京都大学教育研究振興財団助成事業
成 果 報 告 書
平成26年11月9日
公益財団法人京都大学教育研究振興財団

辻会 長

井昭

雄

様

所 属 部 局：

Graduate School of Engineering

職名：D2氏名：

Tamer ElGharbawi

助 成 の 種 類

平成２６年度 ・ 若手研究者在外研究支援 ・ 国際研究集会発表助成

事

業

内容

IGARSS 2014

開催

期

間

平成26年7月13日～平成26年7月18日

開催

場

所The Québec City Convention Centre, Québec, Canada

参加者

成 果 の 概 要

総 数: Over 300

内 訳: America, Austria, Australia, Canada, China et al.

タイトルは「成果の概要／報告者名」として、A４版２０００字程度・和文で作成し、添付して下さい。 「成果の概要」以外に添付する資料 ☑ 無□ 有()
事業に要した経費総額

300,575円

うち当財団からの助成額

250,000円(機関や資金の名称)

その他の資金の出所

経 費 の 内 訳 と 助 成 金 の 使 途 に つ い て費会

計報

告目

Registration FeeAir Ticket
Transportation (Airport to Hotel)
Accommodation
Insurance
VISA Application
Total

金額（円）

財団助成充当額 （円）21,528
163,513
12,00081,4004,520
17,614
300,575

21,528
163,513
12,00052,95900
250,000*( Foreign Exchange Rates is based on 2014.07.1)（今回の助成に対する感想、今後の助成に望むこと等お書き下さい。助成事業の参考にさせていただきます。）

当財団の助成についてReport on the International Geoscience and Remote Sensing
Symposium (IGARSS 2014)To:

The Kyoto University Foundation
E-mail: info@kyodai-zaidan.or.jp

By:

Tamer ElGharbawi
E-mail: elgharbawi.mosaad.64z@st.kyoto-u.ac.jp
PhD Student, under supervision of Professor Masayuki Tamura
Kyoto University, Katsura Campus
Department of Civil and Earth Resources Engineering
Geinformatics Lab.

First of all, I would like to express my gratitude to Education and Research Revitalization
Consortium of Kyoto University (京都大学教育研究振興財団) for giving me theopportunity to attend the IGARSS 2014 Symposium.

Introduction
This is a comprehensive report on the IGARSS 2014 event. This report aims to identify themain features and contributions of this event.

Event description
The International Geoscience and Remote Sensing Symposium (IGARSS 2014) / 35thCanadian Symposium on Remote Sensing (35th CSRS).
The symposium theme is “Energy and our Changing Planet”. The development of new andrenewable sources of energy in the context of a changing planet is a critical issue throughout
the world.
This partnership between the IEEE Geoscience and Remote Sensing Society (GRSS) andthe Canadian Remote Sensing Society / Société canadienne de télédétection (CRSSSCT) builds on a fine tradition of collaboration that began over 25 years ago.
http://www.igarss2014.org/default.asp

Conference Venue
The Québec City Convention Centre, which won the 2006 Apex Award for the World’s Best
Congress Centre bestowed by the prestigious International Association of Congress Centers (IACC ) was selected to host IGARSS 2014/35th CSRS.

Our contribution
An oral presentation was successfully delivered at the conference on Tuesday, July 15, 2014

Session Chairs:
Clemence Dubois, Karlsruhe Institute of Technology
Michael Henschel , MDA Corporation

Paper Detail
Paper:

TU4.01.4

Session:INSAR Applications IIITime:

Tuesday, July 15, 16:40 - 17:00

Presentation: Oral
Topic:

Analysis Techniques: InSAR and High Resolution SAR

Title:SURFACE
DEFORMATIONMONITORINGUSINGSARINTERFEROGRAMS AND GPS OBSERVABLES: APPLICATION TOTOKYO, JAPANAuthors:

Tamer Elgharbawi; Kyoto University
Masayuki Tamura; Kyoto University

To watch a recording of the presentation please visit
http://www.igarss2014.org/ShowRecording.asp?C=C38C47A6
A copy of the published paper is also included in this reportSURFACE DEFORMATION MONITORING USING SAR INTERFEROGRAMS AND GPS
OBSERVABLES: APPLICATION TO TOKYO, JAPAN
Tamer ElGharbawi and Masayuki Tamura
Department of Civil and Earth Resources Engineering, Kyoto University, Kyoto, Japan
series least squares analysis combined with GPS based
supervised spatial phase filtering. This method is presented
to overcome some of the limitations that prevent anadequate application of the existing methods. In thisanalysis we didn’t have the luxury of choosing a normal
baseline threshold other than the critical one. With the aid ofleast squares analysis and GPS based spatial filtering wemanaged to estimate crustal deformation time series withaccuracy of millimeters level using only 10 interferograms.
The differential unwrapped phase maps were analyzed using
least squares method to obtain the most probabledeformation values for each epoch of the study time frame.
After estimating the deformation maps we used the GNSS
Earth Observation Network System’s (GEONET) GPS
stations for supervised spatial phase filtering, this method
was presented by ElGharbawi and Tamura [3].
ElGharbawi and Tamura [3] used more than 30 GPS
stations to model and correct the tropospheric delay andintroduced the application of GPS based supervised spatial
phase filtering. Using GPS tropospheric delay to correct theInSAR deformation maps will generate interpolation errors
between GPS stations even when using a dense GPSnetwork like GEONET. In addition, the availability of morethan 30 operational GPS station within and around the SAR
scene can be very challenging for many sites around theworld. That’s why we used the least squares analysis as atemporal filter to reduce the tropospheric effect among othererrors and we used GPS stations in supervised spatial phasefiltering which will reduce the number of needed GPSstations down to four or five operational stations with in theSAR scene.
The use of GPS stations observables in spatial phasefiltering was necessary because of the special nature of
deformations occurred due to the 2011 Tohoku Earthquake.
Because of that earthquake the whole Tohoku Island wasshifted and the deformation signal was presented as longinterferometric wave pattern dominating the entire
deformation maps leaving no stable area that can be
identified to facilitate the differentiation between actual
deformations and other imposed biases.

ABSTRACTThis paper presents a method to monitor surface
deformations using SAR interferometry and GPS
observables. In this analysis we used least squares to reducethe errors presented in InSAR deformation maps temporally
then we used a GPS based spatial phase filtering to spatiallyfilter the estimated deformation maps. This method cangenerate crustal deformation time series with geodetic
accuracy using only 10 interferograms and few operational
GPS stations. The proposed method was tested on Tokyobay area, Japan which has been affected by the 2011
Tohoku earthquake. The results were verified against
deformations detected by GPS stations showing a mean
standard deviation of 7.9 millimeters.
Index Terms— InSAR, GPS, Deformation Monitoring,
2011 Tohoku Earthquake1. INTRODUCTION
Surface deformation monitoring using differential SAR
interferometry has been established and used successfully tomeasure and study terrain deformations due to several
phenomena by numerous researchers. Because of the errors
presented in InSAR deformation maps several researchers
have presented valuable methods for InSAR stacking andtime series analysis that can produce an accurate
deformation time series. Berardino et al. [1] have presented
Small Baseline (SBAS) approach which uses the singularvalue decomposition (SVD) to link independent SAR
acquisition datasets. They validated their approach using 70
differential interferograms of southern Italy with amaximum normal baseline of 130 m., Schmidt andBurgmann [2] have used least squares inversion ofdifferential interferograms to estimate the incremental rangechange between SAR acquisitions. They processed 115
differential interferograms of Santa Clara valley, California,
with a maximum normal baseline of 200 m. They stated thata minimum number of 30 interferograms are required toproduce a reasonable time series.
The main target of this paper is estimating the co-seismic
crustal deformations in Tokyo bay area which has been
affected by the 2011 Tohoku earthquake using InSAR time978-1-4799-5775-0/14/$31.00 ©2014 IEEE990

IGARSS 2014

2. METHODOLOGY
2.1. InSAR Stacking
Let’s consider the number of available SAR images for thesame area equal N and ordered in a time series [t1: tN]. Thenthe number of the unknown deformations for each pixel will
equal n = N-1 and can be identified by [d1: dn]. The
maximum number of differential interferograms that can be⁄(() ).
calculated isFor clarification let’s consider that we have N = 5 SARimages, then the unknown deformations will equal n=4
deformation segments [d1, d2, d3, d4] and the maximum
number of interferograms M = 10. Figure 1 illustrates theinterferogram structure for this example. Every horizontal
bar represent an interferogram starting at the master image(left side) and ending at slave image (right side) andcontaining the unknown deformation components thatrepresented by that interferogram. It is clear that every
unknown deformation in this example is presented in at least
4 interferogram. It is important that every unknown
deformation should be presented by as many as possible
interferograms. After the generation of interferograms;
flatting, topography removal, filtering and phaseunwrapping should be carried out using suitable methods.

Fig. 1. InSAR Stacking Structure.
The matrix (AT·A) is non-singular matrix; therefore
inverting this linear system should be simple. Considering
the example illustrated by Figure 1, the design matrix[] (5) and unknown deformation vector of the[]. It should be noted that thesystemestimated deformations are discrete epoch to epoch values
and to generate a deformation time series a successivesummation of the deformation values should be done first as
shown in (2).

2.2. Problem Formulation
After phase unwrapping the unknown deformations d = {d1 :dn} can be estimated by minimizing the squared error
function (E) (1). We had to take the topography error into
consideration (3) because we are using interferograms withlarge normal baselines.
∑∑

(()

(()

()

((1)

)⁄()

(4)

[(2)))(3)[thWhere,is the i InSAR LOS deformation,is theunknown deformation components for the ith interferogram,is the topography error,
is the unknown
deformation of time segment j,
is the normal baseline of
the ith Interferogram,is the DEM error, is the sensor
target distance and is the incident angle.
The minimum value of the squared error functionisreached when the first derivatives with respect to eachcomponent of the unknown surface deformations (di) arezero. This gives rise to the linear equation (4). Where A isdesign matrix with dimensions M × (n+1), d is the unknown
deformation vector with dimensions (n+1) × 1 and L is theobserved LOS deformation vector with dimensions M × 1.

()⁄()()⁄()](5)]

2.3. Supervised Spatial Filtering
ElGharbawi and Tamura [2] have presented a methodology
for spatial phase filtering using GPS derived products. Thismethodology can be briefly described as follows. First,
calculate the LOS deformation using the GPS stations’coordinates, radar incident and azimuth angles for eachpixel. Second, identify the phase values corresponding toGPS stations in the deformation map and subtract the GPS
derived LOS deformations from it. The residuals are thespatially correlated component of the imposed errors indeformation measurements at GPS stations positions inaddition to the ambiguity value of the unwrapped phase.
These residuals were used to fit a two dimensional surfaceof the first degree in range and azimuth directions. Finally991

this surface is subtracted from the deformation map. Thismethod should be applied to the deformation maps
estimated using the least squares method described in theprevious section.
3. APPLICATION
The proposed method was tested using 5 ALOS-PALSAR
images for Tokyo bay, Japan which was struck by an M9.0
megathrust earthquake on March 11, 2011. Tokyo hassuffered from large deformations that affected the entire
area. All the images are in ascending orbit and Fine Beam
Single polarization (FBS) mode, additional details are inTable 1.
By using 5 SAR images the example illustrated in Figure
1 and the linear equations system in (4) and (5) are valid in
our study; therefore interferograms were generated andstructured as illustrated in Figure 1. The effect oftopography was removed using SRTM-3 DEM. Goldsteinmethod was used for filtering and Minimum Cost Flow(MCF) method was used for phase unwrapping withcoherence threshold equals 0.2. To reduce the effect ofphase decorrelation we used multilooking of 3 looks inrange and 8 looks in azimuth. The unwrapped phase maps
for all the interferograms were calibrated with respect to areference GPS station (Figure 1).
Using least squares method to solve the system in (4) weestimated the unknown deformation values for the entire
scene. For spatial filtering, 10 GPS stations were identified
with in the study area with coherence value higher than 0.2
for the study time frame. We used 6 stations for spatialfiltering as described in section 2.3 and reserved 4 GPSstations for verification. Figure 2 shows deformation map(d4) which represents the effect of the 2011 Tohoku
Earthquake on Tokyo bay area, in addition to the locationsof GPS stations.
Master
3-4-2010

19-8-2010

4-1-2011
19-2-2011

Slave
19-8-2010
4-1-2011
19-2-2011
6-4-2011
4-1-2011
19-2-2011
6-4-2011
19-2-2011
6-4-2011
6-4-2011

B┴ (m.)
593
1285
2328
2690
761
1849
2185
1850
2082
396

Fig. 2. Deformation map (d4) of Tokyo bay area (19 Feb
2011 : 6 Apr 2011), with GPS stations locations.

Fig. 3. Observed and estimated deformation time series ofGPS stations.

∆ t (days)
138
276
322368
138
184
230
46
92
46

Table 1. Details of SAR images and interferograms

Fig. 4. LOS deformations obtained by GPS and InSAR
before and after applying the supervised spatial filtering,
positive values means motion towards the satellite.

992

GPSStation
ID
12
3
4
Mean

Error Standard Deviation(± m.)
Without
WithGPS based
GPS based
FilterFilter
0.01910.01330.06090.00560.01760.00710.07920.00560.04420.0079[2] D. Schmidt, and R. Bürgmann, Time-dependent land uplift andsubsidence in the Santa Clara valley, California, from a large

Improvement%

interferometric synthetic aperture radar data set. Journal ofGeophysical Research: Solid Earth, 108, ETG 4. 2003.

30.5
90.7
59.4
92.9
68.4

[3] T. ElGharbawi, and M. Tamura, Measuring deformations using
SAR interferometry and GPS observables with geodetic accuracy:
Application to Tokyo, Japan. ISPRS Journal of Photogrammetry
and Remote Sensing, VOL. 88, pp. 156-165. 2014.

Table 2. Statistical analysis for GPS stations time series.
After estimating the deformation maps for Tokyo bay
area we extracted the deformation values for the reserved
GPS stations and estimated the deformation time series (2),then compared the estimated deformations time series with
the LOS deformations obtained by GPS (Figure 3). Also, we
compared the estimated discrete deformations with the LOS
deformations obtained by GPS before and after applying thesupervised spatial phase filtering (Figure 4). The errors in
the estimated deformation values were in sub centimeterlevel showing a mean standard deviation of 7.9 millimeters
and the use of supervised spatial phase filtering has reduced
the errors inherited in deformation time series showing amean improvement of 68.4 % (Table 2).
4. CONCLUSION
In this paper we present a methodology to estimate surface
deformation time series using small number ofinterferograms. This method uses the unwrapped phasemaps and least squares analysis to estimate a series ofdeformation maps for the study area. Then by using theavailability of continuous GPS network observations asupervised spatial phase filtering was conducted to produce
the final deformation map series. This method was tested
using 10 interferograms of Tokyo bay area, Japan which
was affected by 2011 Tohoku Earthquake. The final
deformation time series were compared against GPS stations
observations and the mean standard deviation of theresiduals after using the GPS based supervised spatial phasefiltering was 7.9 millimeters showing a mean improvementof the deformation time series accuracy equals 68.4%, theseresults demonstrate the accuracy and usability of theproposed methodology.
5. REFERENCES[1] P. Berardino, G. Fornaro, R. Lanari, and E. Sansosti, A New
Algorithm for Surface Deformation Monitoring Based on Small
Baseline

DifferentialTRANSACTIONSONSARInterferograms.

GEOSCIENCE

AND

IEEE
REMOTESENSING, VOL. 40, No. 11, pp. 2375-2383. 2002.

993