Ground-Based Synthetic ApertureRadar Data Processing for Deformation

Measurement

Andreas Jungner

Master’s of Science Thesis in Geodesy No. 3116

TRITA-GIT EX 09-11

Division of Geodesy

Royal Institute of Technology (KTH)

100 44 Stockholm, Sweden

May 2009

TRITA-GIT EX 09-11ISSN 1653-5227ISRN KTH/GIT/EX–09/011-SE

Abstract

This thesis describes a first hands-on experience working with a Ground-Based SyntheticAperture Radar (GB-SAR) at the Institute of Geomatics in Castelldefels (Barcelona,Spain), used to exploit radar interferometry usually employed on space borne platforms.We describe the key concepts of a GB-SAR as well as the data processing procedure toobtain deformation measurements. A large part of the thesis work have been devoted todevelopment of GB-SAR processing tools such as coherence and interferogram generation,automating the co-registration process, geocoding of GB-SAR data and the adaption ofexisting satellite SAR tools to GB-SAR data. Finally a series of field campaigns havebeen conducted to test the instrument in different environments to collect data necessaryto develop GB-SAR processing tools as well as to discover capabilities and limitations ofthe instrument.

The key outcome of the field campaigns is that high coherence necessary to conductinterferometric measurements can be obtained with a long temporal baseline. Severalfactors that affect the result are discussed, such as the reflectivity of the observed scene,the image co-registration and the illuminating geometry.

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iv

Sammanfattning

Det har examensarbetet bygger pa erfarenheter av arbete med en mark-baserad syntetiskapertur radar (GB-SAR) vid Geomatiska Institutet i Castelldefels (Barcelona, Spanien).SAR tekniken tillater radar interferometri som ar en vanligt forekommande teknik bade pasatellit och flygburna platformar. Det har arbetet beskriver instrumentets tekniska egen-skaper samt behandlingen av data for att uppmata deformationer. En stor del av arbetethar agnats at utveckling av GB-SAR data applikationer som koherens och interferogramberakning, automatisering av bild matchning med skript, geokodning av GB-SAR datasamt anpassning av befintliga SAR program till GB-SAR data. Slutligen har matningargjorts i falt for att samla in data nodvandiga for GB-SAR applikations utvecklingen samtfa erfarenhet av instrumentets egenskaper och begransningar.

Huvudresultatet av faltmatningarna ar att hog koherens nodvandig for interferometriskamatningar gar att uppna med relativ lang tid mellan matepokerna. Flera faktorer sompaverkar resultatet diskuteras, som det observerade omradets reflektivitet, radar bildmatchningen och den illuminerande geometrin.

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vi

Acknowledgments

I want to thank my tutor Dr. Michele Crosetto at the Institute of Geomatics for goodadvice and for the opportunity the conduct my thesis at the institute. I also wish to thankmy tutor at KTH, Dr. Milan Horemuz. Furthermore I want to acknowledge Dr. BrunoCrippa (Politecnico di Milano) for teaching me the co-registration technique used in thiswork. I also wish to acknowledge Virtual City Modeling Lab (UPC) for the surface modelof Sagrada Famılia. Last but not least I thank Oriol Monserrat for daily help and adviceon all kinds of topics including Catalan culture.

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Contents

Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vSammanfattning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viiAcknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix

1. Introduction 11.1. Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.2. Outline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

2. GB-SAR Concepts 32.1. Imaging Radar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

2.1.1. Range Resolution . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42.1.2. Cross-range Resolution . . . . . . . . . . . . . . . . . . . . . . . . . 6

2.2. Radar Interferometry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62.2.1. Interferogram . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82.2.2. Coherence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

3. The GB-SAR System 113.1. The Sensor Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123.2. Linear Scanner . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123.3. Control Unit and Power Supply . . . . . . . . . . . . . . . . . . . . . . . . 13

4. GB-SAR Data Processing 154.1. Processing Chain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 154.2. Co-Registration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 184.3. Geocoding of GB-SAR Data . . . . . . . . . . . . . . . . . . . . . . . . . . 21

4.3.1. Projection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 214.3.2. Transformations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

5. Field Campaigns 235.1. Port of Barcelona . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 235.2. L’Eixample, Barcelona . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 265.3. Castelldefels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 295.4. Sagrada Famılia, Barcelona . . . . . . . . . . . . . . . . . . . . . . . . . . 355.5. Montserrat . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

6. Conclusions 43

References 45

ix

Contents

A. Appendix 47A.1. Co-registration Parameter File . . . . . . . . . . . . . . . . . . . . . . . . . 47A.2. Co-registration Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50

x

List of Figures

2.1. Amplitude image of a GB-SAR acquisition . . . . . . . . . . . . . . . . . . 42.2. Range measurement illuminating several targets . . . . . . . . . . . . . . . 52.3. The relationship between the quadrature components . . . . . . . . . . . . 62.4. Range measurements acquired along a rail . . . . . . . . . . . . . . . . . . 72.5. Interferometric measurements principle . . . . . . . . . . . . . . . . . . . . 82.6. Projected displacement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92.7. Interferogram and Coherence . . . . . . . . . . . . . . . . . . . . . . . . . . 10

3.1. The IBIS-L instrument . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113.2. Vertical antenna radiation pattern . . . . . . . . . . . . . . . . . . . . . . . 12

4.1. A simplification of a SAR image formation . . . . . . . . . . . . . . . . . . 154.2. Close up of amplitudes of well focused and badly focused data . . . . . . . 164.3. Comparison of wrapped and unwrapped phase. . . . . . . . . . . . . . . . . 184.4. Linear term correction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 194.5. Correlation windows positions . . . . . . . . . . . . . . . . . . . . . . . . . 20

5.1. Sensor view, Port of Barcelona . . . . . . . . . . . . . . . . . . . . . . . . . 245.2. Amplitude image, Port of Barcelona . . . . . . . . . . . . . . . . . . . . . . 245.3. Loss of Coherence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 255.4. Thermal Signal to Noise Ratio . . . . . . . . . . . . . . . . . . . . . . . . . 255.5. View of L’Eixample . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 275.6. Amplitude image of L’Eixample . . . . . . . . . . . . . . . . . . . . . . . . 275.7. The importance of co-registration. . . . . . . . . . . . . . . . . . . . . . . . 285.8. Sensor view, Castelldefels . . . . . . . . . . . . . . . . . . . . . . . . . . . 305.9. Acquisitions made with normal transmitted power level . . . . . . . . . . . 315.10. Acquisitions made with increased transmitted antenna power level . . . . . 325.11. Acquisitions made with single calibration configuration . . . . . . . . . . . 335.12. Geocoding of Castelldefels . . . . . . . . . . . . . . . . . . . . . . . . . . . 345.13. Sagrada Famılia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 365.14. Sagrada Famılia Pointcloud from Terrestial Laser Scanner. . . . . . . . . . 365.15. Amplitude image of Sagrada Famılia . . . . . . . . . . . . . . . . . . . . . 375.16. Geocoding of Sagrada Famılia . . . . . . . . . . . . . . . . . . . . . . . . . 375.17. Sensor view, Montserrat . . . . . . . . . . . . . . . . . . . . . . . . . . . . 395.18. Montserrat geocoding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 405.19. Montserrat processing steps . . . . . . . . . . . . . . . . . . . . . . . . . . 415.20. Montserrat processing steps (continued) . . . . . . . . . . . . . . . . . . . 425.21. A Time Series of a pixel . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

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1. Introduction

This thesis was written at the Institute of Geomatics in Castelldefels (Barcelona, Spain)during July 2008 - April 2009 as a M.Sc. thesis in Engineering Geomatics at the RoyalInstitute of Technology (KTH).

The Institute of Geomatics (http://www.ideg.cat/) is a public consortium made up ofthe Autonomous Government of Catalonia (Ministry of Town and Country Planning andPublic Works, the Ministry of Innovation, Universities and Enterprise) and the TechnicalUniversity of Catalonia created by Decree Law 256/1997 of the Autonomous Governmentof Catalonia on September 30, 1997. As a research centre, the Institute’s aim is promotionand development of Geomatics through applied research and teaching for the benefit ofsociety.

The Institute of Geomatics acquired a first commercial version of a Ground-Based Syn-thetic Aperture Radar (GB-SAR) instrument in July 2008. The opportunity to conductmy thesis work at the very beginning of the arrival of a new instrument made me gracefullychoose the Institute of Geomatics for my thesis work.

A Ground-Based Synthetic Aperture Radar (GB-SAR) is an active microwave acqui-sition sensor that provides its own illumination and measures the reflected signal. Thismakes data acquisition possible day and night independently of natural light. SyntheticAperture Radar (SAR) is today a relatively mature technique implemented on numer-ous satellites and aircraft. In recent years the SAR technique have been implemented onground-based platforms with the advantages of being able to illuminate an area of interestfrom an optimal angle and the possibility to acquire images at any time in comparison toavailable satellite systems. In case of fast deformations this is a decisive factor.

Furthermore in contrast to other terrestrial deformation measurement instruments theGB-SAR covers a continuous surface up to approximately 1 km2 from a single mea-surement position. GB-SAR has been used for landslide monitoring, glacier monitoring,avalanche prediction, volcano front monitoring, dams monitoring and subsidence moni-toring (Noferini, 2004).

1.1. Objectives

The aim of this thesis work is to gain experience of a new ground-based deformationmeasurement instrument using a technique adopted from space borne platforms. A largepart of this work consists of developing processing tools for the GB-SAR data such as co-herence and interferogram generation, automating the co-registration process, geocodingof GB-SAR data and the adaption of existing satellite SAR tools to GB-SAR data.

1

1. Introduction

1.2. Outline

The thesis in divided into four main parts. The first part describes general GB-SARconcepts and the second part describes the particular instrument used. The third partoutlines the data processing and describes two processing steps more in detail. In thelast part the processing tools are used to interpret real world environment data collectedduring five field campaigns to discover capabilities and limitations of the instrument.

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2. GB-SAR Concepts

This Chapter describes the general concepts of a Ground-Based Synthetic Aperture Radarvalid for the GB-SAR used in this project. The GB-SAR is fundamentally based on threedifferent techniques:

∙ Stepped Frequency Continuous Wave (SF-CW), a frequency modulation techniquethat makes it possible to resolve resolution in range.

∙ Synthetic Aperture Radar technique (SAR), that makes it possible to resolve thecross-range resolution.

∙ Interferometric Technique (InSAR), exploits the coherent phase of the receivedechoes.

The first two techniques which make it possible to create two dimensional radar images arecovered in the first section, while the last technique which exploits the phase to measuredeformations, is covered in the second section.

2.1. Imaging Radar

Radar is an acronym for Range Detection And Ranging and refers both to a technique andan instrument. A radar works by transmitting short pulses of electro-magnetic energy,which are propagated at speed of light and reflected by the terrain surface creating returnechoes that are collected by the receiving antenna. Measuring the time delay of thetwo-way propagation of the echo determines the range R by the equation

t =2 ⋅Rc

(2.1)

where c is the speed of light. The ability to determine range by measuring the time forthe radar signal to propagate to the target and back is probably the distinguishing andmost characteristic of a conventional radar (Skolnik, 1990).

A special class of radars is imaging radars that are capable of not only measuring rangesbut also creating images. To accomplish this, resolution has to be resolved in both the in-range and the cross-range directions. A requirement to fully exploit the image informationis that the radar is coherent, which means that both the phase and the amplitude of thereturned echo are stored for the image processing.

The transmitted pulses are of conical shape with an elliptic base that illuminates anangular area producing images of polar geometry. See Figure 2.1. The following twosubsections will cover how resolution is resolved.

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2. GB-SAR Concepts

Figure 2.1.: Amplitude image of a GB-SAR acquisition of a mountain side. Red areasrepresent high backscatter echoes which mean that a large portion of thetransmitted energy was reflected back. The type and geometry of the surfacealso affect the strength of the reflected backscatter.

2.1.1. Range Resolution

Resolution is defined as the minimal distance at which two distinct scatters of the samebrightness can be uniquely discerned as a separate signal (Hanssen, 2001). To distinguishbetween objects at different distances a short pulse is used. The shorter the pulse, thebetter the resolution (Skolnik, 1990).

Both a short pulse as well as a good Signal to Noise Ratio (SNR) that requires highpeak power are desired. However, the shorter the pulse, the lower the transmitted energysince the energy the instrument can emit in a finite timespan is limited. The effect ofa short pulse is obtained with a long pulse using a frequency modulated waveform thatincreases the spectral bandwidth of the pulse. When filtered with the transmitted signal,the returning waveform produces a compressed pulse whose duration is approximately thereciprocal of the spectral width of the modulated pulse � ≈ 1

B(Skolnik, 1990; Hanssen,

2001). This is referred to as pulse compression and defines range resolution ΔR as afunction of bandwidth by

ΔR =c�

2≈ c

2B(2.2)

where c is the speed of light, � is compressed pulse duration and B is bandwidth. Eachdiscrete distance defined by ΔR is referred to as a range bin. See Figure 2.2. Therange measurements are one dimensional which makes it impossible to distinguish betweenobjects located in the same range bin. Multiple targets in the same range bin return acumulative response.

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2.1. Imaging Radar

Range [m]

Echointensity

Target

Range bin

Antenna direction

Figure 2.2.: Range measurement of radar geometry illuminating several targets. Targetsin the same range bin return a cumulative response. It is not possible todistinguish between different targets in the same range bin.

Stepped Frequency Continuous Wave

A large bandwidth is obtained using a Stepped Frequency Continuous Wave (SF-CW)frequency modulation technique.

SF-CW is commonly used in close range applications since it permits modulated pulseduration to be longer than the two-way propagation delay of the signal. Frequency is in-creased in discrete steps through instrument bandwidth dwelling on each frequency steplong enough for the transmitted signal to return. The SF-CW technique consists of syn-thesis and transmission of a burst of N monochromatic pulses equally and incrementallyspaced in frequency (with fixed frequency step of Δf) within a bandwidth B (Bernardiniet al., 2007a,b), where

B = Δf(N − 1) (2.3)

For each frequency step both the orthogonal In-phase (I) and Quadrature (Q) complexcomponents of the returned echo are stored representing the frequency response of theN pulses. The data is then reconstructed in time domain using an Inverse DiscreteFourier Transform (IDFT) (Bernardini et al., 2007a). From the time domain quadraturecomponents the amplitude and phase is obtained by means of the magnitude and theargument of the complex parts, respectively:

A =√I2 +Q2 (2.4)

' = tan

(Q

I

)(2.5)

where A is the amplitude and ' is the phase. See Figure 2.3.

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2. GB-SAR Concepts

Q

I'

A

Figure 2.3.: The relationship between the complex In-phase (I) and Quadrature (Q) com-ponents, amplitude (A) and phase ('). The amplitude is the magnitude of thecomplex components marked with a thick line and the phase is the argument.

2.1.2. Cross-range Resolution

Using only one range measurement it is not possible to distinguish between differentobjects located at the same distance as illustrated in Figure 2.2. But combining allcoherent range acquisitions acquired observing the same scene slightly offset creating asynthetic long antenna makes it possible to focus the acquisitions into two dimensionalimages exploiting the Synthetic Aperture Radar technique.

This is accomplished by displacing the sensor along a rail parallel to the illuminatedscene observing the same scene from slightly different angles. All offset range measure-ments acquired are focused into a single image with origin at the center of the baseline.See Figure 2.4.

Analogously to pulse compression in range, the resolution in azimuth is obtained bycompressing the range measurements in the cross-range direction (Hanssen, 2001). Toobtain resolution in range a long pulse is compressed. In the cross-range direction along acquisition time acquiring multiple range measurements is compressed into a singleimage considered captured at a same instant of time. Cross-range is defined as an angularresolution

Δ' =�

2L(2.6)

where � is wavelength and L is synthesized antenna length. Note that the term 1/2accounts for the fact the that the combined acquisitions were not acquired at the sameinstant of time. Since cross-range resolution is defined as an angle the pixel size increaseslinearly in the cross range direction by distance. Moving objects in the illumined scenemay cause focusing distortions since the SAR technique is based on observing the samescene from slightly different angles during a small timespan.

2.2. Radar Interferometry

Using coherent data acquired at different viewpoints or instants of time makes inter-ferometry possible. The interferometric technique is based on measuring relative range

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2.2. Radar Interferometry

xx0 xnxi

Figure 2.4.: The sensor moves along a baseline from x0 to xn acquiring range measure-ments of the same scene. This permits focusing the range measurements intoa single image using Synthetic Aperture Radar (SAR) technique.

differences by comparing the phase components of two images, denoted as master andslave. The phase difference of each pixel is calculated by the argument of the pointwisemultiplicated complex master image M containing the quadrature components I and Qwith the corresponding conjugate of the slave image S∗. The deformation length d foreach pixel is then obtained by

d = − �

4�arg (M ⋅ S∗) = − �

4�Δ�M−S (2.7)

where � is the wavelength, arg represent the argument function of complex numbers andΔ�M−S is the phase difference of each pixel between the compared image pair, denotedmaster and slave. See Figure 2.5.

Data to be compared must be acquired at exactly the same position. For repeatedmeasurements the system must be carefully repositioned and the images co-registeredin post-processing using the amplitude information of the images to be compared to beperfectly superimposed. This is a very important requisite and will be further discussedin Section 4.2.

Since the images to be compared are acquired at different instants of time the at-mospheric conditions must be considered. The variation of the diffraction index, dueto temperature, humidity and pressure, causes a variation of the wavefront propagationvelocity which may introduce an error in the range measurements.

Introducing the atmospheric contributions and ambiguity errors, the phase differencesare summarized in Equation 2.8:

Δ�M−S = Δ�(R) + Δ�atm + Δ�n + Δ�noise (2.8)

where Δ�M−S is the measured phase difference, Δ�(R) is true phase difference, Δ�atm isthe atmospheric contribution, Δ�n is the phase ambiguity and Δ�noise is noise.

All measurements are along the radial direction, i.e. in the Line Of Sight (LOS) of theantennas. This must be considered when positioning the sensor in relation to the area to

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2. GB-SAR Concepts

Tx

Tx

Rx

Rx

atm1

d

Master (t0)

Slave (t1)

atm2

Figure 2.5.: Interferometric measurements of acquisitions acquired at the same positionat two different instants of time ΔT = t1 − t0. The phase components ofthe master acquisition under the influence of atmosphere atm1 and the slaveacquisition under the influence of atm2 is compared to measure deformationd.

be illuminated. In Figure 2.6 the geometrical relations between measured displacementand effective point displacement d are obtained with geometrical uniformity,

d = dpR

ℎ(2.9)

where d is effective point displacement, dp is projected point displacement, R is range andℎ is height.

2.2.1. Interferogram

To determine the range variations using SAR interferometry, two radar images of thesame surface area are acquired and differenced in phase, forming a radar interferogram.The complex interferogram is defined as (Kampes and Usai, 1999)

I = M ⋅ S∗ (2.10)

where I is the interferogram, M is the Master image, S is the Slave image and {⋅}∗ is thecomplex conjugate. This is equal to the argument of the phase differences

Δ�M−S = arctan

(QΔ

IΔ

)(2.11)

The phase is periodic within [−�, �] which implies that if the phase exceeds this range thephase jumps a cycle. This phenomena is easily seen in a visualized phase and is referredto as fringes. See Figure 2.7(a).

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2.2. Radar Interferometry

�

Rℎ

ddp

�

Figure 2.6.: The illuminated area marked with a thick line is measured from a height ℎmeasuring a projected displacement dp. The fraction between range R andheight ℎ determines the effective point displacement d

The rand value of the phase domain also defines the maximum non-ambiguous mea-surable phase between adjacent pixels as ±�

4using ±� in Equation 2.7. It should be

emphasized that as long as the phase gradient is below this value the absolute phasevalue can be reconstructed with phase unwrapping. This topic will be further discussedin Section 4.1.

2.2.2. Coherence

SAR interferometry only works under coherent conditions where the received waveformscorrelate in the compared SAR image pair. Coherence describes the correlation betweenthem and is a vital measurement of where the phase is exploitable. It should be empha-sized that coherence serves as quality measurement both of the acquisitions as well asthe data processing. Data with high coherence is the only data of interest. The complexcoherence between two images is defined as

c =E {M ⋅ S∗}√

E {M ⋅M∗} ⋅ E {S ⋅ S∗}(2.12)

where E {⋅} is statistical expectation. The coherence is defined by ∣ c∣, and its estimatoras (Kampes and Usai, 1999)

=

∣∣∣∣∣∣1n

∑ni=0MiS

∗i√

1n

∑ni=0 MiM∗

i1n

∑ni=0 SiS

∗i

∣∣∣∣∣∣ (2.13)

The coherence resides in the range [0, 1] with high values being coherent. Coherencedecorrelates with time due to changes in the observed scene. The material and shape ofthe scene highly affect the coherence. Vegetation has low coherence due to its entropicnature while solid materials such as rocks and structures maintain high coherence for alonger period of time. See Figure 2.7(b).

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2. GB-SAR Concepts

(a) Interferogram (b) Coherence

Figure 2.7.: An interferogram with fringes to the left. In this particular case the fringesare due to phase variations provoked by combining measurements acquiredfrom positions with 10 cm height separation. To the right is the correspondingcoherence image of the same scene. Bright pixels are coherent.

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3. The GB-SAR System

The GB-SAR used in this project is a digital stepped-frequency continuous wave, highstability coherent interferometric radar system called IBIS-L (acronym for Image By In-terferometric Survey - L) manufactured by Ingegneria Dei Sistemi (IDS) S.p.A.(http://www.idsgeoradar.com/). The system consists of four main components:

∙ Sensor module, containing the radar head and antennas.

∙ Linear scanner, consisting of a 2.5 meter long rail and motor used to displace thesensor module parallel to the observed scene to acquire multiple images of the samescene slightly offset exploiting the SAR technique.

∙ Control unit, PC with software to control the radar system.

∙ Power supply, containing two serial connected 12 V car batteries, fuses and servesas a hub for PC connections and external power sources.

Figure 3.1.: The IBIS-L system. The yellow sensor module is mounted on top of the linearscanner acquiring multiple range measurements from left to right, exploitingthe SAR technique. To the right is the power supply and control unit.

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3. The GB-SAR System

3.1. The Sensor Module

The IBIS-L is working on Ku-band at 17.1 GHz with a maximum bandwidth of 300 MHz,which gives a range resolution of 0.5 m using Equation 2.2:

ΔR =c

2B= 0.5m

The system uses two identical antennas, one for transmitting and one for receiving. Thestandard antennas are characterized by a maximum gain of 20 dBi1. The amplitudecharacteristics of the antenna main lobe at -3 dB, which is defined as the angular areawithin which antenna gain is more than 50% of the maximum gain (−3dB = 10⋅log10(0.5))are 17∘ in the horizontal plane and 15∘ in the vertical plane. This has to be consideredwhen pointing the sensor at the scene of interest. See Figure 3.2. It is possible to changeto antennas with other radiation pattern characteristics. However increasing the aperturethe transmitted energy needs to be increased.

Figure 3.2.: Vertical antenna radiation pattern for the 20 dB gain antenna

3.2. Linear Scanner

The linear scanner serves as the platform to acquire the range measurements along astraight track to create a synthesized antenna. In comparison to space borne SAR therail represents a small part of the trajectory along the orbit.

The rail weighs 54 kg and is 2.5 meter long of which 2 m is available for the synthe-sized antenna length. See Figure 3.1. Cross-range resolution is a function of synthesizedantenna length and wavelength which defines the maximum cross-range resolution usingEquation 2.6:

Δ' =�

2L= 4.4 mrad

1dB(isotropic) - the forward gain of an antenna compared with the hypothetical isotropic antenna, whichuniformly distributes energy in all directions

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3.3. Control Unit and Power Supply

This means that a pixel is 4.4 m long in the cross-range direction at 1000 m distance. Usinga smaller baseline gives as coarser cross-range resolution but a faster acquisition. Theoffset step along the rail for each measurement is 5 mm which means 401 measurementsare acquired when using the maximum 2 meter synthesized antenna length.

3.3. Control Unit and Power Supply

The system is controlled from a laptop with controlling software. It is possible to controlthe system remotely. The controlling software is used to set resolution in range and cross-range, maximum range, transmitted power and number of acquisitions etc. It creates apreview image that re-focuses for every range measurement made along the synthesizedantenna and provides a simple in-situ deformation visualization tool.

The power supply is powered by two serial connected 12V car batteries that have acapacity of approximately 24 hours. It is possible to use power from an external solarpanel.

It is a rather heavy and bulky instrument of which the radar head weights about 10kg, the baseline rail 54 kg and the power supply 89 kg. The system characteristics aresummarized in Table 3.1:

Table 3.1.: System Characteristics Summary

Frequency band Ku-band, 17.05-17.35 GHzAntennas Horn antennas (20 dB or 13.5 dB)Wavelength 1.8 cmMaximum range 4.0 kmSpatial resolution Max resolution; range: 0.5 m, cross-range: 4.4 mradPower supply Batteries or 12VDC solar cellsDimensions 250 x 50 x 60cm (linear scanner)Weight 170 kg (with power supply)Power consumption 70 WProcessing unit PC Panasonic CF-19 with IBIS-L operational software

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4. GB-SAR Data Processing

This Chapter describes the different processing steps of the GB-SAR data adopted in thisproject starting with the image acquisitions. The processing chain is outlined in the firstsection and two of the steps are described in detail in subsequent sections.

4.1. Processing Chain

∙ Data Collection This first fundamental step is to collect the data in the field whichinvolves transportation and data acquisitioning. Since the instrument is heavy andbulky two persons are needed for the transportation and assembly of the instrument.About half an hour is needed for the assembly of the instrument.

The reflectivity of the area of interest is a key factor for good measurements, espe-cially at long ranges when the returning signal becomes weaker. Reflectivity is afunction of material and observed geometry. Long range measurements also increasethe acquisition time which increases the probability of focusing errors, especially ina noisy ambiance. However it is possible to shorten the acquisition time by using asingle calibration configuration. When using the single calibration configuration theinstrument makes only one phase calibration instead of one for each range measure-ment. This is sufficient if the instrument has reached its internal working tempera-ture needed to maintain a stable phase acquired for accurate measurements. Thisis accomplished by warming up the instrument acquiring measurements for abouthalf an hour or using the internal calibration capabilities of the instrument.

∙ Focusing Raw SAR data is spread out through the image in azimuth and rangedirection. In the range direction the information is spread out due to the frequencymodulated pulse (SF-CW) and in the azimuth direction it is spread due to theacquisition time used to observe the same scene slightly offset. The focusing processserves to collect the dispersed data into single output pixels. See Figure 4.1.

point target raw data point target

dataacquisition processing

data

Figure 4.1.: A simplification of a SAR image formation. A point target has been measuredfrom several azimuth positions along a linear scanner. The hyperbola repre-sent range as a function of azimuth position. The point target is reconstructedwith the SAR processing.

15

4. GB-SAR Data Processing

Focusing errors occur if the linear scanner could not be kept stable during theacquisition. The system is very sensitive to this because of its weight and theinertia caused by the displacement of the sensor on top of the linear scanner. Toavoid this the instrument needs to be fastened in a heavy base support. Two custommade 25 kg support concrete blocks were constructed to secure the instrument. Aninstrument rotation at millimeter level may produce measurements meters off targetin far-range. When range measurements acquired from an instable platform arefocused a global distortion is induced and the amplitudes look blurry or out of focus.Focusing errors also occurs due to moving objects in the illuminated scene. Thesemovements cause local distortions in the images. The errors caused by instrumentmovement cannot be compensated for. See comparison of well focused and badlyfocused images in Figure 4.2. Significant distortions will also be shown in section5.1.

(a) Well focused amplitude (b) Badly focused amplitude with global distor-tion

Figure 4.2.: Close up of amplitudes of well focused and badly focused data induced bymovement of the system during the acquisition.

A target with very high reflectivity may also cause focusing errors if the receivedenergy spreads out to adjacent pixels. This can however be compensated for byapplying a windowing function such as a Kaiser or Hanning window during thefocusing at the cost of loosing signal over the whole image. In summary the errorsare a function of acquisition time and observed scene characteristics.

∙ Co-Registration Co-registration is the process of aligning images acquired at thesame scene exactly on top of each other to be able to exploit the phase information.This is done comparing the amplitudes of two images, referred to as Master andSlave, calculating the correlation of a large number of windows distributed over theimages. The correlation windows are chosen visualizing the amplitudes and selectinghigh gain points evenly distributed ever the Region Of Interest. This will be furtherexplained in Section 4.2.

∙ Interferogram and Coherence Generation Using the resampled slave from the

16

4.1. Processing Chain

co-registration step, cumulative interferogram and coherence are calculated fromthe quadrature components. Cumulative in the sense that they all share the samemaster image so the history of each pixel can be evaluated, Δ�M−i.

∙ Phase Unwrapping The principal observation is the two-dimensional relativephase from the interferogram which is the 2�-modulus of the unknown absolutephase. The inverse problem of unwrapping a phase from the interval [−�, �) isarguably one of the main difficulties in radar interferometry (Hanssen, 2001). Theabsolute (unwrapped) phase for a pixel i, j is equal to the relative (wrapped) phaseand a 2�-modulus.

Φ(i, j) = (i, j) + 2�k(i, j) (4.1)

where Φ(i, j) is unwrapped phase, (i, j) is wrapped phase and k(i, j) is the numberof 2� cycles. With the assumption that the phase difference between adjacent pixelsalways is smaller than ∣�∣ the following formula is valid for a neighboring pixel inthe x direction and analogously for y:

Φx =

⎧⎨⎩Δ x if ∣Δ x∣ ≤ �

Δ x − 2� if Δ x > �

Δ x + 2� if Δ x < −�(4.2)

where Φx is unwrapped phase and x is wrapped phase. If the phase difference ofadjacent pixels do not stay smaller than ∣�∣, there exists aliasing and the ambiguityof the 2�-modulus cannot be determined. Then a shorter temporal baseline ΔTshould be used when forming the phase interferogram. The unwrapping in this workwas done using a minimal cost flow (MCF) method solved as a global minimizationproblem as suggested by Costantini (1998). See Figures 4.3(a) - 4.3(d) of wrappedand unwrapped phase.

∙ Atmospheric Correction A plane model was used to compensate for the atmo-spheric effects with the assumption that atmospheric phase contributions are mainlylinear. A mask is used to select points from which plane parameters are to be esti-mated with a Least Squares Solution with observation equation.

Φ = A+B ⋅ line + C ⋅ column (4.3)

where Φ is phase and A,B,C are plane coefficients. See phase trend, estimatedplane and detrended phase in Figure 4.4.

∙ Deformation Time Series Since interferograms are comparisons of two phasemeasurements they are relative measurements. To be able to compare the phasesthey must be anchored at a point considered stable. The images are normalized byadjusting this point to zero. This is done masking the stable point and subtractingthe median value of the mask from the image. After the images are normalized thestack of interferograms are referenced to the first image in the series by subtractingthe first image from each image in the stack. This sets the first image to zero andthe following interferograms as relative changes to the first one.

17

4. GB-SAR Data Processing

(a) Wrapped phase (b) Unwrapped phase

(c) Wrapped phase (d) Unwrapped phase

Figure 4.3.: Comparison of wrapped and unwrapped phase.

∙ Geocoding and Visualization This is a fundamental step for interpreting thedata consisting of projection and coordinate transformation. This will be coveredin Section 4.3.

4.2. Co-Registration

Co-registration is the process of aligning images acquired at the same scene exactly ontop of each other to be able to exploit the phase information. This is done comparingthe amplitudes of two images, referred to as Master and Slave, calculating the correla-tion of a large number of windows distributed over the images. The correlation windowsare chosen visualizing the amplitudes choosing high gain points evenly distributed everthe Region Of Interest. This can be semi-automated using a visualizing tool like ENVI.The co-registration was done using a free InSAR processor named DORIS (Delft Object-oriented Radar Interferometric Software). This program is intended for satellite SAR

18

4.2. Co-Registration

(a) Phase with linear term (b) Estimated plane

(c) Detrended phase

Figure 4.4.: Linear term correction using 51889 observations to estimate three plane pa-rameters.

19

4. GB-SAR Data Processing

image processing and requires hundreds of input parameters lines containing informationabout the master and slave image and the processing steps. Pseudo satellite parameterssuch as orbit information have to be included in the parameter files for the InSAR proces-sor used to accept the GB-SAR data. An example of slave image parameters is attachedin Appendix A.1. The generation of input parameters was handled using shell scripts.The co-registration process is outlined in the following steps.

∙ Correlation windows position selection. High gain amplitudes are chosen with gooddistribution. Typically more than 10000 windows are chosen and stored in an ASCIIfile. See Figure 4.5.

Figure 4.5.: Correlation windows positions marked in with red.

∙ Input parameter files generation. Four input parameter files are created; One eachfor the master and slave image, one for the co-registration parameters and one forthe resampling.

∙ Co-registration. The offset vectors to align the slave image to the master are com-puted with sub pixel accuracy for the correlation window positions selected in themaster. The offset between master and slave is estimated by computing the correla-tion of the amplitude images for shifts at pixel level. Next, in a local neighborhood ofthe maximum (correlation at pixel level) these correlations are harmonically over-sampled to find the maximum at sub pixel level. Based on the estimated offsetscomputed and correlation threshold selected, the transformation parameters areestimated by means of a Least Squares Solution (Kampes and Usai, 1999).

∙ Resampling. Using the transformation parameters estimated in the previous step,the slave image is resampled to the master by an affine transformation. This stepcan be quite time consuming. Using Generic Mapping Tools (GMT) fitting statistic

20

4.3. Geocoding of GB-SAR Data

plots are obtained. Figures of offset vectors obtained from resampling with DORISwill be presented in Section 5.3.{

x′ = a0 + a1x+ a2y

y′ = b0 + b1x+ b2y(4.4)

where a0 and b0 are translations and a1, a2, b1, b2 are rotations.

4.3. Geocoding of GB-SAR Data

This section describes the geocoding procedure which consists of assigning ground refer-enced coordinates to GB-SAR data. The procedure is solved as an inverse problem byprojecting the surface model geometry into radar polar geometry.

Required data for geocoding is a surface model and known sensor position and rotationbetween surface model and radar local reference system. Sensor position and rotation aredetermined by identifying known points in the acquired images. Corner reflectors thatreturn high radar backscatters are preferably used. Identifying enough points allows overdetermination using a Least Squares Estimate.

In this project surface models from a Digital Elevation Model (DEM) of Catalonia anda Terrestrial Laser Scanner point cloud (TLS) are used.

4.3.1. Projection

All points in surface model are projected into radar geometry (line and column) withrespect to radar position and rotation between surface model and radar local referencesystem. For each pixel of the surface model the range R and azimuth angle � is calculatedwith respect to radar position by

R =√

(E − e0)2 + (N − n0)2 + (Z − z0)2 (4.5)

� = arctan

(E − e0

N − n0

)(4.6)

where � is azimuth angle with respect to radar antenna pointing direction and E,N,Z iseasting, northing and orthometric height of the surface model pixel. The radar positione0, n0, z0 is expressed in equal coordinates as the used surface model. These coordinatesare obtained from identified corner reflectors in acquired radar image or with an externalmeasurement such as GPS or Laser Scanner. Finally the line and column of acquiredradar image is obtained by

line =R

ΔR(4.7)

column =� − �0

Δ�+ central column (4.8)

where ΔR is used radar range resolution and Δ� is used cross-range resolution. Theantenna pointing direction is expressed with �0. The central column of the acquired radarimage is added to obtain positive column values.

21

4. GB-SAR Data Processing

In the case of large binary Digital Elevation Model (DEM) it is reasonable to cut out thescene of interest to minimize the calculation time. All surface model points assigned to thesame radar pixel are interpolated to a discrete position. Multiple surface model pointswithin the same pixel are interpolated using bilinear interpolation or inverse distanceweighted (IDW) interpolation in case of insufficient interpolation data. The result istheoretical radar coverage. Filtering of data to obtain true coverage is done applyinga mask visualizing only data that are actually seen by the sensor or areas of interest,e.g. containing deformation. The mask can be based on a high coherence threshold orhigh gain amplitudes. Overall it is a very useful tool for GBSAR measurements allowingprediction of measurements a priori given a surface model of the area of interest.

4.3.2. Transformations

Once data is projected and interpolated it is transformed into desired reference system.In this project surface model data was given in Universe Transverse Mercator (UTM)meridian zone 31N with European 1950 datum. GBSAR measurements were transformedto WGS84 geodetic coordinates to visualize data with Google EarthTM. The transforma-tion UTM (N,E,H) with ED50 datum to WGS84 (', �, ℎ) is done in five steps using ageoide model of Catalonia for the second step.

1. UTMED50 (N,E,H) → ED50 (', �,H)

2. ED50 (', �,H) → ED50 (', �, ℎ)

3. ED50 (', �, ℎ) → ED50 (X,Y,Z)

4. ED50 (X,Y,Z) → WGS84 (X,Y,Z)

5. WGS84 (X,Y,Z) → WGS84 (', �, ℎ)

where N is northing, E is easting, H is orthometric height, ' is latitude, � is longitudeand ℎ is ellipsoidal height.

22

5. Field Campaigns

Five field campaigns have been made to test the instrument in real world environments.The main goal of the field campaigns was to collect data necessary to develop GB-SARprocessing tools as well as discover limitations of the instrument and gain experience.

5.1. Port of Barcelona

The sensor was positioned at an altitude of 178 meters on the hill of Montjuıc beside theport admitting a good illuminating geometry of part of the port pointing the antennasdownwards. Instrument settings and acquisition data are summarized in Table 5.1.

Table 5.1.: Settings: Port of Barcelona

Height 178 mMax range 3000 mRange resolution 1 mCross-range resolution 5.1 mradImage Dimensions nx: 341, ny: 3000Acquisition time 15’Acquisitions 16Epochs 2

Four positions labeled a-d are marked in Figures 5.1 and 5.2 displaying the sensor viewand the corresponding amplitude image for easier interpretation of the image.

Note the distortion rings marked with b. These focusing distortions are due to thesideways movement of the cruise ships. The movement results in range measurementswith different distances to the same objects. When combined during the focusing thedistortions occur because the distance to the ships varied. The ideal scenario is completelystationary. The port is however a noisy environment where this will be difficult to achieve.A solution may be to measure in low season with little or no cruise ships. It may be betterto measure at night to capture a more placid scene. This would also be advantageous forthe atmospheric error contributions.

There are known subsidence deformations in a newly constructed pier at about 1800meters distance. The distance is however ten times the altitude of the sensor positionwhich only permits a 10% projected measurement of the point deformation. The port ofBarcelona covers a land area of more than 800 ha, which makes it difficult to reach allareas of interest with reasonable height to distance ratio.

Note the substantial loss of coherence at point a in Figure 5.3 in two consecutiveacquisitions although it is a large structure clearly visible in the amplitude image. Anexplanation is found in Figure 5.4 showing a low Signal to Noise Ratio (SNR). In fact

23

5. Field Campaigns

position a is outside of the -10dB SNR area receiving less than 10% of the transmittedenergy.

a b c

d

Figure 5.1.: Sensor view, Port of Barcelona. Note the locations marked a− d marked foreasier interpretation of sequent radar images.

a

b

c

d

Figure 5.2.: Amplitude image, Port of Barcelona. The letters a − d corresponds to thepositions marked in Figure 5.1.

24

5.1. Port of Barcelona

a

c

(a) Amplitude

a

c

(b) Coherence

Figure 5.3.: Loss of Coherence at point a.

a

b

c

d

Figure 5.4.: Thermal SNR, -3dB antenna beamwidth (blue line) and -10dB antennabeamwidth (red line)

25

5. Field Campaigns

5.2. L’Eixample, Barcelona

The sensor was positioned at the hill of Turo de la Rovira with a view of district L’Eixamplein central Barcelona. The scene is a completely urbanized area with high reflectivity. SeeFigure 5.5. These acquisitions served to test the long range capabilities of the instrumentand to test the adopted co-registration technique intended for satellite SAR images in anadvantageous environment. To evaluate the co-registration one of the acquisitions wasacquired rotating the linear scanner a few centimeters to provoke a slightly different point-ing direction of the antennas. Instrument settings and acquisition data are summarizedin Table 5.2.

Table 5.2.: Settings L’Eixample

Height 260 mMax range 4000 mRange resolution 1 mCross-range resolution 4.4 mradImage Dimensions nx: 401, ny: 4000Acquisition time 41’Acquisitions 3Epochs 1

The temple of Sagrada Famılia is marked in Figures 5.5 and 5.6 for easier interpretation.Note the absence of received backscatter outside the indicated -10dB beam width area.This measurement confirm the importance of the antenna beam width in Figure 5.6 asprevious seen in the acquisitions from the port of Barcelona.

Note the vast difference of coherence and phase created from co-registered images ver-sus non co-registered images in Figures 5.7(a)-5.7(d). Without co-registering the imagescontain only noise and the information is lost. In this particular case the coherencedrops significantly after three kilometers, indicating that it is problematic to measurelong distances. See Figure 5.7(b). Note the fringes in the phase in Figure 5.7(d) thatare provoked by the rotation of the linear scanner. The rotation induces the slave imageto have slightly different ranges to the same targets with respect to the master acquiredbefore the rotation.

26

5.2. L’Eixample, Barcelona

a

Figure 5.5.: View of L’Eixample from the sensor position at Turo de la Rovira. NoteSagrada Famılia indicated with an a.

a

Figure 5.6.: Amplitude image of L’Eixample. The width of the -10dB antenna lobes aremarked with red dashed lines. Note Sagrada Famılia marked with an a.

27

5. Field Campaigns

(a) Coherence calculated withoutco-registration of input data.

(b) Phase calculated without co-registration of input data.

aa

(c) Coherence from co-registrereddata.

aa

(d) Phase from co-registrered data.

Figure 5.7.: Note the fringes in the phase. In this particular case the fringes are due tothe rotation of the sensor. Note Sagrada Famılia marked with an a.

28

5.3. Castelldefels

5.3. Castelldefels

In contrast to previous acquisition positions, the system was positioned at low altitudepointing the antennas slightly upwards. The scene consists of mountain area, the village ofCastelldefels and a 16th century castle. See Figure 5.8. The illuminated area is rather hillymaking it suitable for geocoding of radar data using a Digital Elevation Model (DEM). Thescene consists of a large vegetated area, which is difficult to capture due to entropic nature.To minimize this problem attempts were made to make faster acquisitions using singlecalibration configuration. Some acquisitions were also acquired with increased transmittedpower. Forty acquisitions were made at seven epochs to compare coherence obtained fromimages with different temporal baselines. Instrument settings and acquisition data aresummarized in Table 5.3.

Table 5.3.: Settings Castelldefels

Height 7 mMax range 4000 mRange resolution 1 mCross-range resolution 4.4 mradImage Dimensions nx: 401, ny: 4002Acquisition time 23’ (7’ single calibration)Acquisitions 40Epochs 7

Four position are marked in Figure 5.8 for easier interpretation. Note the mountain ridgesat a and c mainly covered with vegetation. At point b is the castle and the village of theCastelldefels at d. Three different acquisition configurations are compared and someobservations are presented below. The default setting is compared to increased antennapower and single calibration configuration allowing a faster acquisition.

∙ Normal antenna power. Coherence and amplitude with offset vectors obtained fromthe resampling for the standard antenna power are shown in Figures 5.9(a) - 5.9(d).The first image pair with a temporal baseline of 24 minutes shows good results whilesecond image pair with 48 minutes shows poor results. There is a shift obtained bythe fitting in far range.

∙ Increased antenna power. Figures 5.10(a) - 5.10(d). This image pair shows oppo-site results compared to normal transmitted antenna power. The longer temporalbaseline show higher coherence.

∙ Single calibration configuration. Figures 5.11(a) - 5.11(b). The fast acquisitionsshow by far the best results. Note that the single calibration leaves an artifactalong the centerline of the images as seen in the amplitude image.

All data are calculated from consecutive acquisitions using identical co-registration param-eters. The co-registration is the decisive factor. Where correlating points are found thecoherence is high. The scene displays a difficult geometry since there is little informationin the upper part of the image to co-register the images. The concentration of correlation

29

5. Field Campaigns

points in near range admits a high degree of rotation which ruins the coherence. The farrange area is also very vegetated making the faster acquisition more suitable.

The Castelldefels test site served well for geocoding of data since the illuminated scenecorresponds well to the available Digital Elevation Model (DEM) in contrast to urban ar-eas. A 30x30 meter resolution DEM was resampled to 5x5 meter using bicubic convolutioninterpolation. See Figure 5.12.

a

b

c d

Figure 5.8.: Sensor view, Castelldefels. Note the mountain ridges at a and c mainly cov-ered with vegetation. At point b is a castle, and at point d the village ofCastelldefels.

30

5.3. Castelldefels

2009 Apr 14 12:42:28

500

1000

1500

2000

2500

3000

3500

4000

Ran

ge

100 200 300 400

Azimuth

Observations_distribution+offsets

(a) Offset vectors, ΔT 24’ (b) Coherence, ΔT 24’

2009 Apr 14 13:41:52

500

1000

1500

2000

2500

3000

3500

4000

Ran

ge

100 200 300 400

Azimuth

Observations_distribution+offsets

(c) Offset vectors, ΔT 48’ (d) Coherence, ΔT 48’

Figure 5.9.: Acquisitions made with normal transmitted power level. Offset vectors ob-tained with correlation windows threshold 0.8 using different temporal base-lines.

31

5. Field Campaigns

2009 Apr 14 12:03:54

500

1000

1500

2000

2500

3000

3500

4000

Ran

ge

100 200 300 400

Azimuth

Observations_distribution+offsets

(a) Offset vectors, ΔT 24’ (b) Coherence, ΔT 24’

2009 Apr 14 12:27:18

500

1000

1500

2000

2500

3000

3500

4000

Ran

ge

100 200 300 400

Azimuth

Observations_distribution+offsets

(c) Offset vectors, ΔT 48’ (d) Coherence, ΔT 48’

Figure 5.10.: Acquisitions made with increased transmitted antenna power level. Off-set vectors obtained with correlation windows threshold 0.8 using differenttemporal baselines.

32

5.3. Castelldefels

2009 Apr 14 13:53:13

500

1000

1500

2000

2500

3000

3500

4000

Ran

ge

100 200 300 400

Azimuth

Observations_distribution+offsets

(a) Offset vectors, ΔT 21’

b

d

a

c

(b) Coherence, ΔT 21’

Figure 5.11.: Acquisitions made with single calibration configuration. Offset vectors ob-tained with correlation windows threshold 0.8. Fitting statistics of this co-registration is attached in Appendix A.2. The letters corresponds to to thelocations marked in Figure 5.8.

33

5. Field Campaigns

b

d

a

c

Figure 5.12.: Geocoding of Castelldefels visualized with Google EarthTM. The letterscorresponds to to the locations marked in Figure 5.8.

34

5.4. Sagrada Famılia, Barcelona

5.4. Sagrada Famılia, Barcelona

This campaign served primarily as a geocoding test of an object with a complicatedgeometry and was an attempt to integrate data from different sensors. The integration ofsensors will not be discussed here, see Marambio et al. (2009). The ”El Nacimiento” facadeof the Sagrada Famılia temple in Barcelona was chosen because of its complexity. In thiscase the surface model was made with a RIEGL LMS Z420i Laser Scanner equipped witha calibrated Nikon D100 6 Mega Pixel digital camera. The laser scanner measurementswere conducted by the Virtual City Modeling Lab, Technical University of Catalonia(UPC). Instrument settings and acquisition data are summarized in Table 5.4.

Table 5.4.: Settings Sagrada Famılia

Height 7 mMax range 400 mRange resolution 0.5 mCross-range resolution 4.4 mradImage Dimensions nx: 401, ny: 801Acquisition time 6’Acquisitions 6Epochs 1

In Figure 5.15 an amplitude image show the facade of Sagrada Famılia at a distance of120-160 meters. At this distance the pixel size is about 50×50 centimeters at the bottomom the building growing in the cross-range direction to about 50×70 centimeters at the topof the building. This measurement show the importance of geocoding for interpretationof data.

The point cloud consisting of more 5.3 million points were projected into polar radargeometry. The pointcloud is shown in Figure 5.14. Multiple points assigned to equalpixels were interpolated resulting in a reduction of the point cloud to 6047 radar pixels.These pixels are visualized with Google EarthTMin Figure 5.16.

35

5. Field Campaigns

Figure 5.13.: Sagrada Famılia

Figure 5.14.: Sagrada Famılia Pointcloud from Terrestial Laser Scanner.

36

5.4. Sagrada Famılia, Barcelona

Figure 5.15.: SAR amplitude image of the facade of Sagrada Famılia at a range of 120–150 meters. Note the focusing distortions from moving objects during theacquisition. In this case there were moving construction cranes.

Figure 5.16.: Geocoding of Sagrada Famılia. Colors represent orthometric height, butcould be visualized with respect to arbitrary information, e.g. deformationif such data were available.

37

5. Field Campaigns

5.5. Montserrat

The Montserrat mountain is situated about 50 kilometers northeast from Barcelona. Itis considered one of the most important mountains in Catalonia because of its thousandyears pilgrimage history and its monastery situated at 720 meters height. The mountainreaches 1236 meters above sea level and consists of conglomerate sedimentary rocks. Themountain has a history of rockfall caused by a combination of the conglomerate charac-teristics and its steep geometry. A recent rockfall in December 2008 detaching more than100 m3 of rocks, closed the only road to the monastery, immuring more than 200 carsand damaged a rail road. Instrument settings and acquisition data are summarized inTable 5.5.

Table 5.5.: Settings Montserrat

Height 218 mMax range 2001 mRange resolution 0.5 mCross-range resolution 4.4 mradImage Dimensions nx: 401, ny: 4002Acquisition time 7’Acquisitions 42Epochs 3

The sensor was placed at 218 m altitude with antennas pointed upwards at the rock fallzone. A small concrete base was casted as a stable support permitting repositioning ofthe instrument. All acquired images were co-registered in post-processing.

See sensor view from instrument position in Figure 5.17, with four positions markedfor comparison with amplitude image in Figure 5.19(a). The zone of interest is situatedat about 1200 m range, marked with a. The afflicted road and railway are marked withc and d respectively.

Using a maximum spatial resolution of 0.5 m in range and 4.4 mrad in the cross rangedirection a pixel dimension in the rockfall zone of just over 0.5 by 5 m is obtained.

All the processing tools developed were utilized for the processing. Some observationsare presented below.

∙ Amplitude (Figure 5.19(a)). Observing the photo it is hard to realize that the moun-tain peak b is significantly further away. The mountain peak b situated about 300m further away than the rock fall zone. This is clearly seen in the amplitude imagein Figure 5.19(a) and highlights the importance of geocoding the GB-SAR data tointerpret them. The amplitude image is obtained from input data constructed frommultiple images to reduce noise. This is accomplished by calculating the vector sumof the quadrature components of consecutive acquisitions.

∙ Coherence (Figure 5.19(b)). Note the loss of coherence in the central part of ofthe image and the lower part of the scene below the railway d compared with theamplitude image. This is due to the non-coherent vegetation surface characteristics.

∙ Interferometric Phase (Figure 5.19(c)). Note the phase cycle slip due to the atmo-sphere.

38

5.5. Montserrat

a b

c

d

Figure 5.17.: Sensor view, Montserrat. The zone of interest is at point a. Point b is amountain peak situated about 300 m further away from point b. Point c isa road and point d is a railway, both damaged by rockfall.

∙ Unwrapped Phase (Figure 5.19(d)). Note how the unwrapped phase resides in therange [-10,30] even though the extreme values are well out of the zone of interestand may be considered noise. This is necessary step to be able to estimate theatmospheric effects using a plane model.

∙ Linear Term Corrected (Figure 5.20(a)). Compared with previous step the phase isnow homogeneous except for the mountain peak. This is due to the separation ofthe peak in the image. The phase unwrapping only works for connected pixels.

∙ Normalized Phase (Figure 5.20(b)). In this image the aliasing errors from the phaseunwrapping is clearly seen at the mountain peak and at the bottom of the image.However this may be accepted since they are not part of the zone of interest.

∙ Time Series (Figure 5.21). The data obtained from the processing steps were cat-egorized in continuous and discontinuous acquired data. The hypothesis for thecontinuous data is that no deformation is expected. These data served to evalu-ate the noise levels expected for the discontinuously acquired measurements with atemporal baseline of about two weeks for each band. This figure shows the spec-tral profile of a pixel in the zone of interest with a temporal baseline between theillustrated bands of seven minutes. Note that the vertical axis is in radians. Theconversion factor from radians to millimeters is �

4�≈ 1.4mm obtained with wave-

length 18 mm and Equation 2.7. The maximum deviation from zero deformation isat band four of about 0.15 radians which must be considered noise or error inducedby the assumptions made during the processing.

39

5. Field Campaigns

Further analyses should be devoted to key rockfall characteristics, such as volume ofexpected rockfalls, study of precursory movements and the duration of such movements.

The geocoding was done projecting the radar geometry over a Digital Elevation Model(DEM). See Figure 5.18. As in the case of the Castelldefels acquisitions the DEM wasinterpolated from 30x30 meters to 5x5 meters using bicubic convolution. The colorsrepresent orthometric height but could represent deformation in case of such data wereavailable.

a b

cd

Figure 5.18.: Montserrat geocoding. The letters correspond to the locations shown inFigure 5.17. Note point b in comparison with Figure 5.17, which highlightsthe importance of geocoding the GB-SAR data to interpret them.

40

5.5. Montserrat

a

b

c d

(a) Amplitude (b) Coherence

(c) Phase (d) Unwrapped Phase

Figure 5.19.: Montserrat processing steps. The letters correspond to the locations shownin Figure 5.17.

41

5. Field Campaigns

(a) Linear term corrected

a

b

c d

(b) Phase normalized to referencepoint

Figure 5.20.: Montserrat processing steps (continued). The letters correspond to the lo-cations shown in Figure 5.17.

Figure 5.21.: A Time Series of a pixel at point a highlighted in Figure 5.20(b).

42

6. Conclusions

The system characteristics of a GB-SAR, the processing steps and the preliminary resultsfrom GB-SAR data acquired in different environments have been presented. The mainpart of this work has been the development of the processing tools. In particular theimage co-registration and geocoding which was started from zero.

To accomplish this the field campaigns have been important to collect data neededfor the development of the processing tools. Through these campaigns critical aspectsof the data collection and processing have been learned. The key observations of theacquisitioning and processing of data collected in the field campaigns are presented below.

∙ It is the necessary to access a good observation position that allows a good illumi-nating geometry. This is important in many aspects. The first aspect concerns theantenna beamwidth since the antenna receives the strongest signal in the pointingdirection of the antennas. This implies that the system must be placed so the areaof interest is found in center of the beamwidth.

Furthermore the expected deformation direction to measured has to be consideredwhen planning a new site, since the measurements are along to the Line Of Sightof the antennas. This may require the sensor to be placed on a high or low positionwith respect to to the area of interest to obtain a reasonable height to distance rationeeded to calculate a projected displacement.

A good observation position should also produce images that include stable areasthat the measurements can be referenced to. Furthermore it is advantageous for theco-registration if strong backscatter targets are found throughout the whole scene.

∙ Co-registration is a key data processing step fundamental to conduct interferometricmeasurements. The co-registration result depends largely on the geometry and thereflectivity of the captured scene. Several types of measurements have been con-ducted to study co-registration results with different temporal baselines. The imageco-registration have been successfully implemented using a free InSAR processorintended for satellite data.

∙ Coherence is a particularly important quality estimator of the measurement. Onlyhigh level coherence data is useful for deformation measurement. Analyzed datacollected in the field campaigns have demonstrated that coherence can be preservedwith a long temporal baseline showing feasible results for deformation measurement.

∙ Geocoding is necessary to assess where analyzed data is found. To conduct thegeocoding an accurate surface model is necessary. Depending on the scale of theobserved scene an existing Digital Elevation Model may be sufficient, otherwisesurface data can be acquired from external sensors such as an Terrestrial LaserScanner.

43

6. Conclusions

As a final word, it is difficult to fully understand a complex system such as the GB-SARdespite it being a commercial system. For each application type a lot of work should bedone to assess the applicability of the system.

44

References

Bernardini, G., Pasquale, G. D., Bicci, A., Marra, M., Coppi, F., and Ricci, P. (2007a).Microwave interferometer for ambient vibration measurement on civil engineering struc-tures: 1. Principles of the radar technique and laboratory tests. In Proc. ExperimentalVibration Analysis for Civil Engineering Structures (EVACES’07).

Bernardini, G., Pasquale, G. D., Gallino, N., and Gentile, C. (2007b). Microwave in-terferometer for ambient vibration measurement on civil engineering structures: 2.Application to full-scale bridges. In Proc. Experimental Vibration Analysis for CivilEngineering Structures (EVACES’07).

Costantini, M. (1998). A novel phase unwrapping method based on Network Program-ming. IEEE Transactions on Geoscience and Remote Sensing, 36(3):813–821.

Hanssen, R. F. (2001). Radar Interferometry. Kluwer Academic Publishers, Dordrecht.

Kampes, B. and Usai, S. (1999). Doris: The Delft object-oriented radar interferometricsoftware. In Proc. 2nd Int. Symp. Operationalization of Remote Sensing.

Marambio, A., Pucci, B., Jungner, A., Nunez, M., and Buill, F. (2009). Terrestrial LaserScanner, Terrestrial Synthetic Aperture Radar and Topographic Data: An IntegrationProposal. In Proc. 8th International Geomatics Week, Barcelona.

Noferini, L. (2004). Processing techniques of microwave data acquired by Continuous WaveStepped Frequency Radar. PhD thesis, Universita degli Studi di Firenze.

Skolnik, M. I., editor (1990). Radar Handbook. McGraw-Hill Professional, New York, 2ndedition.

45

A. Appendix

A.1. Co-registration Parameter File

The co-registration parameters needs satellite pseudo data in order to work with theInSAR processor used. In this case ERS-1 data is used with doppler values (Xtrack_f_DC)set to zero.

TU DELFT - DEOS

=====================================================

SLAVE RESULTFILE: slave.out

InSAR Processor: Doris (Delft oo radar interferometric software)

Version: Version 2.6 (debug)

VECLIB library: not used

LAPACK library: not used

Compiled at: Aug 10 2000 15:10:01

By GNU gcc: 2.95.2

Creation of this file: Jan 26, 2009 (Monday)

=====================================================

Start_process_control

readfiles: 1

precise_orbits: 1

crop: 1

resample: 1

filt_azi: 0

filt_range: 0

NOT_USED: 0

End_process_control

*====================================================================*

| |

Following part is appended at: Thu Aug 10 15:48:43 2000

| |

*--------------------------------------------------------------------*

47

A. Appendix

*******************************************************************

*_Start_readfiles:

*******************************************************************

Volume file: /cdrom/scene1/vdf_dat.001

Volume_ID: 1

Volume_identifier: 0004094000014024

Volume_set_identifier: 19950726 9492285

(Check)Number of records in ref. file: 26519

Product type specifier: PRODUCT:ERS-1.SAR.SLC

Location and date/time of product creation: GENERATED AT I-PAF

Scene identification: ORBIT 21066 DATE 26-JUL-1995 9:49:22

Scene location: FRAME 2781 LAT: 40.94 LON: 14.02

Leader file: /cdrom/scene1/lea_01.001

Scene_centre_latitude: 40.9400000

Scene_centre_longitude: 14.0240000

Radar_wavelength (m): 0.0566660

First_pixel_azimuth_time (UTC): 26-JUL-1995 09:49:23.394

Pulse_Repetition_Frequency (computed, Hz): 1679.94931897

Total_azimuth_band_width (Hz): 1378.0000000

Xtrack_f_DC_constant (Hz, early edge): 0.0000

Xtrack_f_DC_linear (Hz/s, early edge): 0.0000000

Xtrack_f_DC_quadratic (Hz/s/s, early edge): 0.00000

Range_time_to_first_pixel (2way) (ms): 5.5458330

Range_sampling_rate (computed, MHz): 18.9662980496

Total_range_band_width (MHz): 15.5500000

*******************************************************************

*******************************************************************

Datafile: /cdrom/scene1/dat_01.001

Number_of_lines_original: 4002

Number_of_pixels_original: 401

*******************************************************************

* End_readfiles:_NORMAL

*******************************************************************

*******************************************************************

*_Start_crop: slave step01

*******************************************************************

Data_output_file: iq_2009.01.22_13.24.21_montserrat1_tp4_05m.bin

Data_output_format: complex_real4

First_line (w.r.t. original_image): 1

Last_line (w.r.t. original_image): 4002

First_pixel (w.r.t. original_image): 1

Last_pixel (w.r.t. original_image): 401

48

A.1. Co-registration Parameter File

*******************************************************************

* End_crop:_NORMAL

*******************************************************************

*******************************************************************

*_Start_precise_orbits:

*******************************************************************

t(s) X(m) Y(m) Z(m)

NUMBER_OF_DATAPOINTS: 27

35358.000000 5151580.064 1646538.645 4689765.110

35359.000000 5156717.560 1646224.699 4684240.566

35360.000000 5161849.442 1645908.227 4678710.927

35361.000000 5166975.704 1645589.230 4673176.199

35362.000000 5172096.340 1645267.708 4667636.387

35363.000000 5177211.345 1644943.664 4662091.497

35364.000000 5182320.713 1644617.098 4656541.536

35365.000000 5187424.437 1644288.011 4650986.509

35366.000000 5192522.513 1643956.405 4645426.423

35367.000000 5197614.933 1643622.281 4639861.283

35368.000000 5202701.692 1643285.640 4634291.095

35369.000000 5207782.784 1642946.483 4628715.867

35370.000000 5212858.204 1642604.811 4623135.603

35371.000000 5217927.945 1642260.626 4617550.309

35372.000000 5222992.002 1641913.928 4611959.992

35373.000000 5228050.368 1641564.720 4606364.658

35374.000000 5233103.038 1641213.002 4600764.313

35375.000000 5238150.007 1640858.775 4595158.964

35376.000000 5243191.267 1640502.040 4589548.615

35377.000000 5248226.814 1640142.800 4583933.273

35378.000000 5253256.642 1639781.054 4578312.945

35379.000000 5258280.744 1639416.805 4572687.636

35380.000000 5263299.115 1639050.053 4567057.352

35381.000000 5268311.750 1638680.800 4561422.100

35382.000000 5273318.642 1638309.047 4555781.886

35383.000000 5278319.785 1637934.794 4550136.716

35384.000000 5283315.175 1637558.045 4544486.596

*******************************************************************

* End_precise_orbits:_NORMAL

*******************************************************************

*====================================================================*

| |

Following part is appended at: Mon Jan 26 11:19:20 2009

49

A. Appendix

| |

*--------------------------------------------------------------------*

*******************************************************************

*_Start_resample:

*******************************************************************

Shifted azimuth spectrum: 0

Data_output_file: iq_2009.01.22_13.24.21_montserrat1_tp4_05m.resampled.bin

Data_output_format: complex_real4

Interpolation kernel: 6 point truncated sinc

First_line (w.r.t. original_master): 1

Last_line (w.r.t. original_master): 4002

First_pixel (w.r.t. original_master): 1

Last_pixel (w.r.t. original_master): 401

*******************************************************************

* End_resample:_NORMAL

*******************************************************************

Current time: Mon Jan 26 11:19:53 2009

A.2. Co-registration Statistics

Degree of model: 1

Threshold on data (correlation): 0.8

Oversmaplings factor used in fine: 16

This means maximum can be found within [samples]: 0.03125

A priori sigma azimuth (based on experience): 0.15

A priori sigma range (based on experience): 0.1

Number of observations: 8008

Number of rejected observations: 160

Number of unknowns: 3

Overall model test in Azimuth direction: 0.00586746

Overall model test in Range direction: 0.0522292

Largest w test statistic in Azimuth direction: 1.26765

for window number: 539

Largest w test statistic in Range direction: 1.90333

for window number: 539

Maximum deviation from unity Normalmatrix*Covar(unknowns): 2.70143e-16

Estimated parameters in Azimuth direction

x_hat std

(a00 | a10 a01 | a20 a11 a02 | a30 a21 a12 a03 | ...)

0.0006 0.0152

50

A.2. Co-registration Statistics

-0.0004 0.0140

-0.0023 0.0448

Estimated parameters in Range direction

(b00 | b10 b01 | b20 b11 b02 | b30 b21 b12 b03 | ...)

0.0146 0.0002

0.0096 0.0002

-0.0184 0.0020

Covariance matrix estimated parameters:

---------------------------------------

0.0002 0.0001 -0.0001

0.0001 0.0002 0.0001

-0.0001 0.0001 0.0020

*******************************************************************

Current time: Tue Apr 14 13:53:10 2009

*******************************************************************

*_Start_resample

Data_output_file: slave32.resampled.bin

Data_output_format: complex_real4

Interpolation kernel: 6 point truncated sinc

Resampled slave size in master system: 1, 4003, 1, 401

*******************************************************************

Current time: Tue Apr 14 13:53:27 2009

51

52

Reports in Geographic Information Technology 2009

The TRITA-GIT Series - ISSN 1653-5227

2009

09-001 Ahmed Abdallah. Determination of a gravimetric geoid if Sudan using the KTH method. Master ofScience thesis in geodesy No.3109. Supervisor: Huaan Fan. Janaury 2009.

09-002 Hussein Mohammed Ahmed Elhadi. GIS, a tool for pavement management. Master of Science thesis in geoinformatics. Supervisor: Hans Hauska. February 2009.

09-003 Robert Odolinski and Johan Sunna. Detaljmätning med nätverks-RTK – en

noggrannhetsundersökning (Detail surveying with network RTK – an accuracy research). Master of

Science thesis in geodesy No.3110. Supervisor: Clas-Göran Persson and Milan Horemuz. March 2009.

09-004 Jenny Illerstam och Susanna Bosrup. Restfelshantering med Natural Neighbour och TRIAD vid byteav koordinatsystem i plan och höjd. Master of science thesis in geodesy No. 3111. Supervisor: MilanHoremuz and Lars Engberg. March 2009.

09-005 Erik Olsson. Exporting 3D Geoinformation from Baggis Database to CityGML. Supervisors: Peter

Axelsson and Yifang Ban. April 2009.

09-006 Henrik Nilsson. Referenssystemsbyte i Oskarshamns kommun – en fallstudie (Change of reference

systems in Oskarshamn – a case study). Master’s of Science thesis in geodesy No.3112. Supervisor:

Huaan Fan. May 2009.

09-007 Chi-Hao Poon. Interaktiv Multikriteria-Analys (Interactive Multi-Criteria Evaluation). Supervisor:Mats Dunkars and Yifang Ban. May 2009.

09-008 Emma Lundberg. Fastighetsdokumentation – en jämförelse mellan två geodetiska tekniker. Master’s

of Science thesis in geodesy No.3113. Supervisor: Milan Horemuz, Karin Klasén and Ivar Andersson.

May 2009.

09-009 Andenet Ashagrie Gedamu. Testing the Accuracy of Handheld GPS Receivers and Satellite Image forLand Registration. Master’s of Science thesis in geodesy No.3114. Supervisor: Milan Horemuz andLars Palm. May 2009.

09-010 Abubeker Worake Ahmed and Workaferahu Abebe Mergia. Determination of transformation

parameters between WGS 84 and ADINDAN. Master’s of Science thesis in geodesy No.3115.

Supervisor: Huaan Fan. May 2009.

09-011 Andreas Jungner. Ground-Based Synthetic Aperture Radar Data Processing for Deformation

Measurement. Master’s of Science thesis in geodesy No.3116. Supervisors: Milan Horemuz and

Michele Crosetto. May 2009.

TRITA-GIT EX 09-11

ISSN 1653-5227

ISRN KTH/GIT/EX--09/011-SE