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BACHELOR THESIS TOPIC SAR INTERFEROMETRY IN MOUNTAINOUS AREAS

Context Synthetic Aperture Radar Interferometry is a well established technique allowing to retrieve information about surface model or deformation. The main limitation of this technique is the validity of the domain of application. Indeed, application of interferometry depends on many parameters and terrain slopes, making it difficult to apply it on very mountainous areas.

Fortunately, thanks to the so-called range spectral filtering, it is possible to overcome this problematic.

!Thesis objective The aim of the bachelor thesis is to investigate the potential of a full topographic dependent range spectral filter over different scenario: subduction, earthquake, volcano, and to quantify the observed improvement.

!Expected profile Applicant should have an interest in radar remote sensing. Knowledge in synthetic aperture radar would be a plus. Programming skills are required (C++).

!Contact The master thesis student will be working at the Computer Vision and Remote Sensing Department under the supervision of Dr. Stéphane Guillaso

Interested candidates are invited to contact Stéphane Guillaso (stephane.guillaso@tu-berlin.de) for more information or to submit the candidacy.

TOPOGRAPHIC RANGE SPECTRAL FILTERING

InSAR measurement of! interseismic strain in areas of low coherence: example across the Haiyuan fault (Gansu, China) using a local InSAR adaptive range filter.

Stéphane Guillaso (1), Marie-Pierre Doin (1), Cécile Lasserre (1), Olivier Cavalié (2), Sun Jianbao (3), Gilles Peltzer (4)

(1) Laboratoire de Géologie, ENS, Paris, France (2) LGIT, Université Joseph Fourier, Grenoble, France (3) Chinese Academy of Sciences, Benjing, China (4) University of California, Los Angeles, United States

INTRODUCTION

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Velocity of fault

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ANALYSIS STRATEGY

- Simulated interferometric phase used to

coregistrate data in range

- Amplitude correlation used for azimuth

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(a)

(b)

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Figure 1: Seismotectonic map of the Haiyuan fault system

This study concerns the measurement of the interseismic deformation

across the western Haiyuan fault (Fig. 1), one of the major strike slip

fault in China.

A previous InSAR study shows that along the two easternmost SAR

tracks, a steep velocity gradient has been observed across!the fault,

consistent with a left-lateral slip at a rate of 6.3±2 mm/yr below a small

apparent locking depth (<2 km), which may be indicative of transient

superficial creep or related to a weak fault zone [Cavalié et al. 2008].

The western track has not been studied yet as it covers a very high

mountainous area, which introduces strong geometrical decorrelation.

In this study, a topographic adaptive range spectral filter is proposed in

order to improve phase coherence over the mountainous study area.

measured spectrum of image 1

ground object spectrum

f̂g

f̂z

∆f∆f

f̂1

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measured spectrum of image 2

common part of spectra

θ1

θ2

altitude

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near range middle range far range

Using ROI_PAC Using our topographic range spectral filtering

range range

azim

uth

azim

uth

469 - 0.47

259 - 0.18

059 - 0.18

160 - 0.08

029 - 1.71

473 - 1.33

160 - 2.48

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279 - 0.56 235 - 2.01

Temporal baseline0 1 2 3

0

400

300

200

100

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ase

line

CONCLUSION AND FUTURE WORK

Figure 4: Spectral shift principle in spectral domain

Figure 5: Range dependance of spectral common part

2003 2004 2005 2006 2007 2008

Temporal baseline [year]

-200

0

200

400

600

800

1000

1200

Norm

al baselin

e [m

]

B_N < 50m & B_T < 5y

B_N < 300m & B_T < 3y

B_N < 500m & B_T < 1.5y

Unwrapping

Orbit / Atmospheric Correction

Figure 3: Interferogram selection graph

Figure 2: Block diagram of the analysis strategy

n = n + 1

oversampling (x2)

DEM

estimate simulated topographic phase

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estimate local topographic phase slope

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u1,2(x, n) = u1,2(x, y)ei±�(x,n)2

range topographic spectral filtering

u1,2(x, n) = u1,2(x, n) � {�f(x, n) · sinc(�f(x, n))}

undersampling (x2)

interferogram generation

i(x, n) = u1(x, n)u�2(x, n)

u1,2(x, n)

Figure 7: Interferogram generation block diagram

frequency

am

plit

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frequency domain

range

am

plit

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e

spatial domain

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range spectral shift

topographic range

spectral filtering

interferogram generation(variable

resolution)ra

ng

e

ran

ge

ran

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frequency frequency frequency frequency

Figure 6: topographic range spectral filtering principle

Figure 8: Comparison between standard ROI_PAC chain and our topographic range spectral filtering (473m normal baseline, 1.3 y temporal baseline)

Figure 9: Interferogram results the track 104 (ENVISAT)

Raw

interferogram

filtered

interferogram

unwrapped

filtered

interferogram

Residue (±!)

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high pass

interferometric

phase

–

interferometric

phase DEM

correction

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+

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–

Figure 10: DEM correction strategy Figure 11: DEM correction, test over small area

" The proposed topographic range spectral filtering in the spatial domain gives very promising results in order

to generate high coherence interferograms in mountainous areas.

" It also makes possible the generation of interferograms with large normal baselines.

" An improvement of the DEM, using a set of interferograms with small baselines, is needed.

" The next steps are to correct orbital errors and to remove atmospheric phase delays following Cavalié et al.

2008, to provide information about surface velocity, particularly in the western, mountainous part of the

track.

" Results will be combined with adjacent track (333) to the east (1/3 common part).

REFERENCESCavalié et al. "Measurement of interseismic strain across the Haiyuan fault (Gansu, China), by InSAR",

submitted to Earth Planet. Sci. Lett. 2008 (in revision)

Gattteli et al. "The wavenumber shift in SAR interferometry, " IEEE Trans. Geosci. Remote Sens., vol. 29, no. 5,

pp. 855-864, 1994

Kampes, "Radar Interferometry: Persistent Scatterer Technique", Remote Sensing and Digital Image Processing

vol 12, Springer, 2006

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