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3468 IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 45, NO. 11, NOVEMBER 2007

ScanSAR-to-Stripmap Mode InterferometryProcessing Using ENVISAT/ASAR Data

Ana Bertran Ortiz, Student Member, IEEE, and Howard Zebker, Fellow, IEEE

Abstract—Interferometric synthetic aperture radar (InSAR)images of geophysical events such as preeruptive volcano de-formation or interseismic strain accumulation are often limitedby phase distortions from the superimposed atmospheric signa-ture. Additionally, the approximate monthly repeat cycle of manyradar satellites cannot accurately capture rapidly time-varyingprocesses. The Scanning Synthetic Aperture Radar (ScanSAR)mode of the ENVISAT/ASAR instrument permits more frequentrevisits of a given area, potentially overcoming both of theselimitations. In particular, stripmap mode-to-ScanSAR images pro-vide a denser time series of interferograms than is possible withconventional stripmap-to-stripmap mode InSAR. We present im-ages of ENVISAT/ASAR data acquired over Hawaii in whichdata acquired roughly weekly in ScanSAR mode are combinedwith ENVISAT/ASAR conventional stripmap mode data to forminterferograms at a much denser temporal spacing. The burstnature of ScanSAR data requires a new processing method toform the interferograms. We use traditional matched filtering forthe range compression. For the azimuth processing, we computethe stripmap mode data on the ScanSAR sampling grid using avariation, consisting of different reference functions, of Lanari’smodified SPECAN algorithm that is itself an adaptation of thechirp z-transform to readjust the azimuth pulse spacing. The re-sulting interferograms faithfully reflect the phase of conventionalinterferograms, but exhibit fewer looks and coarser resolutionthan those produced by fully stripmap mode data. For manyproblems, temporal density of the deformation observations isparamount, and the time series analysis and temporal averagingthat were made possible using ScanSAR interferograms far out-weigh the loss in looks and resolution.

Index Terms—Chirp z-transform, ENVISAT, InterferometricSAR, modified SPECAN, SAR, scanning Synthetic ApertureRadar (ScanSAR), SPECAN.

I. INTRODUCTION

SUBTLE crustal deformation phenomena such as inter-seismic strain accumulation or preeruptive deformation of

volcanoes can produce small-magnitude surface deformationsignatures that go undetected in single radar interferograms.Interfering factors such as phase distortions from propagationthrough the spatially variable troposphere can easily obscureuseful signals. These errors may be minimized by reducing

Manuscript received September 26, 2006; revised February 9, 2007. Thiswork was conducted at Stanford University under contract with the NationalAeronautics and Space Administration and the National Science Foundation(NSF) under Grant 0309425.

A. Bertran Ortiz is with the Department of Electrical Engineering, StanfordUniversity, Stanford, CA 94305-9505 USA.

H. Zebker is with the Department of Electrical Engineering, StanfordUniversity, Stanford, CA 94305-9505 USA, and also with the Department ofGeophysics, Stanford University, Stanford, CA 94305-9505 USA.

Color versions of one or more of the figures in this paper are available onlineat http://ieeexplore.ieee.org.

Digital Object Identifier 10.1109/TGRS.2007.895970

Fig. 1. Stripmap acquisition in turquoise versus ScanSAR acquisition inyellow. During ScanSAR mode, the satellite electronically steers the beambetween subswaths, whereas during stripmap mode, the beam is maintainedwithin a single subswath.

the time between acquisitions and increasing the number ofacquisitions available for time averaging. Acquiring deforma-tion at dense time sampling is also critical in the observation ofrapidly time-varying processes. Most operating radar satelliteshave repeat periods on the order of one month, and a shorterrevisit interval would help with both of these limitations.

The ENVISAT/ASAR instrument, with its electronic beam-steering capability, permits multiple observations of an areawithin a single orbit cycle whereby a region of interest isobserved by the sensor on several different orbit tracks. Inaddition, rapid beam switching in Scanning Synthetic ApertureRadar (ScanSAR) operation enables the imaging of very wideswaths. For ENVISAT/ASAR, using ScanSAR, each point onthe ground can be imaged five times during a typical 35-daycycle. The general principles of the stripmap and ScanSARoperation modes are illustrated in Fig. 1.

During stripmap mode, a given target is seen over a contin-uous range of azimuth angles φ ∈ ((−α/2), (α/2)), where αis the antenna beamwidth. In ScanSAR, the target is seen onlythrough a subset of these viewing angles (Fig. 2). To producecoherent interferograms, there must be significant overlap ofthe azimuth viewing angles for the two acquisitions. Sincealong-track timing and control of an orbital SAR is inexact,forming ScanSAR-to-ScanSAR interferograms is difficult. To

0196-2892/$25.00 © 2007 IEEE

BERTRAN ORTIZ AND ZEBKER: SCANSAR-TO-STRIPMAP MODE INTERFEROMETRY PROCESSING 3469

Fig. 2. Ground-projected view of a radar viewing a single point scatterer. Instripmap mode, the point scatterer is seen by the radar through a continuous setof azimuth viewing angles φ, whereas in ScanSAR mode, the point scattererwill only be seen by a discontinuous subset of azimuth angles. (a) Stripmapmode. (b) ScanSAR mode.

Fig. 3. Block diagram of SPECAN.

ensure sufficient overlap, the satellite’s azimuth position at thetwo acquisition instances would need to be known precisely andthe radar pulse timing tightly controlled.

Stripmap-to-ScanSAR interferometric pairs, on the otherhand, always overlap, provided the two Doppler centroids aresimilar enough to ensure good signal-to-noise ratio (SNR) whenboth data sets are processed at the same centroid. We can selectthe pulses in the stripmap image corresponding to the availableScanSAR pulses for each ScanSAR burst. However, since thestripmap image covers a narrower swath than the ScanSAR,stripmap-to-ScanSAR interferograms see a given point on theground less often than ScanSAR-to-ScanSAR interferograms,but still more often than stripmap-to-stripmap interferograms.Thus, stripmap-to-ScanSAR interferometric pairs offer a feasi-ble compromise with more frequent coverage than is possiblein conventional stripmap-to-stripmap interferometric syntheticaperture radar (InSAR). In exchange for the increase in fre-quency of coverage, we trade off interferogram quality sinceburst-mode images exhibit fewer looks than stripmap images.

Several algorithms have been used to process simulatedburst-mode images, including SPECAN [2], traditional range-Doppler after converting the ScanSAR image to a stripmapequivalent through the addition of lines of zeros [3], chirp scal-ing [4], [5], extended chirp scaling [6], and modified SPECAN[1]. An overview of some of these methods can be found in[7] and [8].

The SPECAN algorithm, which consists of an azimuth de-ramping function followed by a fast Fourier transformation(FFT), as illustrated in Fig. 3, was first publicly documentedas an alternative to the fast-convolution algorithm or traditionalrange-Doppler processor in [2]. However, the technique and itsconcepts date back to the 1970s [9]; it was implemented in 1979at MacDonald Dettwiler and European Space Agency’s (ESA)Space Research and Technology Center for a real-time SAR

Fig. 4. Block diagram of the modified SPECAN.

processor. The algorithm requires relatively low computationrates and is suitable for low-resolution imaging. However, whenapplied to azimuth processing, output sample spacing varieswith the Doppler rate and thus with range, which introduces theneed for interpolation, effectively negating any computationaladvantage. In 1998, the modified SPECAN algorithm wasintroduced [1], consisting of the same deramping function butwith the FFT replaced by a chirp z-transform (Fig. 4). Throughthe chirp z-transform, an arbitrary pulse spacing can be chosen,removing pulse spacing dependence on the Doppler rate. Thisapproach eliminates the need for interpolation and leads to amore efficient implementation.

Here, we adapt the chirp z-transform method further toaddress inherent sampling and registration issues faced whencombining ENVISAT/ASAR stripmap with ScanSAR data. Thestripmap and ScanSAR modes of ENVISAT/ASAR instrumentuse different pulse repetition frequencies (PRFs), and the sev-eral ScanSAR beams use different PRFs as well. We resampleboth the stripmap and ScanSAR data sets to a single commonrange-independent azimuth spacing by using different chirpz-transform reference functions for each of the two data sets(denoted sref∗

chirp(t) in Fig. 4).ScanSAR data are necessarily acquired in bursts, each con-

sisting of a collection of continuous pulses from the samesubswath. The burst-to-burst repetition interval is not an integernumber of pulses, further complicating the resampling problem.We use the timing and position of the azimuth pulses from theorbit information to calculate initially which stripmap modepulses and what subpixel shifts are required to coregister theinterferometric pair. These estimates are close but not suffi-ciently accurate for high-quality imaging, so as a next step, weuse the residual phase ramp due to misregistration to refine thesubpixel offsets. In this manner, a high-quality interferogramcan be produced.

To our knowledge, the first ScanSAR-to-ScanSAR andstripmap-to-ScanSAR interferograms using actual data underfavorable interferometric conditions are presented in [10] forRADARSAT and [11]–[15] for ENVISAT/ASAR. We createScanSAR-to-stripmap interferograms from actual raw data andaccount for differences in PRF and azimuth coregistration.As can be seen from the interferograms shown in [14] and[15], processing of such interferograms can lead to phaseramp artifacts across azimuth if the images are not carefullycoregistered in azimuth. Appendix A provides a table with keyparameters for those stripmap and ScanSAR modes that can beinterferometrically combined.

The remainder of this paper is organized as follows. InSection II, we derive the response from a single scatterer asseen in both passes of an interferogram, and in Section III,we discuss in detail the processing steps for the generation


Fig. 5. Azimuth sampling of a particular subswath in stripmap and ScanSARmodes. NB is the number of samples in a burst and NR is the burst-to-burstrepetition period in samples. In ScanSAR mode, the radar acquires data fromother subswaths in between the two bursts shown.

of a stripmap mode-to-ScanSAR interferogram. Finally, inSection IV, we apply the method to data acquired over theisland of Hawaii and discuss the interferogram quality as com-pared to conventional InSAR.

II. PHASE HISTORY AND PROCESSING OF

STRIPMAP AND BURST-MODE DATA

A. Phase History of Stripmap and Burst-Mode Data

The technical challenges associated with creating a stripmap-to-ScanSAR interferogram result from the differences in burst-mode data acquisition as compared to stripmap mode imaging.As illustrated in Fig. 5, stripmap mode data are acquired using acontinuous series of pulses, whereas ScanSAR data consist of alimited number of pulses forming a “burst,” where subsequentbursts may belong to different subswaths. Selecting only thebursts from a particular subswath results in missing data pulsesin between bursts, in contrast to continuous acquisition overthe subswath.

The two acquisitions may also differ in PRF. If the PRFdiffers, pulses within one burst in the ScanSAR image cannotalign with stripmap image pulses. Additionally, the repeat timebetween bursts will generally not be an integer number ofpulse periods.

Consider first the phase history of a point scatterer as viewedby a radar (see Fig. 6). Assume the antenna orientation to beperpendicular to the flight direction, let t be the current azimuthtime and t0 be the time at which the scatterer will be imagedat the center of the beam, that is, at zero Doppler. Let x′ bethe distance between a scatterer and the center of the beam,then x′ = v(t − t0), where v is the velocity of the radar. Let r0

be the distance to the scatterer when it is at the center of thebeam. Omitting the amplitude term, after range processing ofany given return, the signal in azimuth from the single scattererat location vt0 with the radar at vt will be

s(t, t0) = ejπfR[t2−2t0t+t20]e−4π

λ r0 (1)

where fR = (−2v2/λr0) is the azimuth Doppler rate(Appendix B).

The end goal of the azimuth compression is to coherentlysum returns due to the scatterer being at offset x0 = vt0, while

Fig. 6. Geometry for a single scatterer assuming a straight radar path andplane Earth geometry. We have defined t as the current azimuth time at whichthe point scatterer is imaged, t0 as the time at which the scatterer will be imagedat the center of the beam, x′ as the distance between the target and the centerof the beam, and v is the radar velocity. The range to the point scatterer at thecurrent time is r(t) and the range to the point scatterer when it is at the centerof the beam is r0.

the returns from scatterers not at location vt0 cancel due toimproper compensation. The sum should span the pulses inwhich the scatterer lies within the antenna beam. Traditionalrange-Doppler processing [16] uses a single matched filter,where through correlation, several reference signals sref , asshown in the following equation, are effectively created fordifferent values of vt0, and the one with the peak response ischosen:

sref(t, t0) = e−jπfR[t−tref ]2 , |t − tref | <S

2(2)

where S is the integration interval—the time the scatterer staysin the beam.

Assuming a large enough time bandwidth product for thestationary phase approximation to apply, the magnitude of theresult of the matched filter operation [16] is then

|h(t, t0)| = |sinc (−fRS(t′ − t0))| (3)

where sinc(x) = sin(πx)/πx. The peak of the result, which isthe center of the sinc, is at t′ = t0 and the width is

− 1fRS

. (4)

This approach to matched filtering works well for signals instripmap mode where the target will be seen through the fullbeam aperture. However, in ScanSAR, as mentioned previously,the scatterer will only be seen through a subset of azimuthangles. Due to the limitation in ScanSAR viewing angles, weshould not apply the traditional matched filter where S, which isthe integration interval, is the time the scatterer stays within thewhole aperture. In addition, if we apply the traditional matchedfilter, the output data will be sampled at a rate determined bythe PRF, which may differ from stripmap to burst-mode.

It is possible to form an image by applying the traditionalmatched filter by filling in the missing pulses with zeros andusing the full aperture integration time. The data may beinterpolated if the pulses from one burst do not align properly


with other bursts. However, coherent addition of multiple burstsresults in grating azimuth sidelobes with poor resultant imagequality. Thus, the SPECAN [9], modified SPECAN [1], or chirpscaling [5] algorithms are usually used for processing burstdata. These methods for processing ScanSAR data are alsomuch more computationally efficient than zero-filled range-Doppler processing [7].

B. Processing Using SPECAN and Modified SPECAN

We next summarize the SPECAN and modified SPECAN al-gorithms. A derivation of the SPECAN and modified SPECANthat assumes a pulse spacing of the azimuth resolution can befound in [1]. Here, we include a short rederivation assumingthat pulses are spaced at the natural spacing instead of theresolution size, which will be useful for subsequent sections.Consider (1) and note that the signal has a quadratic phase termand a linear phase term that need resolving to form the matchedfilter. To eliminate the quadratic phase term, we multiply thesignal by the complex conjugate of a reference quadratic term,a step often referred to as first-order deramping [9]. If thederamping reference term is centered at boresight (see [2] foran arbitrary location) then the deramped signal is

sderamp(t, t0) = s(t, t0)e−jπfRt2 = ejπfR[−2t0t+t20] (5)

where we have ignored the azimuth-independent propagationdelay phase term and we still assume that the radar lookdirection is perpendicular to the flight path. If this were not thecase, here we would also need to compensate for the average ofthe stripmap and ScanSAR data Doppler centroids.

After deramping, the linear term remains along with a con-stant phase term. That is, we have a constant frequency sinusoiddependent on azimuth location, and FFT methods are oftenemployed to estimate the frequency and hence the scattererposition [9]. As many have noted [2], and as can be observedfrom (5), this linear term results in a linear relation (f = fRt0)between frequency and t0, through the Doppler rate.

We discretize (5) by setting t = kTs − (NBTs/2), where kis the azimuth sample number, and Ts is the azimuth samplingperiod, or (1/PRF). We set t0 = mTs, that is, the target will bem samples away from the center of the beam. Following from(5), where sderamp is the azimuth signal after deramping, weobtain the following discrete form:

sderamp[k,m] = ejπfR

[

−2(mTs)(kTs−NBTs

2 )+(mTs)2]

. (6)

Consider the discrete Fourier transform (DFT) of sderamp,where the DFT of a signal x[k] is defined as

DFT (x[k]) =N−1∑

k=0

x[k]e−j 2πkqN . (7)

Then

DFT (sderamp[k,m]) = ejπfRm(TS)2NBejπfR(mTS)2

·NB−1∑

k=0

e−j2πfRmT 2Ske−j 2πkq

NB (8)

Fig. 7. Discrete form of chirp transform algorithm block diagram, where x[k]is the input and y[k] is the output.

Fig. 8. Changing the ς factor in the chirp z-transform reference functionaffects output sample spacing. The two targets are originally centered atlocation +25 and −25. After deramping and chirp z-transform, we obtain theabove 1-D impulse responses. As expected, (left) setting ς to 1 results in targetsat 25 and −25, whereas (right) setting ς to 0.5 results in targets at locations 50and −50. (a) Setting ς to 1. (b) Setting ς to 0.5.

or (after integration)

DFT(sderamp[k,m])= ejπfR(mTS)2

(

NBm +1

)

· e−j(NB−1) πNB

(q+fR(mTS)(NBTS)) · NB

· sincNB

{

2π

NB[q+fR(mTS)(NBTS)]

}

(9)

where sincM (w) = (1/M)(sin(M(w/2))/ sin(w/2)) is thediscrete version of the sinc function, q is the discrete azimuthfrequency variable, NBTS is the length of the burst aperture,and TS is (1/PRF). According to (9), each signal peak willbe located at bin −fR(mTS)(NBTS). As remarked in [17],(9) further shows that the constant phase offset depends onthe distance from the scatterer to the center of the beam(t0 = mTS).

The resulting azimuth bin spacing after the DFT varies inthe range direction due to the dependence of fR on r0. Thus,the image will be geometrically distorted. Interpolation can beused to overcome the range dependence; however, this is avery inefficient processing step. Instead, we can use the chirpz-transform rather than the DFT to eliminate the r0 depen-dence [1]. The chirp z-transform is equivalent to a DFT witha controllable output bin spacing. The concept of the chirpz-transform dates back to 1969 [18], and the principle is shownin Fig. 7.

After the chirp z-transform, we obtain samples at pointswq = w0 + q∆w, where ∆w is defined in the chirp z-transformreference function as shown in Fig. 7. Without loss of


Fig. 9. Processing steps. In the block diagram, srefderamp(t) is the reference function used for deramping and sref

stripmap(t) is the reference function used for the

chirp z-transform in the stripmap case, whereas srefScanSAR(t) is the reference function used for the chirp z-transform in the ScanSAR case. The signals sderamp

represent the data after deramping, whereas CZT(sderamp) are the data after the chirp z-transform.

generality, we can set w0 to zero. The chirp z-transform withwq = 2πq/NBTS is equivalent to a DFT [19], and thus theoutput would be (9). For our purposes, we set wq = 2πςfRq′,thus the phase expression e−j∆w((kTs)2/2) in Fig. 7 becomese−jπςfR(kTs−(NBTs/2)2). The fR in wq compensates for therange-dependent spacing that results from a DFT. We haveexplicitly introduced the scale factor ς to control the azimuthpulse spacing (see example in Fig. 8).

Starting from the DFT output expression in (9), we employthe chirp z-transform by substituting NBT 2

SςfRq′ for q toobtain the following:

CZT(sderamp[k,m])= ejπfR(mTS)2

(

NBm +1

)

· e−jπNB−1

NB(NBT 2

SςfRq′+fRmTSNBTS)

·NB ·sincNB

[

2π(

TS2ςfRq′+fRmTS

2)]

(10)

= ejπfR(mTS)2

(

NBm +1

)

· e−jπ(NB−1)T 2SςfR(q′+ m

ς )

· NB · sincNB

[

2πT 2SςfR

(

q′ +m

ς

)]

.

(11)

Comparison of (11) with (9) now shows constant azimuthbin spacing with range, since the center of the scatterer’ssignal, which is the center of the sinc, no longer depends onfR. Each scatterer’s signal is now centered at q′ = −(m/ς).

In addition, through the ς factor, the azimuth grid can be chosenas desired.

III. PROCESSING STEPS

In this section, we describe the SAR and InSAR processingsteps we use to form a stripmap-to-ScanSAR interferogram.Theoverall block diagram is illustrated in Fig. 9. The blocks de-noted by “(∗)” are steps identical to those in a range-Dopplerprocessor. In the following sections, we will elaborate on theremaining blocks, those that had to be specifically designed forour method.

We use deramping and the chirp z-transform for azimuthprocessing after matched filtering for range processing. Here,we define one of the SAR images from the interferometric pairas the “master” to whose grid the “slave” image is resampled.We choose the stripmap image as the slave because its continu-ous azimuth sampling makes azimuth coregistration simpler.

A. Coregistering the Stripmap Image to the ScanSAR One

In our method to coregister the two acquisitions in azimuth,we have to determine a coarse azimuth offset as well as afine azimuth offset. We define the coarse azimuth offset as theinteger raw pixel difference of the index of the first azimuth linein the first burst in the ScanSAR image to its correspondingpulse in the stripmap image. The fine azimuth offset is thesubpixel azimuth offset between each corresponding pixel in


Fig. 10. Different ς values amount to different azimuth pulse spacings. (a) The “short lines” that result when the pulses are processed to the natural pulse spacingare shown here. (b) The same targets processed to have zero overlap among consecutive bursts are shown here. (c) The targets processed to see a full antennabeamwidth within one burst are shown here. With this option, the same target will be seen in the maximum number of consecutive bursts. (a) Natural pulse spacing[ς = 1 · (Re/(Re + h))]. (b) Pulse spacing without overlap between bursts [ς = (NR/NB) · (Re/(Re + h))]. (c) Pulse spacing to cover antenna beamwidthwithin one burst [ς = (λr/LNB) · (PRF/v)].

the two images. The fine azimuth offset varies in both the rangeand azimuth directions.

We initially estimate the coarse azimuth offset from theorbital ephemeris and imaging geometry. Lack of precision ofthe squint calculation, the angle from antenna pointing directionto the flight perpendicular, and in the ephemeris parameterslimits the accuracy of this initial estimate.

In common InSAR algorithms, the initial estimate of thecoarse azimuth offset is refined by comparing observed fea-tures in the master and slave images. The images are usu-ally processed to the natural azimuth pulse spacing ∆x =(v/PRF) · (Re/(Re + h)), in which we account for curvedorbits and the projection of orbital distances to the ground. Inthe equation for ∆x, Re is the radius of the Earth and h isthe satellite altitude. In ENVISAT/ASAR ScanSAR, since thebursts are limited to 50 pulses for ScanSAR beam SS1, theprocessed image resolution is about 20 times coarser than forcontinuous mode imaging, and such offset estimates are thatmuch poorer. One ScanSAR resolution cell (resel), which is theminimum resolvable entity, spans approximately 25 naturallyspaced pixels, whereas in the stripmap case, one resel spansapproximately one and one half naturally spaced pixels.

We use correlation methods as a next step to refine theinitial coarse offset. Once the azimuth coarse offset to thestripmap image is determined, we use cumulative pulse timinginformation from the stripmap and ScanSAR data to identifyburst start indexes for the stripmap data, chosen to match theseries of ScanSAR bursts. We account for missing lines bycopying adjacent lines when a line is missing and keeping trackof the effect on the timing. The start indexes will not necessarilybe integers since the burst-to-burst repetition interval is not aninteger number of pulses, as mentioned in Section II. Once thecoarse offsets for each burst are determined, we copy into thecomputer memory each ScanSAR burst and the stripmap datacorresponding to each ScanSAR burst.

For fine azimuth coregistration, we make use of the phaseramps present across a burst in ScanSAR mode in incor-rectly coregistered images (as shown later) [17]. Correlationmethods for the fine registration of stripmap-to-stripmap,whose accuracy depends on the resolution cell size, will notsuffice here since the inherent resolution of the ScanSAR datais 20 times worse than the stripmap data. For the European Re-mote Sensing (ERS) satellite, it has been estimated [20] that forsufficiently correlated images, the accuracy of the offsets usingincoherent cross correlation will be about 20 cm in azimuth,which corresponds to 1/30th of a stripmap azimuth resolutioncell. The accuracy of incoherent cross correlations for ScanSARis then only 4 m, which represents one raw data pixel groundazimuth distance. Thus, incoherent cross-correlation methodsare sufficient for determining coarse offsets but not fine offsets.The misregistration, if any, can be inferred much more accu-rately by estimating the slope of the azimuth phase ramps andis a key part of our method. In the past, the spectral diversitymethod [17] was used to coregister ScanSAR-to-ScanSARimages. This method is an alternative to estimating the phaseramps in each burst. Comparison of both methods is beyond thescope of this paper.

Our phase ramp algorithm is iterative, where we first assumeno misregistration, focus the images, form the interferogram,and estimate the phase ramp from which we infer the misreg-istration present. The process may be repeated to maximizeaccuracy.

To understand the phase ramp, consider the signal from ascatterer viewed from two acquisitions where one image ismisregistered in azimuth by ∆m. The resulting interferometricphase between the two acquisitions is

Φm = (Φ2 − Φ1) + πfRT 2S(∆m + ∆m2) + (2πfRT 2

S∆m)m(12)


Fig. 11. Azimuth compression and coregistration using deramping and chirp z-transform. In the block diagram, srefderamp[k] is the reference function used for

deramping, and srefstripmap[k] is the reference function used for the chirp z-transform in the stripmap case, whereas sref

ScanSAR[k] is the reference function usedfor the chirp z-transform in the ScanSAR case. The signals CZT(sderamp) are the data after the chirp z-transform. The shift required to coregister the stripmapto the ScanSAR grid is Ψ, whereas Nf represents the FFT size.

where Φ1 and Φ2 are the propagation delay phase terms foracquisitions 1 and 2.

The propagation delay phase term difference in (12) is thedesired deformation interferometric phase difference. The sec-ond term represents a constant in azimuth phase error due tomisregistration. The last term is a phase error term that dependson m, that is the target position in azimuth, and thus resultsin the azimuth phase ramp. We estimate the misregistration byevaluating the slope in Φm across a burst and readjust the outputlocations for the slave image by the estimated registration error∆m. The subpixel resampling is incorporated into the chirpz-transform step for efficiency purposes (see Section III-B). Fornonzero Doppler centroids where both images are processed tothe average of the Doppler centroids, (12) will remain the sameexcept for a constant phase in the azimuth term. This does notimpact the relation between the slope and the misregistration.

Range coregistration is done using traditional methods, sincein the range direction, the ScanSAR data are similar to thestripmap data. Feature matching for range coarse offset de-termination is sufficient. Incoherent cross-correlation methodsare adequate, while suboptimal for fine offsets determinationin range.

After this step, the stripmap image will be coregistered to theScanSAR image grid. We next proceed to focus both data setsin the azimuth direction.

B. Deramping and Chirp z-Transform

As explained in Section II, we use deramping followed by achirp z-transform to focus the images in azimuth. The following

Fig. 12. Overlap relation between two adjacent bursts. The X marks the centerof the bursts and the diagonal lines show the amount of ground area covered byeach burst.

deramping reference function is set to:

srefderamp[k] = e

−jπfR

(

kTS−NBTS2

)2

. (13)

For the nonzero Doppler centroid case, we can process theimages after multiplication with the following:

srefdop[k] = e

−j2πfdcavg

(

kTS−NBTS2

)

(14)

where fdcavg is the average of the two images’ Doppler cen-troids. The Doppler centroids are estimated using the averagephase shift from azimuth line to line. We process both sets ofdata to the same Doppler to ensure maximal correlation.


Fig. 13. Stripmap-to-ScanSAR interferogram of Hawaii’s Big Island, from 2004/9/13 to 2004/5/31 B ⊥= 74 m. (a) Amplitude and phase. (b) Phase only.

Fig. 14. Stripmap-to-ScanSAR interferogram of Hawaii’s Big Island, from 2005/05/16 to 2005/6/20 B ⊥= −89 m. (a) Amplitude and phase. (b) Phase.

Referring to the result after the chirp z-transform, (11), wecan control the resulting azimuth grid through the constant ς .The natural time spacing TS = 1/PRF that is equivalent to adistance spacing of (v/PRF) follows when the constant ς isset to 1. A typical single look image has a distance azimuthpixel spacing of (v/PRF) · (Re/(Re + h))m, accounting forthe ground projection. To match, the constant ς should be setto 1 · ((Re + h)/Re).

For the ScanSAR mode, we have a limited number of pulsesper burst which results in rather coarse azimuth resolution. Thesize of a resel compared to a pixel implies that resolving eachburst to the natural bin spacing will not focus the targets inazimuth to a single point but to a sinc-weighted “short line”[Fig. 10(a)]. Thus, for ScanSAR, we reduce oversampling byusing a larger pulse spacing. Covering the entire real antennaaperture width with each burst means that we will see a singlescatterer in the maximum number of consecutive bursts possible[Fig. 10(c)]. Each burst will have an independent look at the

scatterer, and the pixels containing the scatterer can then beaveraged across the bursts to reduce speckle. To cover thewhole beamwidth within one burst, we set the constant ς to(λr/LNB) · (PRF/v).

We have already pointed out that ENVISAT/ASARScanSAR images have different PRFs than their stripmapcounterparts. Using different reference functions at the chirpz-transform step, as shown in Fig. 9, we set the spacing in theScanSAR image grid to that of the stripmap image. We thus usedifferent ς constants in the ScanSAR and stripmap referencefunctions, differing by a ratio of PRFstripmap/PRFScanSAR.

The chirp z-transform reference functions used are

srefstripmap[k] = e

−jπςfR

(

kTs−NBTs

2

)2

(15)

srefScanSAR[k] = e

−jπς·PRFstripmapPRFScanSAR

·fR

(

kTs−NBTs

2

)2

. (16)


Here, as in [1], we implement the convolution part of thechirp z-transform in the frequency domain. The final blockdiagram for azimuth focusing is shown in Fig. 11. Notice thatin the figure we have included the required azimuth shift forcoregistration. As was mentioned in Section III-A, the requiredfine coregistration can be incorporated into the deramping andchirp z-transform steps. To account for the shifts, sref

derampand sref

Stripmap during its first multiplication step need to havetheir azimuth time variable shifted. Additionally, the signal inthe frequency domain, after FFT, needs to be phase shifted(multiplied by ej2π(Ψq/Nf ), where Ψ is the shift amount, q isthe discrete frequency variable, and Nf is the FFT size).

For the stripmap and ScanSAR images, respectively, the ςvalue in sref can be adjusted to set a certain number of overlappixels between bursts, while maintaining the ς ratio among thetwo. Overlap pixels are those pixels that show the same scenein contiguous bursts. Choosing an integer number of overlappixels simplifies the contiguous bursts overlap step in Fig. 9, byavoiding subpixel interpolation.

We derive the relation between overlap pixels and the con-stant ς using the geometry shown in Fig. 12. The figure showsthe projected area seen by two contiguous bursts. The burstcenters are marked with an X. From one burst to the next,the radar will have traveled by a ground-projected distanceof ∆L = (NRv/PRF) · (Re/(Re + h)). The distance betweenthe center of the burst and both burst edges is NB∆x/2, where∆x is the pulse spacing controlled through ς .

Defining the number of overlap pixels as opixels, we obtainthe following:

opixels · ∆x =NB∆x

2−

[

NRv

PRF· Re

Re + h− NB∆x

2

]

(17)

where ∆x is (ς · v)/PRF, which simplifies to the following:

opixels = NB − NR

ς· Re

Re + h. (18)

As an example, to maintain zero pixel overlap betweenbursts, the ς constant is set to (NR/NB) · (Re/(Re + h)) [seeFig. 10(b)].

C. Azimuth Ramps

In (12), we saw a linear relation between the phase rampand the amount of misregistration. As an example, in oneENVISAT/ASAR IM beam IS2 data set we analyzed here,the main parameters were PRF = (1/Ts) = 1652.42, v =7555.39, λ = 0.056237, and r0 = 824930.5. The expectedslope [from (12)] of the phase ramp is 0.00566 per azimuthpulse or per v/PRF for a one pixel offset error. Thus, for aone pixel misregistration offset, assuming ς is set to 6 and50 pulses per burst, we expect 1.7 rad of phase ramp per burst.Simulations in which we purposefully misregister the bursts byone pixel in fact show a 1.7-rad phase ramp. As r0 changes inthe range direction, the slope changes slightly.

We use this linear relation between the phase ramp and theamount of misregistration to infer the misregistration amount.Initially, we process the images assuming no misregistration.

Fig. 15. Reference topographic map.

After the images have been focused, we estimate the phaseramp and then infer the misregistration amount. The calculatedmisregistration is then used to coregister the images with sub-pixel accuracy.

IV. RESULTS

We show here ENVISAT/ASAR beam IS2 and SS1 acquisi-tions processed to stripmap-to-ScanSAR interferograms of theBig Island of Hawaii. The topographic signature is removed inFigs. 13 and 14. The topographic map in Fig. 15 serves as areference to locate Mauna Loa (bottom), Mauna Kea (top), andKilauea on the bottom right side of the images.

The temporal baseline for the data shown in Fig. 13 is 106days from May 2004 to September 2004, whereas for Fig. 14,it is 36 days from May 2005 to June 2005. The perpendicularbaselines are 74 and 89 m, respectively. The noise of the phaseis 0.34 rad in Fig. 13 and 0.33 rad in Fig. 14. Each fringeof colors represents a change in slant range of 2.8 cm. Notethe significant atmospheric signature present, which providesfurther motivation to process more stripmap-to-ScanSAR inter-ferograms over Hawaii to obtain a denser time series to helpdetermine the atmospheric compensation.

To verify that the chirp z-transform algorithm presented herepreserves the phase signature of the interferogram, we taketwo stripmap images and convert one of them to a ScanSARformat, form the interferogram and compare it to its equivalentstripmap-to-stripmap interferogram. Previous analyses usingthe chirp z-transform were done with ERS data, such as thatin [21], and used values for the number of pulses in oneScanSAR burst and the repetition interval among bursts thatresult in better innate resolution and higher SNR than presentin ENVISAT/ASAR ScanSAR-to-stripmap mode.

Here, we do the comparison using ENVISAT/ASAR dataand a repetition interval among bursts that results in similar


Fig. 16. Stripmap-to-ScanSAR ENVISAT/ASAR interferogram of Hawaii’s Big Island using decimated stripmap data as ScanSAR, from 2003/12/8 to 2004/2/16B ⊥= 16 m. (a) Amplitude and phase. (b) Phase.

Fig. 17. Stripmap-to-stripmap ENVISAT/ASAR interferogram of Hawaii’s Big Island, from 2003/12/8 to 2004/2/16 B ⊥= 16 m. (a) Amplitude and phase.(b) Phase.

innate resolution and SNR to that present in ENVISAT/ASARScanSAR-to-stripmap mode interferograms. These previouscomparisons attested to the ability of deramping followed bychirp z-transform to preserve phase integrity, a property weexpect to remain the same by using the actual ENVISAT/ASARparameters. In this case, we expect to have poorer, but morerealistic, phase noise/correlation performance than the previouscomparisons.

For example, [21] used simulated ERS data over Mount Etnawith a resolution of ∼37 m and four azimuth looks, that is, thesame scatterer appeared in four bursts, by assuming 128 pulsesper burst and repetition interval of 236 pulses. In our verifica-tion, we compare a stripmap-to-ScanSAR interferogram, whichis created by decimating the master image to form the ScanSAR

part, to its equivalent stripmap-to-stripmap interferogram, usingparameters close to ENVISAT/ASAR SS1 ScanSAR mode.ENVISAT/ASAR ScanSAR SS1 uses 50 pulses in one burstand a repetition interval close to 350 (actual is 317.28), giving∼102-m resolution and three looks.

Comparing the stripmap-to-ScanSAR interferogram inFig. 16 to the stripmap-to-stripmap interferogram in Fig. 17(Fig. 18), we see that the phase integrity is preserved sincethe phase shape is maintained. As expected, there is a decreasein the coherence of the image, with more granularity presentin the phase shown in Fig. 16, which in many cases is anacceptable tradeoff for the increase in time density availablein stripmap-to-ScanSAR interferograms. This can be morereadily observed in the coherence images shown in Fig. 19.


Fig. 18. Difference between the interferograms in Figs. 16 and 17. A phaseconstant offset and a phase ramp in range and azimuth across the entire imagewere removed from Fig. 16 before taking the difference.

Note that around Mauna Loa, the stripmap-to-stripmap image[Fig. 19(b)] exhibits more yellow, representing a higher degreeof coherence, than the stripmap-to-ScanSAR [Fig. 19(a)]. Thecoherence in the low coherence parts of the image such as theocean is apparently higher than expected in the stripmap-to-ScanSAR [Fig. 19(a)] due to the bias caused by the loss oflooks [22].

Another equivalent measure for the decrease in quality is thephase noise in both images. The root-mean-square phase noiseof the stripmap-to-ScanSAR interferogram in a high-coherenceregion is 0.181 rad, compared to 0.115 in the same regionfor the stripmap-to-stripmap, or a decrease in performanceratio of 1.57. This difference is close to the expected valuesince the stripmap-to-stripmap image has 11 azimuth looks,whereas the stripmap-to-ScanSAR has only four and thus weexpect the phase noise ratio to be around 1.66 (

√

11/4 = 1.66)[23]. We note in passing that the atmospheric signature in theinterferograms in Figs. 16 and 17 has an interesting shape,tracing a line of constant elevation around Mauna Loa.

V. CONCLUSION

To achieve coherence, the two acquisitions need to be fromthe same beam, limiting a single stripmap-to-ScanSAR interfer-ogram time interval to the repeat orbit time, which is 35 days forENVISAT/ASAR. Through beam steering, the ScanSAR modeof the ENVISAT/ASAR satellite allows for an increase in therevisit frequency of a given area by observing it on differenttracks. Within a particular time interval, there will thus be moreinterferograms available when the radar uses the ScanSARmode. Thus, stripmap-to-ScanSAR processing can result in adenser time series of interferograms. The increase in the timeseries density can be helpful in capturing rapidly time-varyingprocess, as well as to overcome some of the limitations causedby the phase distortions from the superimposed atmosphericsignature. In particular, from the aforementioned images, onecan see how the time density increase will be helpful in an-alyzing areas such as Kilauea in Hawaii, which are typicallyshadowed by an atmospheric signature.

We have shown that an azimuth SAR processor consisting ofderamping followed by chirp z-transform permits generation ofactual ENVISAT/ASAR stripmap-to-ScanSAR interferograms.It is a simple method that allows for reuse of many of the toolsalready available for range-Doppler SAR processing since theonly change is in the azimuth processing.

We have documented all the necessary steps for the az-imuth processing. The azimuth coregistration for stripmap-to-ScanSAR interferograms is difficult due to poor resolutionin ScanSAR acquisitions and differences in PRF betweenstripmap and ScanSAR. The PRF differences lead to a lackof grid correspondence among stripmap and ScanSAR pulseswithin a burst and to noninteger repetition intervals betweenbursts.

We have put forward the use of correlation to determine thecoarse azimuth coregistration. We estimate the fine azimuthcoregistration by using its relation to the phase ramp artifactpresent in misaligned images. After aligning a few bursts, weuse the orbit timing information for the alignment of consecu-tive bursts.

We have proposed reference functions for the chirpz-transform step that result in images with data sample spac-ing multiple of the natural pulse spacing. To achieve align-ment of the pulses within a burst, we use two different chirpz-transform reference functions for the processing of thestripmap and ScanSAR data. Additionally, we have shownreference functions that can be used to obtain a certain integerpixel overlap among consecutive bursts.

We have shown a few stripmap-to-ScanSAR interferogramsover Hawaii processed using the method in this paper. Wehave evaluated the method’s performance by comparing astripmap-to-stripmap interferogram to its equivalent stripmap-to-simulated ScanSAR interferogram.

The stripmap-to-ScanSAR interferograms shown in this pa-per faithfully reflect the phase of conventional interferograms,but exhibit fewer looks and coarser resolution compared totheir stripmap-to-stripmap counterparts. For many applications,a denser time series is of foremost importance, and the advan-tages from the denser time series analysis and temporal av-eraging possible through stripmap-to-ScanSAR interferogramsoffset any loss in coherence.

APPENDIX AKEY PARAMETERS OF THE ENVISAT/ASAR

SCANSAR BEAMS AND CORRESPONDING

STRIPMAP BEAMS

Fig. 20 provides the key parameters for stripmap andScanSAR modes that can be interferometrically combined.

The theoretical resolution of each of the beams is summa-rized in Fig. 21.

APPENDIX BPHASE HISTORY OF STRIPMAP AND BURST-MODE DATA

The range to the scatterer can be expressed as

r(t, t0)2 = r20 + v2(t − t0)2 (19)


Fig. 19. Coherence images of Hawaii’s Big Island, from 2003/12/8 to 2004/2/16 B⊥ = 16 m. (a) Coherence for the stripmap-to-ScanSAR. (b) Coherence forthe stripmap-to-stripmap.

Fig. 20. Key parameters of those ENVISAT/ASAR beams usable for stripmap-to-ScanSAR interferometry. Each stripmap beam has been placed next to itsScanSAR counterpart. NB is the number of pulses in one burst and NR is the number of pulses in one burst-to-burst repetition cycle. srange is the chirp slope,whereas τrange is the chirp pulse duration and the chirp bandwidth can be obtained through their multiplication. The range sampling frequency for all modes andbeams is 19.20768 MHz.

Fig. 21. Theoretical resolution for the different beams, where δRg is theground range resolution and δx is the azimuth resolution. The resolution inthe stripmap case is shown for the two processing cases, where in one, the fullbandwidth is used and in the more typical one, 80% of the bandwidth is used.

or

r(t, t0) = r0

√

1 +v2(t − t0)2

r20

. (20)

Using a first-order Taylor series expansion for the range, sincer0 ≫ v(t − t0), the following equation can be derived:

r(t, t0)≈ r0

(

1 +v2(t − t0)2

2r20

)

= r0 +v2(t − t0)2

2r0. (21)

The phase difference between the transmitted and receivedwaveforms due to the two-way travel to the scatterer willthen be

φ(t, t0) =−4π

λ

[

r0 +v2

(

t2 − 2t0t + t20)

2r0

]

. (22)

Thus, omitting the amplitude term, after range processing ofany given return, the signal in azimuth from the single scattererat location vt0 with the radar at vt will be

s(t, t0) = e−j2πv2

λr0 [t2−2t0t+t20]e−4π

λ r0 . (23)

To simplify the notation, let the azimuth Doppler rate fR =−2v2/λr0, so that

s(t, t0) = ejπfR[t2−2t0t+t20]e−4π

λ r0 . (24)


ACKNOWLEDGMENT

The authors would like to thank P. Rosen and S. Hensley (JetPropulsion Laboratory) for helpful discussions and the anony-mous reviewers for their helpful comments and suggestions.They also acknowledge ESA for supplying the raw data fromthe ENVISAT/ASAR satellite through the WinSAR project.

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Ana Bertran Ortiz (S’06) received the B.S. andM.S. degrees in electrical engineering from StanfordUniversity, Stanford, CA, in 1997 and 2003, respec-tively. She is currently working toward the Ph.D.degree at Stanford University.

She is currently a Graduate Research Assistantwith the Radar Interferometry Group, Stanford Uni-versity. Her current research interests include SARprocessing and radar remote sensing.

Howard Zebker (M’87–SM’89–F’99) received theB.S. degree from the California Institute of Technol-ogy, Pasadena, in 1976, the M.S. degree from theUniversity of California, Los Angeles, in 1979, andthe Ph.D. degree from Stanford University, Stanford,CA, in 1984.

He holds a joint appointment in the Departmentsof Geophysics and Electrical Engineering, StanfordUniversity and studies Earth processes from theviewpoint of spaceborne instruments. His group isinvolved in basic research ranging from crustal de-

formation related to earthquakes and volcanoes to global environmental prob-lems as evidenced in the flow and distribution of ice in the polar regions. Thegroup is also developing new observational technologies such as radar inter-ferometry. His current research interests include the definition and scientificapplications of new spaceborne imaging systems, particularly those containingimaging radar systems.

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