sweep characterized by
, while
and
are the envelope
and the phase term of the azimuth chirp, respectively.
SAR processing “convolves” the raw data with an appropriate
filter kernel such that every raw data point response gets focused to a sharp point as narrow as the range and azimuth bandwidths of the system allow. In fact, the required operation is
not a convolution in the strict sense, since the point response
to be inverted is range-variant [see (2)]. SAR focusing usually
starts with range compression, i.e., the application of a simple
range-matched filter to compress the transmitted range chirp.
After that operation, the raw data look like a short pulse, i.e., as
HOLZNER AND BAMLER: BURST-MODE AND SCANSAR INTERFEROMETRY
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Fig. 2. Raw data of a single-point scatterer recorded in strip-map (top) and in four-look burst-mode (bottom). To illustrate the oscillations in these complex-valued
signals, only the real parts are shown. The range migration is exaggerated to illustrate the SAR response properties.
if a sinc-like envelope was transmitted. Fig. 2 (top) illustrates
such a range-compressed raw-data-point response. Clearly visible is the range sweep or range cell migration effect due to
, renthe range history in the envelope term
dering the problem of SAR processing not only range-variant
but also two-dimensional. This means that SAR processing is
not readily separable into independent azimuth- and range-filtering steps. In order to achieve separability, most of the SAR
processing algorithms employ an operation called range cell migration correction that straightens the range trajectory prior to a
purely one-dimensional (1-D) range-variant azimuth filtering.
For the purpose of this paper, it is sufficient to know that
high-quality strip-map SAR focusing algorithms exist (e.g., see
[12] and [13] and references therein). We will introduce and
discuss burst-mode processing algorithms that take maximum
advantage of existing strip-map algorithms as far as possible.
Since burst-mode data differ from strip-map data only in the
azimuth direction, we can simplify the equations and consider
only the azimuth direction of the SAR signal. For convenience,
we will use the quadratic approximation of the range history of
(1), the azimuth (Doppler) frequency modulation rate
FM
Hz
s
(3)
and the Doppler frequency envelope generated by the azimuth
antenna pattern
FM
(4)
B. Strip-Map Signal
Now, let [Hz] be the (Doppler) frequency domain coordinate conjugate to azimuth time , PRF [Hz] the pulse repetition
[Hz] the processed Doppler bandfrequency (PRF), and
width of a strip-map (nonburst) SAR signal. Then, the synthetic
aperture length is
FM
s or
PRF
number of range lines
(5)
The azimuth resolution of such a SAR system is (in time units)
FM
s
(6)
where is a factor close to unity that depends on the shape of
the azimuth antenna pattern and on weighting functions used
by the SAR processor for side-lobe control. For simplicity, we
throughout the paper. The effective strip-map
will use
is
azimuth raw data signal of a point scatterer located at
then given by
FM
FM
(7)
with spectrum (stationary phase approximation)
FM
(8)
Here, we have assumed zero-Doppler steering of the antenna
beam and have discarded an inconsequential factor in the specis the spectral envelope and reflects the two-way
trum.
Doppler antenna pattern (Section III-A). For mathematical
convenience, we also include any weighting and band-lim. Within the
iting function applied during processing in
FM is the time-domain
stationary phase approximation,
envelope of the azimuth chirp. Its support is restricted to
.
SAR processing removes the quadratic phase from the
spectrum of (8). Hence, the shape of the focused point target
is the inverse Fourier transform of
response
(9)
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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 40, NO. 9, SEPTEMBER 2002
Fig. 3. Train of raw-data bursts and its spectrum (magnitude) according to (14).
where corresponding small and capital letters denote the
]. Since
is
Fourier transform pairs [e.g.,
,
has the resolution given
band-limited to
in (6).
phase assumption. The low time-bandwidth-product case has
been investigated in [14].
The number of bursts per synthetic aperture
(12)
C. Burst-Mode Signal
Consider now that the SAR system characterized above is
consecutive
operated in the burst-mode such that bursts of
[s]. We will refer to
echoes are recorded every
s or
PRF
as the burst duration and to
s or
PRF
number of range lines
(10)
number of range lines
(11)
is often called the number of looks. Fig. 2 illustrates the relationship between the strip-map and the burst-mode SAR raw data of
a single point scatterer. For the illustrations in Figs. 2, 3, 5, and
.
9, we use
The burst-mode raw-data-point scatterer response can be
described as
FM
in general is not
as the burst cycle period. Note that
integer, since the SAR may be operated at a different PRF
between two bursts [5]. This subtlety is not substantial for
the following signal description and will not be considered
further. Throughout the paper, we assume that the number
of range lines per burst is large enough for a sufficiently high
time-bandwidth product, such that we can use the stationary
rect
FM
(13)
with spectrum (within the stationary phase approximation)
FM
rect
FM
(14)
HOLZNER AND BAMLER: BURST-MODE AND SCANSAR INTERFEROMETRY
1921
Fig. 4. Time-frequency diagram of focused multiple-burst RADARSAT
ScanSAR data in a distributed target area (f : Doppler centroid frequency).
where
and
are the burst bandwidth and the spectral
burst cycle period, respectively:
FM
and
FM
(15)
Fig. 3 illustrates the relationship of the burst-mode signal
to its spectrum. Each burst contributes an approximately
rectangular-shaped band-pass spectrum at center frequency
FM , which depends on the location
of
the point scatterer and the burst number . We will neglect
in
the slight distortion of these rect-functions due to
the following. Weighting of the bursts is often applied in
the processor for purposes of side-lobe control. In that case,
the single-burst spectrum reflects the shape of the weighting
function rather than a rect-function.
If each of the bursts is processed individually, its point response will be
FM
(16)
with the inherent single-burst resolution of
FM
s
(17)
If the full-burst train is processed coherently, the spectral and
time-domain representations of the coherent point response will
be [5]
rect
Fig. 5. Relationship of single-burst and burst train point responses. The small
SAR image patch shows the response of a corner reflector processed in the burst
train fashion [5].
Fig. 6. Doppler centroid estimates f^ (circles) as a function of range and
fitted second-order polynomial (data: RADARSAT ScanSAR Narrow, orbit
10 969).
position the entire Doppler bandwidth
to
is
swept. The distance between the bursts in time coordinates is the
burst period time , and in Doppler domain it is the frequency
.
difference
The same point response as given in (19) will apply if the
individually focused bursts are summed coherently. Obviously,
can be interpreted as a train of full-aperture (strip-map)
with the single-burst point response
point responses
as an envelope (Fig. 5).
FM
IV. PREPROCESSING
(18)
(19)
Fig. 4 illustrates the time-varying property of the transfer function (see also [6]) estimated from real data in a distributed target
area (ScanSAR dataset 10 969, Table I). The energy of the burst
data is represented in the domain spanned by azimuth frequency
versus azimuth time. It is concentrated in areas that show the
mentioned azimuth dependency of the burst raw-data signal. For
one scatterer, the spectrum of the processed signal is of bandFM
centered around
pass type with bandwidth
FM [see (18)]. Thus, with varying scatterer
A. Doppler Centroid Estimation
Doppler centroid estimation of burst-mode data is more difficult than in the strip-map case due to the particular shape
of the spectra. Spectra are distorted according to the azimuth
intensity profile of the target. They require sufficiently large
estimation windows to average out the distortions. In our implementation, the bursts are packed densely in the azimuth direction—giving an uninterrupted sequence of range lines—and
then passed through a standard Doppler centroid estimator. This
procedure fits well into the pack-and-go algorithm described
later.
Fig. 6 depicts the Doppler centroid estimates as a function
of range and the resulting second-order polynomial fits. The
estimates are based on range-compressed data. We have used
window sizes of 10 000 range lines by 150 range samples, i.e.,
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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 40, NO. 9, SEPTEMBER 2002
Fig. 7. Range displacement of two azimuth looks of a point-like target as
a function of PRF bands. The correct PRF band is 5 (data: RADARSAT
ScanSAR, orbit 10 969).
0
45 and 53 estimation windows for beam 1 and beam 2, respectively. A quadratic fit to these estimates suggests a standard deviation of the individual estimates of about 20 Hz for beam 1
and 10 Hz for beam 2. After fitting the Doppler centroid, the
error reduces to about 3 Hz and is thus negligible. The interferometric image pairs were processed to the average of the estimated Doppler centroid frequencies of the two datasets to avoid
spectral decorrelation in azimuth.
B. PRF Ambiguity Resolution
RADARSAT is not yaw-steered like ERS or ENVISAT.
Hence, Doppler centroid values exceeding PRF/2 are expected, making PRF ambiguity resolution necessary. We
modified the range look correlation technique [15] for this
purpose. Since ScanSAR scenes cover areas about 10 times as
large as strip-map images, it is very likely that there are a few
point-like targets in the scene. These points can be detected
automatically and used for range look correlation. Fig. 7 shows
the range cross-correlation offset of two looks of a point-like
target. In this case, a safe determination of the PRF band was
possible using a single point. In less favorable cases, the results
from several points must be averaged.
C. Azimuth Scanning Pattern Synchronization
Interferogram formation from burst-mode data requires the
synchronization of the burst patterns [azimuth scanning pattern
synchronization (ASPS)] [6], [16]. The following procedure
was applied [16]. First a small azimuth portion of the master
and slave dataset is processed to a complex image. This
means that several bursts are processed and added coherently
at the acquisition PRF (multiple coherent burst method; see
Section VI-C). Then (for each beam separately) a patch at
midrange is taken to determine the mutual azimuth shift
between the two images. We implemented the measurement
by forming interferograms for different mutual shifts. The lag
with the maximum interferogram spectral peak is chosen. This
allows for azimuth coregistration of the two burst-mode raw
datasets to an accuracy of at least one range line, i.e., sufficient
along-track referencing of the two azimuth-scanning patterns.
Fig. 8. Peak value of interferogram spectrum versus mutual azimuth shift for
azimuth scanning-pattern synchronization.
Fig. 8 shows the variation of the spectral peak maximum as
a function of mutual shift after synchronization of the scanning
patterns. The strong peak of the curve suggests sufficiently accurate ASPS. An obvious feature of the curve is its modulation,
which is caused by the particular correlation properties of the
burst-mode data (Section VI-D).
Once the mutual displacement of the two datasets has been
determined, only the range lines that have counterparts in the
other dataset’s on-cycle are kept for further processing; all the
others are discarded [16]. Table I lists the number of remaining
burst lines and the corresponding burst bandwidth for the dataset
under investigation. Each burst has a bandwidth of about 95 Hz,
resulting in an azimuth resolution on the order of 75 m. This
resolution is better than the one of DTED-1 with sampling grid
of about 100 m [17].
V. PHASE-PRESERVING BURST-MODE PROCESSING
Several algorithms have been proposed for phase-preserving
processing of burst-mode data [4]–[8], [18]. The basic question is: what does a complex burst-mode image product look
like? Two approaches are possible with implications for data
volume and further interferometric processing: single- and multiple-burst image processing.
A. Single-Burst Images
In this option, the bursts are processed to individual complex
images. The complex burst-mode product is then the ensemble
of these burst images. Each burst image has a resolution of
[see (17)] and covers an azimuth extent of
.
Allowing for the same azimuth oversampling factor as the raw
data, its sampling frequency will be
PRF
PRF
(20)
and it is sufficient to represent the burst image by
PRF
PRF
and subswath, we
range lines. Per synthetic aperture
burst images of
range
thus have
HOLZNER AND BAMLER: BURST-MODE AND SCANSAR INTERFEROMETRY
Fig. 9.
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Pack-and-go range processing of a burst train. For illustration purposes, the raw data are assumed to be pre-range-compressed.
lines each, i.e., a data volume and processing effort of
PRF lines. The factor
(21)
is approximately the number of ScanSAR subswaths (or polarization modes in the ASAR case).
An inconvenience associated with individual burst images is
their azimuth-variant spectral bandpass characteristic (time-frequency diagram in Fig. 4). Although the signal is of bandpass
type in a small neighborhood of every azimuth position, the
bandpass center frequency varies linearly with azimuth from
to
and wraps around several times in the
PRF
Nyquist band of the burst image. This excludes the
use of standard coregistration and resampling algorithms for
later interferometric processing.
B. Multiple-Burst Images
An alternative complex burst-mode product would be the
coherent superposition of the burst images forming a single
full-aperture image file. Such a representation facilitates data
handling and visual browsing, although it suffers from the
“nasty” shape of the point response of Fig. 5. It has been shown,
however, that a low-pass filter applied to the detected image
gives an image comparable to the one obtained by incoherent
superposition of burst images [5]. The average spectral envelope of such a complex burst-mode image is equivalent to that
of a strip-map SAR signal. Hence, standard interferometric
processing algorithms can be applied (Section VI). The price
to be paid is increased data volume. The full-aperture image
requires the same PRF as the raw data and, hence, has a data
than in
volume higher by a factor of
the aforementioned individual burst case. If the superposition
Fig. 10. Azimuth power spectral densities (normalized) of strip-map,
single-burst, and multiple-burst interferograms. Stationary Gaussian scattering
and a linear phase ramp, i.e., a single fringe frequency f , are assumed. The
triangular shapes of the spectra apply for rect-shaped burst profiles. In case the
bursts are weighted for side-lobe control, the triangular spectra are replaced
by the autocorrelation of the weighting function. Note that the position of the
Dirac distribution is restricted to the support [
,
] or [
,
] for
the strip-map mode or burst-mode data, respectively.
0W
W
0W
W
image is to be formed from single-burst images, the data will
have to be oversampled from PRF to PRF before adding them
coherently.
For the following systematic overview of burst-mode
processing strategies, we have conceptually separated the processing chain into range and azimuth processing. The first step
includes range compression, range cell migration correction,
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Fig. 11.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 40, NO. 9, SEPTEMBER 2002
Measured ACF of (a) ScanSAR single look, (b) multiple (
N = 2), (c) comparison of both, and (d) histogram of 121 coregistration estimates.
and secondary range compression, leaving the second step as a
purely 1-D azimuth compression (AC).
C. Range Processing
Within this section, we will focus on modifications of highprecision strip-map algorithms. Standard SAR processing algorithms [12] such as range-Doppler, wavenumber domain, or
chirp scaling can be used for range processing of burst-mode
data. Three approaches are possible.
1) Single-Burst Range Processing: Each burst of range lines
is processed separately by a standard processor whose azimuth
compression has been disabled. This method is computationally efficient and has been adopted for use in the extended chirp
scaling (ECS) algorithm [7] and in the ScanSAR extension of
our BSAR processor [19]. However, often the bursts must be
supplemented by a number of range lines containing zeroes to
the next power of two.
2) Pack-and-Go Range Processing: Since during range
processing (almost) no energy leakage in azimuth occurs,
HOLZNER AND BAMLER: BURST-MODE AND SCANSAR INTERFEROMETRY
1925
Fig. 12. (a) Differential phase of the beam 1 and beam 2 interferogram in the overlap area after reduction of a phase offset of
phase versus azimuth in (b) shows no systematic effects.
several bursts can be concatenated (with a few zero-lines as
“safety margins” separating the different bursts) and processed
simultaneously by the standard SAR processor. After range
processing, the bursts can be easily separated (Fig. 9). This
method is more efficient than single-burst processing, since
arbitrarily long FFTs can be used and zero padding (to meet the
power-of-two requirement) is negligible. Existing strip-map
SAR processors can be used like a black box, no matter how
large their internally used azimuth processing blocks are.
Also Doppler centroid estimation that uses large estimation
windows can be conveniently applied to this packed burst
dataset (Section IV-A). We denote this new efficient algorithm
by “pack-and-go.”
3) Coherent Full-Aperture Multiple-Burst Processing: A
less efficient algorithm for range and azimuth processing has
been proposed in [5]: the interburst pauses are filled with zeroes
such that the burst train resembles a standard strip-map-mode
raw dataset where some range lines (the pause lines) have been
deliberately set to zero. The resulting coherent burst train (as
depicted in Fig. 9, top) is then processed in range and azimuth
just like a standard strip-mode raw dataset. Also, this algorithm
can use existing strip-map SAR processors conveniently at the
expense of reduced throughput. The resulting image is already
sampled at the final sampling rate PRF, and oversampling of
burst images is not required.
D. Azimuth Processing
1) SPECAN: The classical SPECAN algorithm is computationally efficient and can be made phase-preserving. A focused burst image, however, exhibits a range-dependent azimuth
03.11 rad. The range mean of the
sample spacing known as fan-shape distortion and, hence, requires an additional interpolation step.
2) Azimuth-Variant Filtering: After a burst has been range
processed, standard azimuth compression is applied. The result is a coherent superposition of several shifted replicas of
the desired burst image. A time-domain azimuth band-pass filter
whose center frequency varies with azimuth position is required
finally to separate the correct result from the aliased one [6].
3) ECS: The high-precision ECS algorithm [7] is a combination of chirp scaling for accurate range processing and
SPECAN for azimuth processing. An azimuth scaling step is
added to equalize the frequency rates of the azimuth chirps.
Due to this trick, 1) fan-shape distortion of the classical
SPECAN is avoided; 2) no interpolation is required; and 3) the
final azimuth sampling grid can be chosen arbitrarily. This also
allows for mosaicking the processed subswaths (possibly taken
at different PRFs) to the complete ScanSAR image without
further interpolation.
4) Time-Domain AC: Since the bursts are usually short (e.g.,
100 range lines), a straightforward time-domain azimuth correlation is an alternative to frequency domain methods. Although
not as efficient, it is most easily implemented and allows for
a free choice of the output sample spacing. We used this flexible method to process the RADARSAT ScanSAR data (Table I)
used in this paper.
5) Coherent Multple-Burst Processing: If the bursts have
been range processed according to the above-mentioned
coherent multiple-burst range processing technique (Section V-C3), standard azimuth compression can be applied to the
burst train. The result is equivalent to a coherent superposition
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Fig. 13. Phase and amplitude of the two-beam RADARSAT ScanSAR interferogram.
HOLZNER AND BAMLER: BURST-MODE AND SCANSAR INTERFEROMETRY
1927
Fig. 14. Stability of the complex interferogram phase. The phase stability increases (brighter values) in the beam overlap area.
of the (correctly coregistered and oversampled) individual burst
images [5]. As a consequence, the azimuth impulse response
function exhibits the mentioned strong interference modulation
(Fig. 5).
small analysis windows to consider
constant (azimuth spectral shift is discussed in Section VI-G). The cross correlation of
and
is given by
the two processes
VI. BURST-MODE AND RADARSAT SCANSAR
INTERFEROMETRIC PROCESSING
is the Dirac distribution, and the asterisk “ ” dewhere
notes complex conjugate.
(master) (slave),
The focused strip-map SAR data ,
is obtained by convolution with the end-to-end system impulse
response function from (9) by the following integral:
With the complex burst-mode images available, there are
two options for further interferometric processing that will
be discussed in the following: single-burst and multiple-burst
interferograms. We will derive the power spectral densities of
the different types of burst interferograms. In order to simplify
the equations, we will again restrict ourselves to the azimuth
direction of the SAR signal. As a reference, we start with a
review of spectral properties of strip-map interferograms.
(23)
(24)
Moreover, we use the standard definition of the coherence of an
:
interferogram pixel
(25)
A. Spectrum of Strip-Map Interferograms
We assume homogeneous targets, i.e., Gaussian stationary
scatterers with the following complex reflectivity functions
[20]:
Using (24) and Parseval’s theorem, we find for the coherence of
a strip-map signal [21]
(22)
,
, and
are independent zero-mean
The functions
white circular complex Gaussian processes. We denote the co(temporal coherence) and a
herence between both signals as
possible azimuth fringe frequency as . We assume sufficiently
(26)
the transfer functions
.
responses
are the Fourier transforms of the
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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 40, NO. 9, SEPTEMBER 2002
In order to find the power spectral density (PSD) of the
strip-map interferogram, we calculate the autocorrelation
of the interferogram and transform it
function (ACF)
into Fourier domain. The interferogram ACF is obtained by
(27)
and by using the well-known Reed’s theorem [22], we find
(28)
and
being the ACFs of
and
, respectively. For better comparison of the individual
interferograms—strip-map, single, and multiple coherent
burst—we define the following normalized version of the
interferogram ACF:
with
(29)
By inserting (28) and (25) as well as taking into account the
notation for the coherence in (26) we find
Fig. 15. ScanSAR interferogram and DEM (Figs. 13–15) cover an area of
about 110 km in azimuth and 350 km in range. It includes the city of Las
Vegas, the Colorado River, and the Grand Canyon (map: © www.expedia.com/
dayily/home/default.asp).
This coherence can be maximized by processing, i.e., azimuth
spectral shift filtering. The maximum obtainable value will be
the temporal coherence component .
Since the strip-map SAR impulse response is time-invariant,
the PSD of the SAR signals is also rectangularly shaped (assuming zero-Doppler steering):
rect
(30)
We see that the last term in the second line of (30) is directly
related to coherence magnitude and the interferometric phase
ramp. Since the ACF of the interferogram only depends on
(the time lag between the considered interferogram pixel) and
since the coherence magnitude in (26) is a constant, we consider
the interferogram process as wide-sense stationary. The Fourier
transform of (30) is then the normalized PSD of the strip-map
interferogram
(33)
Consequently, their correlation is a trifunction with bandwidth
centered around zero frequency. Thus, we obtain the fol2
lowing for the normalized power spectrum from (31) and using
(32):
tri
tri
(34)
is the
Fig. 10 illustrates this normalized PSD. Obviously,
ratio of the impulse integral of the spectral line at the center
fringe frequency and the integral of the extended triangle
spectrum.
B. Single-Burst Interferograms
(31)
where “ ” is the correlation operator.
In summary, the power spectrum of the interferogram is the
cross correlation of the power spectra of the individual channels
plus a Dirac distribution centered around the fringe frequency
with a weight equal to the square of the coherence of the two
SAR signals.
For example, consider two identical strip-map SAR systems
with a rectangular-shaped transfer function—perfectly coregistered SAR signals with the same Doppler centroid. Then, the
coherence of (26) is given by
tri
(32)
Given the individual complex burst images of the master
dataset and the corresponding ones of the slave data, small
interferograms are generated from each pair of companion
burst images. These are referred to as “burst interferograms”
or “interferometric looks.” In a second step, the burst interferograms are properly shifted in azimuth and are concatenated
or (in case of overlap) coherently added to form a full interferogram of the subswath under consideration. With the usual
ScanSAR scenarios, several ( ) of these burst interferograms
overlap at every point of the final interferogram. This leads
-look processing.
to a phase noise reduction equivalent to
Since the variation of the bandpass center frequency with
azimuth inherent to the complex burst images is the same in
the corresponding master and slave image, it cancels out in the
burst interferogram. Therefore, the burst interferograms can be
HOLZNER AND BAMLER: BURST-MODE AND SCANSAR INTERFEROMETRY
1929
Fig. 15. (Continued) Resulting ScanSAR DEM. The Grand Canyon is stretching from the middle to the right edge of the DEM. In the upper left part of the DEM,
the plane (about 20 km 20 km) hosting the city of Las Vegas is visible. The region used for quality assessment (Fig. 16) is marked by a rectangle.
2
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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 40, NO. 9, SEPTEMBER 2002
added without oversampling. The resolution of the final
-look interferogram is .
In analogy to the derivation of the strip-map interferogram
multilook averspectrum, we find the normalized PSD of
aged single-burst interferograms (Fig. 10)
tri
tri
(35)
Note that due to the multilooking of the burst interferograms, the
;
energy of the extended triangle is reduced by a factor of
hence, coherence or phase stability is larger than in the strip-map
case.
C. Multiple-Burst Interferograms
For the multiple-burst interferometric processing, the burst
images from each beam are added coherently before interferogram formation as described above. This is done automatically,
if the coherent multiple-burst range and azimuth processing algorithm has been used (Sections V-C3 and V-D5). The result is
but having the oscillating azimuth
an image with bandwidth
impulse response function of Fig. 5. From the two high-bandwidth images, we form the interferogram and obtain for the normalized PSD
tri
tri
(36)
Subsequent azimuth averaging of adjacent interferogram
and
samples, i.e., low-pass filtering with a filter
down-sampling, reduces the bandwidth and sampling frequency to that of a single-burst interferogram and removes the
oscillations in the point response function. The normalized
PSD of the filtered multiple-burst interferogram is then
(37)
We see that for a filter that picks our the center triangle spectrum,
rect
, the spectrum of the low-pass
e.g.,
filtered multiple-burst interferogram (37) and the spectrum of
the single-burst multilook interferogram (35) are equivalent.
As already mentioned, the intermediate increase in bandwidth
requires redundant sampling of each burst image and increases
the data volume input to the interferometric processor. On the
other hand, at the higher spectral resolution, the average spectrum of this data resembles strip-map spectra, and, therefore,
conventional coregistration and resampling techniques can be
applied (see Section VI-E).
D. Estimation of Coregistration Parameters
Coregistration parameters between the master and the slave
images are often estimated by cross-correlating homologous
image chips. The accuracy of mutual azimuth shift estimates
is proportional to the resolution of the images [23]. In the
, where the
case of single-burst images, this means
comes in, since at any point
independent shift
factor
estimates can be derived.
For multiple-burst images, estimation of the mutual azimuth
shift requires some care: due to the modulation of the impulse
response function, multiple local correlation maxima will occur
(Figs. 8 and 11). Also, coherence as a function of azimuth
misregistration exhibits multiple maxima.
If we can find the global maximum, the inherent resolution
becomes the determining factor for coregistration
, the expected estimashift estimation. Since
compared to
tion accuracy will be better by a factor of
the single-burst interferogram case.
An elegant method for mutual azimuth shift estimation based
on the so-called spectral diversity algorithm is proposed in [24].
It exploits the fact that the phase offset between the interferograms formed from different looks is proportional to the azimuth
registration error. Since in burst-mode interferometry the looks
are already available, spectral diversity is easily applicable.
E. Interpolation for Image Coregistration
Resampling of the slave image for coregistration with respect to the master image requires interpolation. A multipleburst image can be interpolated in the same way as strip-map
and sampling
data because its average spectral density
frequency PRF are the same as in the strip-map case.
The azimuth interpolation of single-burst images, however,
requires some care, since the time variance of the burst image
spectrum must be considered. Standard interferometric processing systems assume a constant azimuth center frequency
(Doppler centroid), while in burst-mode data the look center
frequency varies linearly with azimuth. Therefore, an azimuth-variant interpolation kernel must be used.
An efficient interpolator for single-burst images is derived in
of a point
the following. According to (16), the response
has a linear phase whose slope is proportional
scatterer at
to . To a first approximation, this looks as if a gross quadratic
phase were superimposed on the burst image. If we compensate
for that quadratic phase, the spectra will be shifted toward zero
frequency and become azimuth-invariant. Without loss of gen. Then
erality, we may assume that we process burst
FM
As a first step, we multiply the burst image by
resulting in
(38)
FM ,
FM
FM
FM
(39)
Now the linear phase in the point response is gone, and its
spectrum is of low-pass type. The residual quadratic phase
FM
, however, broadens the spectrum. Using
as the equivalent width of the point response, the
bandwidth increase is
FM
(40)
This increase of spectral width must be considered when
choosing the sampling frequency PRF of the burst image.
In the RADARSAT example presented in Section VII
HOLZNER AND BAMLER: BURST-MODE AND SCANSAR INTERFEROMETRY
(FM
2000 Hz/s), the burst bandwidth
is in the order
of 100 Hz and, hence, increases by 20 Hz.
Once we have down-modulated the azimuth-variant spectra
by the quadratic phase multiply, we can apply a standard
low-pass interpolator to map the time coordinate to the one
, which is more or less a simple
of the master image:
shift. Finally, we compensate for the gross quadratic phase
(properly mapped to the master coordinates) by multiplication
FM
.
with
The problem of azimuth-variant band-pass interpolation can
be avoided also, if the slave image is processed (or reprocessed)
such that the necessary shift operation is performed in the
processor. This is done most conveniently, e.g., by introducing
linear phase functions in the range-Doppler domain. The ECS
algorithm (see Section V-D3 and [7]) further allows a straightforward implementation of a linear azimuth scaling factor by
properly manipulating the chirp functions involved [24].
F. Variation of Coherence With Varying SNR
We do not correct for scalloping, and we obtain a varying
signal-to-noise ratio (SNR) in the azimuth direction [25]. This
variation might also be seen in the phase or coherence images,
since the coherence depends on the SNR of the SAR images
[21], [26]. With our dataset, however, this effect is negligible,
since temporal decorrelation dominates.
G. Azimuth Spectral Shift
A mutual azimuth spectral shift in SAR images is a consequence of an azimuth terrain angle or converging orbits [6], [20].
The azimuth fringe frequency is given by the following equation
[6]:
Hz
(41)
(the perpendicular baseline), (look angle of the
with
(azimuth terrain angle). For RADARSAT at
radar), and
a look angle of 40 and a perpendicular baseline of 100 m,
we find that the terrain angle must be as high as 22 for a
fringe frequency in the order of a tenth of the burst bandwidth.
Hence, for our dataset this effect is negligible. However, for
the very small bandwidth of the ENVISAT Global Monitoring
mode, the azimuth spectral shifts are significant and should be
compensated [6].
H. Beam Alignment
A ScanSAR dataset can comprise 2–5 beams as planned
for ENVISAT/ASAR operation [3]. The overlap area can be
used for calibration of the ScanSAR data as described in [27],
and in case of swath interferogram combination, it can be used
for beam coregistration verification and mutual phase offset
determination.
After careful ScanSAR dataset coregistration and interferogram formation, there are still tiny misregistrations (in the
order of 1/100 sample) that lead, together with a high Doppler
centroid, to slowly varying phase errors. These are usually
accounted for by suitable orbit trimming. However, the misregistrations lead to a mutual phase offset in the overlap area that
has to be measured and compensated before beam combination.
1931
Note that orbit trimming accounts for both beams, while the
residual misregistrations may be different in the individual
beams. In addition, the range fringe frequency will introduce a
phase offset in the overlap area due to mutual misregistration
of the beam interferograms [28]. The mentioned phase offset
effects can be summarized by the following equations:
PRF
az
az
az
(42)
rg
rg
rg
(43)
and
RSF
In (42) and (43), the spectral center frequencies are the Doppler
and the center of the range spectrum
centroid frequency
(range spectrum off-set frequency [see(45)]. They transform the residual misregistrations az ( az ) and rg
( rg ) for beam 1 (beam 2) into a phase error, e.g., see [29].
and
The other contributions from the fringe frequencies
occur due to the swath interferogram misalignments rg
and az in range and azimuth, respectively.
For the dataset used, by far the most significant contribution to phase error is azimuth misalignment of the ScanSAR
PRF
images, since the Doppler centroid is very high,
6.0 cycles/sample. By contrast, the gross azimuth fringe frequency (converging orbits) impact is negligible (
10 cycles/sample). The range frequencies are in the order of
0.2 cycles/sample.
To measure beam alignment, we used multiple coherent burst
images of either dataset (Table I). Since the spectra of the complex beam signals are nonoverlapping, and hence uncorrelated,
we measured the shifts on detected images.
For our particular RADARSAT dataset, we used several
hundred patches and found that the beams are well aligned in
azimuth. However, a shift of 0.15 0.02 samples is present in
the range direction. After resampling the beam 2 interferogram,
we measured a phase offset of 3.11 rad (with a standard
deviation of 1.47 rad) and subtracted it from the beam 2
interferogram before combining the beam interferograms. The
interferogram pixels were added coherently in the overlap
area. The differential phase of the swath interferograms in the
overlap area (Fig. 12) shows no systematic effects such as
trends or structure from topography originating from a mutual
misregistration. This implies that no azimuth-dependent intrabeam coregistration phase errors show up in the differential
phase, and it shows that interbeam coregistration is sufficiently
accurate, so that no topographic artifacts are visible.
Finally, we obtained a full-range interferogram by beam combination. The interferogram phase and amplitude are illustrated
in Fig. 13. It covers an impressive area of approximately 110 km
in azimuth and 350 km in range.
After burst image mosaicking, coherence information [see
(25)] is not accessible anymore. The following phase stability
measure was used instead:
(44)
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Fig. 16.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 40, NO. 9, SEPTEMBER 2002
Comparison of different DEMs (GLOBE, DTED-1, DTED-2, ScanSAR) for the area (35 km
The result for the combined interferogram is shown in Fig. 14.
As expected, we observe a reduction of phase variance in
the overlap area. This is further evidence that remaining
tiny intrabeam azimuth misregistrations do not result in
azimuth-dependent phase distortions in the individual beam
interferograms.
I. Range Spectrum Shift of RADARSAT Data
Since RADARSAT is not operated in yaw steering mode, high
squint angles occur [9], [11]. High squint angles result in an
off-zero range spectrum center frequency according to the following equation [11], [28]–[30]:
(45)
.
This offset varies considerably from near range to far range,
from 0.95 MHz to 3.8 MHz, due to the high Doppler centroid variation (Fig. 6). Since the bandwidth of the dataset is
only 11.58 MHz, the center frequency of the spectrum is significantly shifted away from zero and complicates spectral shift
filtering and resampling.
J. Orbit Correction
RADARSAT orbit data have very low accuracy; therefore,
orbit correction and baseline estimation become necessary [11],
[31]. The along- and circular across-track accuracies are given
in [31] as 100 m and 20 m, respectively.
Orbit correction was split into two tasks. First, the primary
channel orbit was corrected for shifts in slant range and azimuth
2 35 km) marked in Fig. 15.
direction. Second, the baseline length and angle were optimized, and the secondary channel orbit was corrected.
The timing shift for the primary channel orbit was estimated
using an interferogram simulated from a high-quality DEM
(DTED-2, 25-m sampling grid). For this task, usually ground
control points such as corner reflectors or transponders are
used. However, for the considered region, no ground control
points were available. After maximizing the peak of the differential interferogram spectrum (measured – simulated) in rough
terrain, i.e., minimizing the fine structures on the differential
phase, we found a shift of 310 ns in range and 4.87 ms in
azimuth.
Baseline can be estimated by minimizing the difference between a simulated and the measured interferogram. In order to
avoid phase unwrapping in advance of baseline optimization
(low coherence, Grand Canyon, and mountainous areas), we
proceeded as follows. We simulated the interferometric phase
and in the
for several baseline length and angle settings
neighborhood of the initial interferometer configuration
167 m and
159 . For this task, we used a DTED-2 DEM
(25-m sampling grid), since we did not want to blame ScanSAR
Interferometry for the bad orbit of RADARSAT. We generated
a matrix of differential or flattened ScanSAR interferograms
and considered the corrected baseline optimum when we obtain an overall flat phase. For this kind of baseline optimization, the wide range extent of the ScanSAR data is favorable,
since it limits the space of possible correction parameter vectors
,
) that correct for the smooth phase
(
function on the difference phase. We obtained
4.22 m, 0.0075 rad for the baseline error estimate.
HOLZNER AND BAMLER: BURST-MODE AND SCANSAR INTERFEROMETRY
1933
After baseline estimation (Section VI-J) we subtract an
interferogram simulated from the GLOBE DEM prior to phase
noise smoothing and phase unwrapping. For phase filtering,
we use a Gaussian low-pass filter with a bandwidth that we
matched to the fringe frequency distribution of a representative
area in the interferogram. This improves the unwrapping
performance of the applied minimum-cost-flow algorithm
[32]. Fig. 15 shows the final ScanSAR 75–m resolution DEM
[33]. Through the combination with GLOBE, the final result
contains also information of the coarse DEM in low coherence
areas (dark in Fig. 14). These areas are restricted to extreme
slopes and severe temporal decorrelation.
The DEM is compared to a high-quality DTED-2 (25-m
grid). In flat areas, we find a height standard deviation of
about 3–5 m. This result matches well the expectations from
coherence, which varies from 0.72–0.85 in such regions.
Fig. 16 shows a comparison of the ScanSAR DEM with several other DEMs. The area for comparison has an extent of
35 km 35 km and altitudes ranging from 640–1860 m. The
other DEMs are GLOBE, DTED-1, and DTED-2, available at
sampling grids of 1 km, 100 m, and 25 m, respectively. The
ScanSAR DEM in the lower right corner certainly improves the
GLOBE and even adds detail to the DTED-1. It is remarkable
how distorted the DTED-1 is for the chosen area around Las
Vegas. The DTED-2 serves as the reference in this comparison.
The histograms of the height differences of the GLOBE,
DTED-1, and ScanSAR DEM with respect to the reference
DTED-2 are shown in Fig. 17.
The 1-km GLOBE DEM appears to perform well, since the
chosen area contains sufficiently large patches of flat terrain.
Also, a visual comparison suggests that the GLOBE DEM of
this area had probably been produced from the DTED-2 data.
The band-like artifacts in DTED-1 result in a bimodal error histogram. Besides that, the error of the ScanSAR DEM is much
less than that of DTED-1, and its visually perceptible resolution
is much better and resembles more DTED-2 than DTED-1.
VIII. CONCLUSION
(d)
Fig. 17. Histograms of the height differences (a) GLOBE—DTED-2,
(b) DTED-1—DTED-2, and (c) ScanSAR—DTED-2.
VII. SCANSAR DEM EXAMPLE
In this section, we describe the last steps of the processing
of the ScanSAR interferometric data, the resulting DEM, and a
comparison to other available DEMs of the test site.
ScanSAR will be increasingly used in future spaceborne SAR
systems. ScanSAR interferometry is a complex task because
1) the properties of the involved signals are quite different from
those of conventional SARs and 2) some of the approximations
accepted for standard InSAR processing have to be revised due
to the large swaths covered by ScanSAR.
This paper gives a complete overview of the interferometric
properties of ScanSAR signals and their consequences for processing. The different processing algorithms we have introduced
and discussed should allow the reader to choose the one that is
most efficiently integrated into an existing strip-map InSAR
processing chain. The step-by-step recipe-like explanation of
processing a RADARSAT ScanSAR data pair to a large DEM
included descriptions of many additional precautions and tricks
that have to be considered in ScanSAR interferometry. It was
shown that DEMs of at least DTED-1 quality can be generated by
RADARSAT repeat-pass ScanSAR interferometry in the case of
moderate terrain relief and sufficiently high temporal coherence.
ACKNOWLEDGMENT
The authors would like to thank B. Schättler, H. Breit,
U. Steinbrecher, A. Moreira, and J. Mittermayer (all of DLR)
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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 40, NO. 9, SEPTEMBER 2002
for their contributions to the ScanSAR processing software;
M. Eineder (DLR) for helpful technical discussions and for
making available an efficient interferometric phase simulator;
W. Knöpfle (DLR) for mosaicking 550 DTED-2 DEM tiles
and for his support with geocoding tools; and I. Cumming
(University of British Columbia, Vancouver) for sharing his
experience and advice.
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Jürgen Holzner (S’02) was born on August 9, 1973.
He received the Diploma degree in data and information engineering from the Georg-Simon-OhmFachhochschule University of Applied Sciences,
Nuremberg, Germany, in 1997. He is currently
pursuing the Ph.D. degree at the University of
Edinburgh, Edinburgh, U.K.
In 1997, he joined the German Aerospace Centre
(DLR), Oberpfaffenhofen. He has worked since then
on processing techniques for SAR interferometry.
The main projects he worked on included differential interferometry for subsidence monitoring, RADARSAT-1 ScanSAR
interferometry, evaluation of SAR interferometry-derived digital elevation
models (DEMs), and contributions to the SRTM interferometric processor.
His current research interests are in ENVISAT IASAR alternating polarization
mode interferometry, baseline optimization, DEM evaluation, and comparison
using multiresolution methods, as well as ScanSAR processing algorithms and
spectral estimation algorithms.
Richard Bamler (SM’00) was born on February 8,
1955. He received the Diploma degree in electrical
engineering, the Eng. Dr. degree, and the “habilitation” in the field of signal and systems theory in 1980,
1986, and 1988, respectively, from the Technical University of Munich, Munich, Germany.
He was with the Technical University of Munich
during 1981 and 1989 working on optical signal
processing, holography, wave propagation, and
tomography. He joined the German Aerospace
Center (DLR), Oberpfaffenhofen, in 1989, where he
is currently Director of the Remote Sensing Technology Institute. Since then he
and his group have been working on SAR signal processing algorithms (ERS,
SIR-C/X-SAR, RADARSAT, SRTM, ASAR), SAR calibration and product
validation, SAR interferometry, phase unwrapping, oceanography, estimation
theory, model-based inversion methods, parameter retrieval methods for
atmospheric spectrometers (GOME, SCIAMACHY, MIPAS), content-based
image query systems, and data mining for remote sensing archives. In early
1994, he was a Visiting Scientist at the Jet Propulsion Laboratory, Pasadena,
CA, in preparation of the SIC-C/X-SAR missions, where he worked on
algorithms for the SIR-C ScanSAR, along-track interferometry, and azimuth
tracking modes. His current research interests are in algorithms for optimum
information extraction from remote sensing data with emphasis on SAR. He is
the author of a textbook on multidimensional linear systems.