digital elevation model (dem) generation using the sar interferometry technique
TRANSCRIPT
ORIGINAL PAPER
Digital elevation model (DEM) generation using the SARinterferometry technique
Abdurrahman Geymen
Received: 3 October 2012 /Accepted: 11 December 2012# Saudi Society for Geosciences 2012
Abstract The development of satellite technology is rapid-ly increasing the evolution of remote sensing. Satelliteimages give extensive useful information about the landstructure that is easily manageable in the process of gener-ating true, high-speed information which allows the fore-casting of future environmental and urban planning. Remotesensing comprises active and passive systems. Passive sen-sors detect natural radiation that is emitted or reflected bythe object or surrounding area being observed. Active sys-tems which produce their own electromagnetic energy andtheir main properties are their ability of collecting data innearly all atmospheric conditions, day or night. These sys-tems are frequently used to generate a digital elevationmodel (DEM) because they cover large areas. DEM suppliesessential data for applications that are concerned with theEarth’s surface and DEMs derived from survey data areaccurate but very expensive and time consuming to create.However, the use of satellite remote sensing to provideimages to generate a DEM is considered to be an efficientmethod of obtaining data. Interferometric SyntheticAperture Radar (InSAR) is a new geodetic technique fordetermining earth topography. InSAR measurements arehighly dense and they only give information in Line ofSight of Radar. In the study, interferograms were producedfrom the InSAR images taken by ERS satellites in 1992 and2007 and we developed the methods to generate a DEMusing the InSAR technique and present the results relating toKayseri Province in Turkey. The accuracy of the DEMderived from the InSAR technique is evaluated in compar-ison with a reference DEM generated from contours in atopographical map.
Keywords SAR interferometry . Digital elevation model .
Satellite remote sensing . DORIS
Introduction
Economic, political, and sociological reasons and many otherfactors as well as the fast growth of world population havecreated the necessity to search for resources that provide thecontinuation of human existence (Abdikan 2006). This searchfor resources is becoming faster in parallel to rapid technolog-ical progress. However, the data that is produced can onlycontribute to a country’s economy if it is consistent andcorrect, and that the data can be shared and updated (Köse2000). Technological progress has expedited the developmentof remote sensing method, which allows the collection ofinformation about the objects related to the earth’s surface.Thus, economic data can be produced by obtaining images oflarger areas in shorter time periods (Tolluoglu 2006).
The classic definition of geodesy is the surveying andmapping of the surface of the earth and also of the gravita-tional field (Huurneman 1999; Ge et al. 2004; Sefercik 2007;Şengün 2009; Erdogan 2009). Currently, the topography ofthe earth is determined using GPS-based systems, electromag-netic distance meters, or sensitive levelment systems. Mostgeodesic methods require repetitive surface surveys to deter-mine the topography of the earth’s surface (Wright 2000).However, most of these are point-based measuring systemswhich are too costly for the surveying of large areas. Dataproduced by the remote sensing method are obtained throughpassive or active sensors (Ge et al. 2004; Sefercik 2007).Passive sensors detect natural radiation that is emitted orreflected by the object or surrounding area being observedand they can now produce DEM up to 10 m level accuracy.Although, height accuracy in the range of 5 to 10 m may besufficient for many engineering projects, there are activities
A. Geymen (*)Faculty of Engineering, Department of Geomatics,Erciyes University, Kayseri, Turkeye-mail: [email protected]
Arab J GeosciDOI 10.1007/s12517-012-0811-3
that require height information at centimeter to millimeterlevel accuracy (Arora et al. 2006).
Active sensor systems include RADAR applications,which, have been developed recently and used in manyareas. Synthetic Aperture Radar Interferometry (InSAR) isa new geodesic method that records earth surface topogra-phy and deformations, and does not require surface surveys.The increase of the number of satellites has enlarged theareas in which InSAR can be applied (Kimura andYamaguchi 2000). InSAR methods are frequently used infields such as the assessment of the impact of earthquakes,volcanoes, glaciers, landslides, wildfires, movement of earthplates, oil research, observation of geological structures, andassessment of land use (Massonnet and Feigl 1998; Çakır Z2003; Ge et al. 2004; Bechor and Zebker 2006).
Earth surface deformations due to earthquakes, land-slides, and surface collapses due to drilling for oil or watercan be determined by comparing the phase information ofthe SAR images consisting of complex numbers before andafter deformations. This method used is called syntheticaperture radar interferometry. While the other geodesicmethods provide irregularly scattered and discontinuoussurveying, InSAR provides pixel-based deformation datafrom an area of thousands of kilometer squares covered bythe SAR images (Zhou et al. 2003). Radar interferometryusing satellites allows high-resolution images as well ascovering large areas globally. Unlike optical sensors, thedata can also be collected at night, and surveys carried outthrough cloudy areas. In the last 15 years, the InSAR meth-od has progressed from theoretical studies into a scientifi-cally accepted tool with wide application areas (Smith2002). In deformation surveys, GPS station surveys havehigh time resolution, allowing many surveys to be takenover a short period of time with accuracy at millimeter levelin the three dimensions of topography surveys, whereasInSAR is a survey system that requires stationary facilitiesand has low area resolution. According to Colesanti andWasowski (2006) the main advantages of InSAR are:
& Capability of providing spatially “continuous” data (withthe exception of low coherence areas) directly in a digitalformat and suitable for ground surface feature extraction.
& Possessing the optimal characteristics to cover wideareas (thousands of square kilometers) at low cost whencompared to ground-based conventional topographic orGPS surveying and photogrammetric applications.
The initial development related to SAR satellites began withobservation of other planets. Graham (1974) was the firstresearcher to use data from SAR sensors mounted on airplaneplatform. Rumsey et al. (1974) studied the impact of baselength on the topography produced by interferometry data.These studies were followed by the work of Zebker andGoldstein (1986). The first SAR satellite put into orbit in
1978 was the SEASAT satellite made by NASA. However, itonly supplied images for 105 days. Then Magellan waslaunched in 1990 to observe other planets. The EuropeanSpace Agency (ESA) operated the two European RemoteSensing (ERS) satellites in the ERS-1/2 Tandem mission, ac-quiring interferometric SAR data with a 1-day interval. Theshort interferometric interval is of interest since it reducestemporal de-correlation of the signal. High coherence is re-quired for the reliable interpretation of the interferometric phaseas carried out in the derivation of interferometric DEMs. Amajor limitation to ERS–ENVISAT interferometry is the oftennon-overlapping Doppler spectra of the two sensors. The SARinstrument onboard ERS-2 continues to work well; there is alsoample hydrazine for continued operation and preventativemeasures have been implemented to compensate for the ageingof the power system. After 2000 the attitude control degradeddue to the failure of several gyroscopes. Since January 2001ERS-2 has been operating in the zero-gyro mode. This causedvery significant yaw variations with Doppler Centroid devia-tions moving Arctic glaciers (Wegmüller et al. 2009).
This study shows the generation of the DEM using theSAR Interferometric technique and presents the result ofexperiment in Kayseri Province. The accuracy of DEMderived from InSAR technique is evaluated in comparisonwith the DEM (reference) generated from contours on atopographical map. DORIS software working under Linuxoperation system was used to provide information related tothe results obtained from the application.
Methodology
The first criterion to be considered in the selection of InSARimages to be used in interferometric image matching is tochoose satellite systems that have the same type of sensorparameters. In the interferometry studies related to deforma-tions in Kayseri and immediate surroundings, the images ofERS-1 and ERS-2 systems from ESA radar satellites wereused.
Therefore, InSAR images covering a 15-year periodwhich started from 1992 when the first photos of the workarea were captured, and ending in 2007. The images wereselected from the archive of ERS-1 (23.08.1992–27.09.1992) and ERS-2 (03.10.2007–07.11.2007) satellitesystems earth surface imaging program. Four satelliteimages of ascending movement covering Kayseri and theimmediate surroundings were selected, which were recordedduring the orbital movements from south to north(ascending) with an approximate orientation to the east.
ERS satellites record images with an in-line data arrange-ment shooting from close to distant range always oriented tothe right during their orbital movements. Therefore, duringsouth to north movement, these satellites record images
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from close range west to remote range east, and during northto south movement, they record images from close range eastto remote range west. Part of the data used in the applicationconsists of two pairs of InSAR images. The other data was thehigh-accuracy DEM from the test area sampled in a 10-m gridusing position and altitude data obtained during the photo-grammetric flight included in the project related to productionof photogrammetric digital maps realized by the KayseriMetropolitan Municipality in 2007. The methodologyemployed in this work is shown in Fig. 1.
The first stage in the interferometric method is processingof the complex images with the InSAR image processingsoftware by rectification of the second image to the firstimage; pixel by pixel. After rectification, an interferogramformed by the phase difference is obtained. After the com-patibility image is obtained the correlation of the imagesare correlated. When the flatness effect of the interfero-gram is corrected, the phase unwrapping process is appliedin order to correct the irregularity observed in the phases.In the last stage, the earth surface DEM including altitudeinformation produced with the phase difference informa-tion is obtained.
Preparation of SAR images for interferometric matching
The information relating to radar image pairs from 1992 to2007 used in the study was contained in the files given below:
DAT_01.001: A data file in which the image is saved.LEA_01.001: Information related to the image and thesatellite is saved in this file.NUL_DAT.001: Required to define the file structure.VDF_DAT.001: The file that informs the folder content.
In order to evaluate the image pairs of years for theperiod from 1992 to 2007, three script input files: crop.drs,interferogram.drs, and unwrapping.drs were produced usingDORIS software.
The crop.drs input file reads the image processed asSingle-Look Complex (SLC) and saves it in the DORISformat. Interferogram.drs produces the interferograms ofthe image pairs. Unwrapping.drs script consists of a seriesof commands that realize the phase opening process usingthe SNAPHU algorithm. Table 1 shows command seriesincluded in the crop.drs script written in DORIS software.
The output image of the ERS-1 satellite covering Kayseriand immediate surroundings (Fig. 2b) is obtained by pro-cessing the raw SAR image file (Fig. 2a) in raw format madeready for synthesizing using the commands given in Table 1in DORIS software. The raw SAR images are visuallymeaningless; however, they acquire a visual meaning thatcan be compared to the earth references through the pro-cesses applied during synthesizing. In order to obtain moreaccurate results during these processes the precise orbits ofDelft have been used instead of the orbit data in the file.
Interferometric matching studies
To produce interferometric DEM, three basic data consistingof a minimum of two InSAR images of the study arearecorded at different times and one reference DEM dataare required. The level of accuracy of the interferogramresults to be produced with DORIS software is closelyrelated to use and quality of the DEM data in the study.
Complex Master Image
Complex Slave Image
Co-registrationInitial offset estimationPrecision offset polynomial estimation
Interferogram generationCalculation of normalized interferogramOptional baseline estimation from fringe rate
Flat Earth Phase removalCoherence estimation
Phase UnwrappingAdaptive filtering of interferogramIdentification of phase unwrapping problem zonesConnection of isolated unwrapped areasSnaphu Algorithm
DEMRefined baseline modeling using height control pointsComputation of heights and true ground rangesGIS layers
GeocodingDerivation of transformation using interferometric heightsResamling of heights to orthonormal coordinates
Fig. 1 DEM production steps using InSAR method
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Table 2 shows the series of commands included in inter-ferogram.drs script written in DORIS software. The inter-ferograms of the images are produced in DORIS software byreading the interferogram.drs input file.
One of the most important commands included in interfer-ogram.drs input file is the Coarseorb command which tries toestimate the shift between image pairs coarsely by checkingtheir definitions. The Coarsecorr command processes thisinformation to examine the success of this coincidence by
checking approximately 50 points distributed evenly overthe entire image, then makes corrections such as 3 pixels tothe right and 6 pixels down. The Corsecorr options are thecommand series starting with CC_ in the input file. Thiscommand has three alternatives: Method, Nwin, andWinsize, which provide the floating of the slave image overthe master image. Since it would take too long to float theentire image, regular samples are taken from different placesover the whole image. If desired, the places of these windows
Table 1 Command series included in crop.drs script written in DORIS software
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may be changed in certain circumstances such images contain-ing sea. The regular sampling method is used in the studybecause DORIS software automatically ignores unsuccessfulmatches. The Fine command applies upsampling on approxi-mately 4,000 points scattered over the entire image first, andthen rechecks the coincidences. In this way, former shift infor-mation may be adjusted more accurately than 1 pixel. Thedifference between images must be less than 0.1 pixels forinterferometry. The placement of detected shifts on a surfaceis realized with the coregpm command. As the shift is not evenall over the image, a polynomial plane is defined and the slaveimage is resampled according to this polynomial. The secondcommand used for superimposition more accurate than 1 pixelis the Oversampling Factor (OSFactor) command. As it toler-ates maximum 0.1 pixel error, it requires oversampling of morethan ten times.
The ACC option refers to the accuracy of the system. Inthis option, the sought windows are found in a matrix of128×128 that is magnified 32×32 times, which makes theprocess more difficult. In order to avoid this, only the maintarget is sought in 8×8. In stages starting with DAC_, theoptions required for the Dem-assisted correlation processesare defined. In this stage how much DEM should be shiftedtowards the master image is calculated.
The CPM parameters consist of the Threshold, Degree,Maxiter, and plot commands. Threshold is used to eliminateunsuccessful results. Degree shows the degree of the poly-nomial defining the surface which is generally defined as asecond-degree polynomial. Maxiter defines how many iter-ations are required to find the outliers and 4,000 is the
number of iterations required to find all the outliers. Plot isused to display the results.
The stages starting with RS form the resample process.Important stages to be used after resample are: interfero, com-prefpha, subtrrefpha, and coherence stages. Interfero is the stage,in which interferograms are formed subtracting slave phase frommaster phase. Comprefpha is used to calculate reference phase.When images are being formed, it is assumed that the earth isflat. This causes phases to be formed as if earth was flat. Due tothis reason, erroneous phases must be subtracted. Subtraction ofthese calculated phases are realized in subtrrefpha stage.
Stages starting with RF_ represent range Filter parameters,stages starting with INT represent interferometry stages,stages starting with FE_ represent Compute Reference Phaseprocess stages, stages starting with Out_CINT represent theoutput file of output_Complex Interferogram, and stages start-ing with CRD_ and SRD_ represent compute reference DEMand subtract reference DEM stages.
It is possible to obtain square pixels adjusting parametervariables in theMULTILOOK command as 5 and 1. There arealso two methods for the command series starting with COH.These are include_refdem or refphase_only alternatives.
Radar is a system that operates by collecting the electro-magnetic waves floating in space after they reach the earth andare reflected back. While floating in space, the phase of thewaves regularly changes continuously between 0 and 2π.Thus, the distance between two points side by side is revealedin different phases. By reading the interferogram.drs input filein DORIS software, the amplitudes and interferograms of theimages in Figs. 3 and 4 are produced. It is possible to detectthe mountainous regions in the working area examining theamplitude image of the interferogram.
The differences between Figs. 4 and 5 obtained by process-ing interferometri.drs input file must be examined. It is ob-served that the fringes in Fig. 4 are closer to each other suchthat at some places, the fringes cannot be differentiated. InFig. 5 the fringes are more distant. This is because the phasedifference occurring due to the curvature of the earth is sub-tracted in Fig. 5. Another important point is that in Fig. 5,indistinct fringes in some areas of the right side of the imagecan be seen. Similar to the lake shown in Fig. 5, missing signsoccurring as phase differences are not regular. The interfero-grams observed in Fig. 5 occur due to these phase differences.In one sense, the frequency of these 0–2π changes shows theslope of the mountainous areas as contour lines.
Now, it is going to be tried to obtain a smooth surfacefrom the map obtained from the interferograms using filter-ing and unwrap stages.
Unwrapping and formation of digital elevation model
The algorithm to correct the indefiniteness in the phase isundertaken by decreasing the discontinuity length in the
Fig. 2 a Raw SAR image window without definitive information, bSLC image file
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Table 2 Interferogram.drs script commands written in DORIS software
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sinusoidal structure of the phase in the phase image.Discontinuity of phases are observed as pink lines in the“unwrapping” image and red and blue colors show positiveresidual phases, and the red color shows the negative residualphases.
In order to carry out unwrapping in DORIS software, theunwrap.drs input file must be processed (Table 3). The inputfile and related properties are shown in Table 3.
As mentioned above, the DEM model is an importantmethod of visualizing the surface structure of landscapes,but the DEM is also required to remove the topographic effectduring the differential processing. DORIS uses the SNAPHUsoftware for the unwrapping calculations (SNAPHU 2005).
This software makes calculations using a network-flow algo-rithm. Two-dimensional phase unwrapping is the process ofrecovering unambiguous phase data from a 2D array of phasevalues known only modulo 2π radian. SNAPHU is an imple-mentation of the statistical cost, network flow algorithm forphase unwrapping proposed by Chen and Zebker (2001). Thisalgorithm proposes phase unwrapping as a maximum a pos-teriori probability estimation problem, the objective of whichis to compute the most likely unwrapped solution given theobservable input data. Since the statistics relating the inputdata to the solution depend on the measured quantity,SNAPHU incorporates three built-in statistical models, forthe topography, deformation, and smooth generic data. Theproposed optimization problem is solved approximately usingnetwork flow techniques. SNAPHU always produces com-plete unwrapped solutions, and, in our tests, its accuracy iscomparable to, or better than that of other available algorithms(Chen and Zebker 2002). As SNAPHU uses an iterativeoptimization procedure, its execution time depends on thedifficulty of the interferogram. In single-tile mode the requiredmemory is in the order of 100 MB per 1,000,000 pixels in theinput interferogram. The software is written in C and shouldrun on most Unix/Linux platforms (Chen and Zebker 2001).
The SNAPHU algorithm proposed by Magnard et al.(2007) was used in this study. Large elevation differencesas those occurring in mountainous areas can lead to anambiguous phase even in the smallest baseline interfero-gram. Thus, this interferogram needs to be unwrapped withSNAPHU before using it as reference with the multi-baseline algorithm. The DEM obtained using theSNAPHU algorithm is shown in Fig. 6. A better DEMwas derived from another data set with a 2-month timeseparation.
Fig. 3 Amplitude image of the interferogram
Fig. 4 Produced interferograms
Fig. 5 Improved interferograms
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Comparing of DEM
In order for the DEM data obtained by the evaluation ofinterferometric radar images in DORIS software to be com-pared to the other DEM data, the following procedures mustbe applied:
& Geocode processing& Obtaining real altitude figures using phase values
The DEM data obtained using DORIS software is a localcoordinate system (Kampes 1999). For this to be comparedto the other DEM data, the same coordinate system must beapplied. This is called geocode process. With DORIS thesoftware, latitude, longitude, and altitude information of anypoint on the surface can be determined. Making use ofcommon points, local coordinates are converted into geo-graphical coordinate values (Fig. 7).
Another correction in the DEM data obtained by evalu-ating interferometric radar image pairs in DORIS software isthe calculation of real altitude values using phase values. Inorder to calculate the Z values which is the third dimension,DEM data with a known z value or obtained with accuratesurveys is required. Correlation 1 shows the differencebetween altitude and topographical phase. Using this corre-lation, the z values of the land surface are calculated. As canbe observed in the correlation, topographical phase andaltitude change they are in direct proportion to each other.
Φtopo ¼ Φorb þ 4pB?lr sin8
zþ 4plrdisp þ Φpath þ Φnoise ð1Þ
with an orbital phase term Φorb; the wavelength, 1 and thebaseline component perpendicular to the look vector, B⊥; theincidence angle, 8; the slant range, r; the slant range differ-ence, rdisp; the path delay term Φpath; and a phase noise termΦnoise.
Changes in atmospheric conditions cause a variable pathdelay. The spatial heterogeneity of Φpath, results in the
Table 3 Unwrap.drs script commands written in DORIS software
Fig. 6 Produced digital elevation model
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atmospheric distortions, one of the main error sources inrepeat-pass SAR interferometry. In the case of non-turbulent atmospheric conditions, the spatial variation ofthe path delay should not be very strong. The related phaseterm observed in the interferogram corresponds to thedifference between the path delays of the two individualacquisitions. Under non-turbulent conditions the variationin this path delay over 28 min is typically quite low andspatially rather smooth. In this case, the typical heighterrors are expected to be below 1 m (Wegmüller et al.2009).
The DEM map obtained using Doris software isshown in Fig. 6. The reference model used in this studyis the high accuracy DEM belonging to the test areasampled in a 10-m grid using position and altitude dataobtained during the photogrammetric flight included inthe project related to production of photogrammetric dig-ital maps realized by the Municipality of Greater City ofKayseri in 2007. The reference DEM which is the DEMmap obtained by the photogrammetric surveys is shownin Fig. 8.
In order to compare the two DEM, the maps must beconverted into the same coordinate system and pixel sizes,because it is not possible to make an analysis without over-lapping. If this is considered in points, it can be explained asthe values of any point in the DEM, coordinate values ofwhich are to be checked, must be the same in the secondDEM. The accuracy of the elevation of this point can onlybe checked when it is planimetrically in the same place inboth DEMs. If the point is in different places in the twoDEMs, there is a shift and no accuracy check can be made.ArcGIS 9.3 software was used to compare DEM maps.Using the “Raster Calculator” function in the ArcGISSpatial Analyses module, the difference between the twoDEM data was obtained.
The DEM to be used as the reference in the analyses isintroduced to the program and then, it is given to the shiftedDEM program for comparison, the accuracy of which isrequired to be checked. Furthermore, definitive informationis given to the program such as which elevation differencevalues would be selected as the threshold value, the tangentof the maximum slope value. These were given with the
Fig. 7 Geocode process of the obtained DEM
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purpose of eliminating the differences that exceed thethreshold value and slope values, and to minimize the effectof coarse errors. The average elevation difference betweenreference DEM and InSAR DEM were 1.3 m (Fig. 9).
Results and conclusions
In this study, accuracy checks were made on DEMs producedby InSAR images in the microwave region of the electromag-netic spectrum that has the longest wave length (1mm–1m) inthe active system of remote sensing method. For this, theDEMs of photogrammetric surveys and the DEM obtainedfrom the interferometric SAR images of the same region werecompared and the accuracy of data produced was examined.
The results obtained from this study support the resultsfound in the literature. The accuracy of DEMs derived fromInSAR can vary considerably, depending on local surfacecharacteristics. The InSAR technique shows that betterresults can be obtained from plain areas compared withforested areas. Figure 9 shows these average elevation dif-ference between reference DEM and the InSAR DEM. Theaverage elevation difference was found to be 1.3 m. For theplain and open areas, the absolute mean different elevationbetween the reference DEM and the InSAR DEM is about
90 cm. However, for the mountain and densely forested areas,the absolute mean different elevation is between 1.5 and 2 m.There are several ways in which InSAR is affected by thepresence and characteristics of forested areas. The most im-portant of these, at least for repeat-pass interferometry usingrelatively short wavelengths, is temporal decorrelation.
Based on the results of this study, it may be concluded thatDEMs produced by the radar interferometry method have notyet reached the accuracy level appropriate for cartographicalpurposes, but these DEMs are very useful for tasks that do notrequire a high level of accuracy such as the production ofelevation models of very large areas in these cases economiesof time and money can be achieved. InSAR has the optimalcharacteristics to cover wide-areas at low cost when comparedto ground-based conventional topographic or GPS surveyingand photogrammetric applications. In conclusion, InSAR con-tinues to attract attention for use in deformation assessmentand digital land modeling because of its advantages comparedto other methods. InSAR is a method that is continuallyimproving. High-resolution digital land models and deforma-tion maps can be created using this method. Using azimuthshift data together with this method allows the creation ofthree-dimensional deformation maps. Research related to in-terferometry is focused on making this method stronger andmore reliable.
Pixels
Data Value
ForestedArea
PlainArea
Fig. 9 Height profile through aDEM generated by InSAR anda corresponding DEM of thesurvey administration
N
1015 1300 1800
Fig. 8 Digital elevation modelproduced with photogrammetrymethod
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Acknowledgment We are grateful for the financial support for allstages of this study from the Erciyes University Scientific ResearchProjects Coordination Unit under project numbers FBA-12-3749 andFBA-07-46. We would also like to thank Dr. Batuhan Osmanoglu forhis assistance in the data evaluation.
References
Abdikan S (2006) Interferometric SAR images produced of the digitalelevation model of the stereo and quality investigation.M.Sc.Thesis, Yıldız Technical University, Istanbul
Arora MK, Patel V, Sharma ML (2006) SAR Interferometry for DEMgeneration, GIS Development, http://www.gisdevelopment.net/technology/rs/techrs0021.htm
Bechor BD, Zebker H (2006) Measuring two-dimensional movementsusing a single InSAR pair. Geophys Res Lett 33. doi:10.1029/2006GL026883
Çakır Z (2003) Analysis of the crustal deformation caused by the 1999Izmit and Düzce Earthquakes using Synthetic Aperture RadarInterferometry. Ph.D. thesis, Istanbul Technical University,Istanbul
Chen CW, Zebker HA (2001) Network approaches to two-dimensionalphase unwrapping: intractability and two new algorithms. J OptSoc Am 17:401–414
Chen CW, Zebker HA (2002) Phase unwrapping for large SAR inter-ferograms: statistical segmentation and generalized network mod-els. IEEE Trans Geosci Remote Sens 40:1709–1719
Colesanti C, Wasowski J (2006) Investigating landslides with space-borne Synthetic Aperture Radar (SAR) interferometry. Eng Geol88:173–199
Erdogan S (2009) A comparison of interpolation methods for produc-ing digital elevation models at the field scale. Earth Surf ProcessLandforms 34(3):366–376
Ge L, Chang HC, Rizos C, Trinder J (2004) Multipass differantial radarinterferometry with the aid of GIS. Int Arch Photogramm RemoteSens Spat Inf Sci 34:150–161
Graham LC (1974) Synthetic interferometer radar for topographicmapping. Proceeding of the IEEE 62(6):763–768
Huurneman G (1999) SAR interferometry. ITC Course notes,Enschede
Kampes B (1999) DORIS User’s manual and Technical documenta-tion, Delft University of Technology
Kimura H, Yamaguchi Y (2000) Detection of landslide areas usingsatellite radar interferometry. Photogramm Eng Remote Sens66:337–344
Köse O (2000) North Anatolian fault zone in the tectonic stressaccumulation points with remote sensing techniques to determine.Ph.D. thesis, Hacettepe University, Ankara
Magnard C, Meier E, Rüegg M, Brehm T, Essen H (2007) Highresolution millimeter wave SAR interferometry. In: IEEEInternational Geoscience and Remote Sensing Symposium,Barcelona, Spain, 23 July 2007
Massonnet D, Feigl KL (1998) Radar interferometry and its applicationto changes in the earth’s surface. Rev Geophys 36(4):400–441
Rumsey HC, Morris GA, Green RR, Goldstein RM (1974) A radarbrightness and altitude image of a portion of Venus. Icarus 23:1–7
Sefercik UG (2007) Radar interferometri tekniği ile SYM üretimi vedoğruluk değerlendirmeleri. TMMOB Harita ve KadastroMühendisleri Odası, 11.Türkiye Harita Bilimsel ve TeknikKurultayı, Ankara
Şengün YS (2009) GPS ve Insar ölçülerini birlikte kullanarak İzmitdepreminde oluşan deformasyonların belirlenmesi: Noktaseyrekleştirmede yeni bir algoritma. Doktora Tezi, İstanbulTeknik Üniversitesi, İstanbul
Smith L (2002) Emerging applications of Interferometric SyntheticAperture Radar (InSAR) in geomorphology and hydrology. AnnAssoc Am Geogr 92(3):385–398
Snaphu (2005) Statistical-Cost, Network-Flow Algorithm for phaseunwrapping (SNAPHU), Stanford University, http://www-star.stanford.edu/sar
Tolluoglu D (2006) Monitoring deformations on Tendürek Volcano bydifferential SAR interferometry. M.Sc.Thesis, Yüzüncü YilUniversity, Van
Wegmüller U, Santoro M, Werner C, Strozzi T, Wiesmann A, LengertW (2009) DEM generation using ERS–ENVISAT interferometry.J Appl Geophys 69:51–58
Wright T (2000) Crustal deformation in Turkey from SyntheticAperture Radar Interferometry. Ph.D.Thesis, University of Oxford
Zebker HA, Goldstein RM (1986) Topographic mapping from inter-ferometer SAR observations. J Geophys Res 91:4993–5000
Zhou Y, Stein A, Molenaar M (2003) Integrating interferometric SARdata with levelling measurements of land subsidence using geo-statistics. Int J Remote Sens 24(18):3547–3563
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