MODELING INTERFEROGRAM STACKS FOR SENTINEL - 1
|
|
- Virgil Underwood
- 6 years ago
- Views:
Transcription
1 MODELING INTERFEROGRAM STACKS FOR SENTINEL - 1 Fabio Rocca (1) (1) Politecnico di Milano, Via Ponzio 34/5, Milano, Italy, rocca@elet.polimi.it ABSTRACT The dispersion of the optimal estimate of a subsidence motion is determined, and compared with that of Permanent Scatterers, when the targets are progressively decorrelating due to Brownian motion. Approximately, with N consecutive images and revisit time T, the interferogram stack results are equivalent to PS's, if the pixel count in the window grows with N²T, independently of the frequency. In the case of Sentinel 1, it is possible to expect that, in lightly vegetated areas and in one year time, less than 100 pixels will create an interferogram stack as efficient as a PS 1. Interferogram stacks approximation holds. It is possible to substitute the variable describing motion in the line of sight with the variable describing the unwrapped phase because of the linear relation between the two. The decorrelation law is [8]: with τ=2/σ². For instance for a Brownian motion in the look direction with a standard deviation in a day of, with say the phase deviation would be: Hence: Many targets in a SAR image are not coherent over long temporal intervals, but they can be exploited nevertheless for motion estimation using "conventional" DInSAR techniques. Despite the widely developed literature on differential interferometry, starting from the very first InSAR references, there is a substantial lack of estimates of the decorrelation of distributed targets, after the paper by Zebker, and of optimal techniques to provide an estimate of the motion field [1, 2, 5]. Most approaches can be generally defined as "interferogram stacks", and new and appealing methodologies appear that could be classified in this category [4]. I establish a model for target decorrelation for interferogram stacks, and provide a statistically consistent estimator, to be used mainly for the assessment of the ground motion accuracy. Modeling the decorrelation Exponential model: Brownian motion I suppose that the time decorrelation mechanism is primarily due to the motion of the scatterers in the resolution cell [8]. I model this motion as a Brownian motion, or the sum of many successive independent and equally distributed motions so that the normal 1 This work was partly carried out within ESA contract 19142/05/NL/CB. corresponding, for a single scatterer, to a time-constant τ=40 [days] in C-band. If the resolution cell contains many scatterers so that the observed reflectivity is the sum of elemental contributions, then the coherence shows the same exponential decay with time, provided that each element is affected by the same independent Brownian motion. Exponential model: Markov This alternative model makes the assumption that the elemental scatterers in the resolution cell change suddenly their reflectivity. Supposing the change rate constant and the process without memory other than the state, then the differential variation of the unchanged population is proportional to the current unchanged population. A more complex model could consider different populations characterized by different time constants. In the case of only two populations with very different τ (e.g. with τ 0 very small compared to the acquisition time scale and the other time constant τ 1 ) then the coherence can be approximated with The term p 1 in the last equation can range from 1 to 0 and represents the fraction of the scatterers that didn't suffer from a "quick decorrelation" mechanism. The impact of decorrelation is the same as that of thermal noise, i.e. a multiplication times: Proc. Envisat Symposium 2007, Montreux, Switzerland April 2007 (ESA SP-636, July 2007)
2 Then I account for all the multiplicative terms with a single constant γ₀ and eventually the model writes simply: where T is the interval between the takes, and λ is the wave length. The interferogram is obtained by cross multiplying two images at times k 1 T, k 2 T, and then Validation with real data The results here discussed are based on scenes from an ERS-1 Ice-Phase data set (Track 22, Frame 2763) acquired over central Italy, from the end of December 1993 to April 1994 (26 scenes). During this acquisition phase, the revisit time interval was 3 days, while all the other orbital parameters remained basically unchanged. The images were focused and over sampled by a factor 2 (range only) and co-registered on the master's common grid (image taken March 5, 1994). Then a portion of the entire scene was selected (20 15km, range azimuth). It's near the Fiumicino (Rome) airport and shows the last part of course of the Tevere river. The data set interest stays with the reduced revisit time, 3 days. We can study the decorrelation dynamics in the time span of a few weeks, a task otherwise impossible with the usual 35 days data sets. Even though the maximum baseline span is about 800m we chose to work with a reduced set of 17 images in the range ±250m. This measure is an attempt to reduce the impact of geometric decorrelation. At the same time we applied a spectral shift filtering in the range band common to all images. Approximately half the original bandwidth was retained by this step, as one is limited by the worst case (500m). Decorrelation dynamics Estimates have been made by spatial averaging on windows of pixels (range over sampled 2:1) with no overlap, to make every measure independent. The histograms of the short term coherence and of the time constant τ, with and without spectral shift filtering are shown in Figures 1, 2. The peak in the histogram is at about days. As expected, the coherence is increased if using the common band filtered images, because a source of decorrelation is eliminated. Linear Estimates of Ground Motion The covariance matrix of the interferograms Here I will deal with the approximate estimation of the progressive interferometric phase, namely the subsidence velocity correspondent to an additional phase shift φ identical from one pass to the next. I have Figure 1: Histogram of the time constant, in days. Figure 2: Histogram of γ 0 averaging over L pixels. The removal of the scatterer phases is obtained by multiplying one image times the conjugate of the other. Consistently with the thinner orbital tubes of the forthcoming systems [6], I suppose that the baseline is kept to zero, and thus I will not consider geometrical decorrelation. This impacts on the covariance of the decorrelation noise, too, now only dependent on temporal decorrelation. As the subsidence induced phase shifts could create geometrical decorrelation, if changing with range, I have assumed to have uniform subsidence in all the pixels to be considered. The value of L will found to be consistent with the hypotheses that all pixels stay within the correlation radius of the APS, say within 800m, and therefore are subject not only to the same subsidence as said before, but also to the same atmospheric phase. The interferogram value is: where is the received signal considered as the sum of the temporally decorrelating signal γx plus noise n. The expected value of the interferogram is:
3 where the temporal component of the coherence, can be expressed on the basis of the exponential decay as and ρ is real, smaller than 1. For short, I will call span the temporal baseline of an interferogram i.e. the time interval between the two takes, while calling lag the delay between the time centers of two interferograms with the same or with different span. Further, is the variance of the complex value of the noiseless received signal. The actual coherence will be lower than that due to longer term decorrelation, due to the additive noise n (say, the instantaneous decorrelation term). Using the exponential Markov model, the temporal component of the coherence decreases exponentially with the span. Indicating with ρ the coherence at span T (apart the noise), and considering the phase shifts due both to the progressive subsidence and to the APS, and therefore adding to the subsidence phase shifts the phase shifts due to the APS in the two takes, I get: For small phase shifts I can linearize with respect to the APS and subsidence terms: It is correct to contend that even if the subsidence could be low, the APS won't. In effect, we expect, the ms value of the APS to be of the order of one radian square [3]. The dispersion of the two way additional travel path due to local random variations of the refractivity of the atmosphere is The interferograms covariance, for any given atmospheric and subsidence phase shift is, using the gaussianity of the data and the Gaussian moment factoring theorem: Optimal linear estimates It is possible to check that the entries of the correlation matrix of the imaginary parts of the interferograms, without the additional phase shifts due to APS or ground motion, are: The optimal estimate of φ is a linear combination of the interferograms weighted with the weights vector represented in fig. 3 and obtained with the usual prediction techniques, imposing no bias. Another derivation of these results has been obtained using an extension of the Cramér Rao bound [7]. Figure 3: Interferogram weights vector The weight vector is represented here as a function of the two indexes correspondent to the two takes; for this figure, the total number of takes is 30 (one year work for Sentinel 1) and thus the matrix is However, the matrix is zero on and below the main diagonal as the auto interferograms are irrelevant and not weighted (points on the main diagonal) and each interferogram is considered only once. In the case of no instantaneous decorrelation, and no APS, only the interferograms at span 1 (those on the first sub diagonal) would be used, as remarked in the discussion that will follow. In order to have a better understanding of the interplay of the variables, it is useful to study interferogram stacks, or to move to the frequency domain. Cross spectra of interferogram sequences We have shown that the complex covariance of two interferograms depends only on their mutual lag and on their span difference. Now, we Fourier transform the lag axis, and call the transform variable ψ. This is made in order to study the low frequencies (ψ ~ 0) correspondent to interferogram stacks, i.e. low pass data in the lag domain. The elements of the cross spectral matrix of the imaginary parts of the interferograms, at zero frequency, depend only on the difference and the sum of their spans (indicated as 2p, 2q, as the lags are transformed out) and are:
4 The cross spectra of the atmospheric perturbations at very low frequencies (zero for ψ=0) are: seen in the last section. To avoid (in)significant figures, then, if T is the repeat time in weeks: Hence, the cross spectral matrix of the APS contribution at low frequencies is a dyad increasing with ψ². Finally the signal vector has components: that in the Fourier domain become: i.e. the same dyad as the atmospheric contribution but the frequency behavior is delta like instead of ψ² like as for the atmosphere. The estimate of the interferometric phase φ in the presence of this colored noise can be carried by averaging over N samples of the interferograms, i.e. windowing the spectrum. I approximate this windowing with an ideal filtering in the band: If the decorrelation time is much shorter than the integration time NT, then in the band of the filter the cross spectrum of the decorrelation can be considered as a constant. Then, indicating with a bar the effect of filtering (stacking), the covariance matrix of the filtered (stacked) atmospheric components is: and using the matrix inversion lemma, I have: Discussion This result is reasonable in that the dispersion of the estimate of the subsidence rate decreases with N³ in the case of a PS. In the case of L distributed scatterers decorrelating in M revisits, we combine N interferograms, M spans, and L pixels, and thus the dispersion may well decrease with NML. Then, to make the two behaviors equivalent one needs L increasing with. This model, very crude and overestimating L PS as the atmospheric contribution is too small, still captures the interesting behavior of L PS versus frequency, as it will be seen later in the section on the extension of the model to different frequencies. In fact, as the product does not change with λ, frequency will minimally impact on L PS. Further, it is easy to check that with high SNR only one span is used for the estimate, while the others are redundant as the unique source of noise is decorrelation. This corresponds to say that the inverse of a Toeplitz exponential matrix is tri diagonal, or that with first order Markov processes, the memory to use for estimation is the shortest possible. With lower SNR, more spans are used. Anyway, with the expected Sentinel 1 spatial resolution of 5 20m, we expect well more than 50 independent APS measurements per km², not bad at all [6]. This would allow a good estimate of the APS, its reduction, and therefore the justification of the assumptions made, entering the PS regime and yielding a further reduction of the dispersion of the velocity estimate. Extension to Different Frequencies The optimal weights depend only on ρ, SNR. It is possible to notice from this last equation that the distributed scatterers act as a PS, i.e. the APS and not the decorrelation is the main cause for dispersion of the subsidence rate estimate, if the number of looks is greater than: One advantage of the model that has been considered is the possibility of its extension to different carrier wavelengths λ. The decorrelation at span 1 and the ms atmospheric phase shift become according to : Calling M the number of revisits during the decorrelation time constant, it results approximately: Then, the still rather complex formula previously shown can be approximated with the following very simple one, even frequency independent, as it will be As said, M is the decorrelation time constant measured in terms of revisit times. In figure 4, I show in ordinates the number of pixels yielding an estimate of the subsidence with a standard deviation of 4, 5, 6 mm/year for the center frequencies in abscissas, in the case of 30 acquisitions in one year. In figure 5, I show the standard deviation of the estimate of the subsidence
5 for various center frequencies, according to the model, again in the case of 30 acquisitions in one year, for a number of pixels ranging from 10 to 150. I see from this figures that say L=100 is close to be enough to ensure that, with 12 days repeat and 30 revisits, the average coherence is enough to enter the PS regime, making the atmospheric effect to prevail, while there is no appreciable change with carrier frequency. Indeed, higher carrier frequencies behave worse, but practically big changes are not to be expected until 8-10 GHz. On the very low frequency side, the increment of the dispersion is due to the limited SNR, and also as expected affects both PS as well as interferogram stacks. With longer observation times, there would be a shift of the optimum towards lower frequencies, while keeping the variation of the dispersion of the estimate rather small. [7] S. Tebaldini and A. Monti Guarnieri, Cramér Rao lower bound for parametric phase estimation in multi - pass radar interferometry, Personal Communication, Final report, ESA contract No /05/NL/CB, Analysis of ambiguity noise in the Sentinel-1 Interferometric Wideswath Mode. [8] H. A Zebker and J. Villasenor. Decorrelation in interferometric radar echoes. IEEE Transactions on Geoscience and Remote Sensing, 30(5): , September Conclusion An evaluation of the subsidence rate error budget has been carried out for DInSAR interferometry, using the 3 days revisit interval data over Rome taken by ERS - 1. The results of modeling temporal decorrelation with a Brownian motion depend upon the number N of images used and the revisit interval T. Assuming a short term target coherence of 0.6 and averaging measures over L=100 independent looks the dispersion of the velocity estimate is lower than 4-4.5mm/year for a 12 days revisit time. For a wide band of frequencies including C band, the number of looks needed to make distributed scatterers as accurate as a PS is in the order of 0.1N²T [weeks], approximately. Figure 4: L PS as a function of frequency for different values (4, 5, 6 mm/year) of the standard deviation of the subsidence estimate and 30 acquisitions in one year. The lower the deviation, the higher is L PS. References [1] R. Bamler and P. Hartl. Synthetic aperture radar interferometry. Inverse Problems, 14, R1 --R54, [2] Y. Fialko. Interseismic strain accumulation and the earthquake potential on the southern San Andreas fault system. Nature, 441: , June [3] R. Hanssen. Radar Interferometry: Data Interpretation and Error Analysis. Kluwer Academic Publishers, Dordrecht, [4] A. Hooper, H. Zebker, P. Segall, and B. Kampes, A new method for measuring deformation on volcanoes and other natural terrains using InSAR persistent scatterers, Geophys. Res. Letters, 31, L23611, doi: /2004gl021737, 2004 [5] P. Rosen, S. Hensley, I. R Joughin, Fuk K Li, Soren Madsen, Ernesto Rodríguez, and Richard Goldstein. Synthetic aperture radar interferometry. Proceedings of the IEEE, 88(3): , March [6] Mission Requirement Document, Sentinel _MRD_1-4_approved_version.pdf Figure 5: Standard deviation of the subsidence estimate for 30 acquisitions in one year, different center frequencies, and different values of L PS, the number of pixels in the window. The lower the deviation, the higher L PS. The behavior of a PS is also indicated. The sources of noise are thermal, atmosphere, and decorrelation (for non PS targets).
On the Exploitation of Target Statistics for SAR Interferometry Applications
On the Exploitation of Target Statistics for SAR Interferometry Applications A. Monti Guarnieri, S. Tebaldini Politecnico di Milano Abstract This paper focuses on multi-image Synthetic Aperture Radar Interferometry
More informationModeling of Atmospheric Effects on InSAR Measurements With the Method of Stochastic Simulation
Modeling of Atmospheric Effects on InSAR Measurements With the Method of Stochastic Simulation Z. W. LI, X. L. DING Department of Land Surveying and Geo-Informatics, Hong Kong Polytechnic University, Hung
More informationHigh-resolution temporal imaging of. Howard Zebker
High-resolution temporal imaging of crustal deformation using InSAR Howard Zebker Stanford University InSAR Prehistory SEASAT Topographic Fringes SEASAT Deformation ERS Earthquake Image Accurate imaging
More informationSAR interferometry Status and future directions. Rüdiger Gens
SAR interferometry Status and future directions Rüdiger Gens Polarimetric InSAR Polarimetric InSAR InSAR - Status and future directions sensitivity to changes in surface scattering, even in the presence
More informationJournal of Geodynamics
Journal of Geodynamics 49 (2010) 161 170 Contents lists available at ScienceDirect Journal of Geodynamics journal homepage: http://www.elsevier.com/locate/jog Recent advances on surface ground deformation
More informationNoise covariance model for time-series InSAR analysis. Piyush Agram, Mark Simons
Noise covariance model for time-series InSAR analysis Piyush Agram, Mark Simons Motivation Noise covariance in individual interferograms has been well studied. (Hanssen, 2001) Extend the noise model to
More informationDeformation measurement using SAR interferometry: quantitative aspects
Deformation measurement using SAR interferometry: quantitative aspects Michele Crosetto (1), Erlinda Biescas (1), Ismael Fernández (1), Ivan Torrobella (1), Bruno Crippa (2) (1) (2) Institute of Geomatics,
More informationDIFFERENTIAL INSAR STUDIES IN THE BOREAL FOREST ZONE IN FINLAND
DIFFERENTIAL INSAR STUDIES IN THE BOREAL FOREST ZONE IN FINLAND Kirsi Karila (1,2), Mika Karjalainen (1), Juha Hyyppä (1) (1) Finnish Geodetic Institute, P.O. Box 15, FIN-02431 Masala, Finland, Email:
More informationImplementation of Multi-Temporal InSAR to monitor pumping induced land subsidence in Pingtung Plain, Taiwan
Implementation of Multi-Temporal InSAR to monitor pumping induced land subsidence in Pingtung Plain, Taiwan Presenter: Oswald Advisor: Chuen-Fa Ni Date: March 09, 2017 Literature Review Pingtung Plain
More informationSAR Data Analysis: An Useful Tool for Urban Areas Applications
SAR Data Analysis: An Useful Tool for Urban Areas Applications M. Ferri, A. Fanelli, A. Siciliano, A. Vitale Dipartimento di Scienza e Ingegneria dello Spazio Luigi G. Napolitano Università degli Studi
More informationALOS PI Symposium 2009, 9-13 Nov 2009 Hawaii MOTION MONITORING FOR ETNA USING ALOS PALSAR TIME SERIES
ALOS PI Symposium 2009, 9-13 Nov 2009 Hawaii ALOS Data Nodes: ALOS RA-094 and RA-175 (JAXA) MOTION MONITORING FOR ETNA USING ALOS PALSAR TIME SERIES Urs Wegmüller, Charles Werner and Maurizio Santoro Gamma
More informationDEM GENERATION AND ANALYSIS ON RUGGED TERRAIN USING ENVISAT/ASAR ENVISAT/ASAR MULTI-ANGLE INSAR DATA
DEM GENERATION AND ANALYSIS ON RUGGED TERRAIN USING ENVISAT/ASAR ENVISAT/ASAR MULTI-ANGLE INSAR DATA Li xinwu Guo Huadong Li Zhen State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing
More informationERS Track 98 SAR Data and InSAR Pairs Used in the Analysis
ERS Track 98 SAR Data and InSAR Pairs Used in the Analysis Date 1 Date 2 Date 1 Date 2 Date 1 Date 2 Date 1 Date 2 7/17/1992 6/19/2000 7/17/1992 7/2/1993 9/10/1993 10/28/1996 9/3/1995 10/18/1999 9/25/1992
More informationThe Potential of High Resolution Satellite Interferometry for Monitoring Enhanced Oil Recovery
The Potential of High Resolution Satellite Interferometry for Monitoring Enhanced Oil Recovery Urs Wegmüller a Lutz Petrat b Karsten Zimmermann c Issa al Quseimi d 1 Introduction Over the last years land
More informationCHAPTER-7 INTERFEROMETRIC ANALYSIS OF SPACEBORNE ENVISAT-ASAR DATA FOR VEGETATION CLASSIFICATION
147 CHAPTER-7 INTERFEROMETRIC ANALYSIS OF SPACEBORNE ENVISAT-ASAR DATA FOR VEGETATION CLASSIFICATION 7.1 INTRODUCTION: Interferometric synthetic aperture radar (InSAR) is a rapidly evolving SAR remote
More informationGround surface deformation of L Aquila. earthquake revealed by InSAR time series
Ground surface deformation of L Aquila earthquake revealed by InSAR time series Reporter: Xiangang Meng Institution: First Crust Monitoring and Application Center, CEA Address: 7 Naihuo Road, Hedong District
More informationERS-ENVISAT CROSS-INTERFEROMETRY SIGNATURES OVER DESERTS. Urs Wegmüller, Maurizio Santoro and Christian Mätzler
ERS-ENVISAT CROSS-INTERFEROMETRY SIGNATURES OVER DESERTS Urs Wegmüller, Maurizio Santoro and Christian Mätzler Gamma Remote Sensing AG, Worbstrasse 225, CH-3073 Gümligen, Switzerland, http://www.gamma-rs.ch,
More informationERS-ENVISAT Cross-interferometry for Coastal DEM Construction
ERS-ENVISAT Cross-interferometry for Coastal DEM Construction Sang-Hoon Hong and Joong-Sun Won Department of Earth System Sciences, Yonsei University, 134 Shinchon-dong, Seodaemun-gu, 120-749, Seoul, Korea
More informationMethods and performances for multi-pass SAR Interferometry
Methods and performances for multi-pass SAR Interferometry Methods and performances for multi-pass SAR Interferometry Stefano Tebaldini and Andrea Monti Guarnieri Dipartimento di Elettronica e Informazione
More informationto: Interseismic strain accumulation and the earthquake potential on the southern San
Supplementary material to: Interseismic strain accumulation and the earthquake potential on the southern San Andreas fault system by Yuri Fialko Methods The San Bernardino-Coachella Valley segment of the
More informationGround deformation monitoring in Pearl River Delta region with Stacking D-InSAR technique
Ground deformation monitoring in Pearl River Delta region with Stacking DInSAR technique Zhao Qing*, LIN Hui, JIANG Liming Institute of Space and Earth Information Science, Room 615, Esther Lee Building,
More informationSurface Deformation Measurements Scientific Requirements & Challenges
Surface Deformation Measurements Scientific Requirements & Challenges 1st Science and Application Workshop for Germany-Japan Next-Generation SAR M. Eineder, C. Minet, A. Parizzi Tokyo, 27.6.2013 Tandem-L
More informationJ. Manuel Delgado (1,2), Roberto Cuccu (1), Giancarlo Rivolta (1)
MONITORING GROUND DEFORMATION USING PERSISTENT SCATTERS INTERFEROMETRY (PSI) AND SMALL BASELINES (SBAS) TECHNIQUES INTEGRATED IN THE ESA RSS SERVICE: THE CASE STUDY OF VALENCIA, ROME AND SOUTH SARDINIA
More informationAPPLICABILITY OF PSINSAR FOR BUILDING HAZARD IDENTIFICATION
APPLICABILITY OF PSINSAR FOR BUILDING HAZARD IDENTIFICATION. STUDY OF THE 29 JANUARY 2006 KATOWICE EXHIBITION HALL COLLAPSE AND THE 24 FEBRUARY 2006 MOSCOW BASMANNY MARKET COLLAPSE Zbigniew Perski (1),
More informationATMOSPHERIC ERROR, PHASE TREND AND DECORRELATION NOISE IN TERRASAR-X DIFFERENTIAL INTERFEROGRAMS
ATMOSPHERIC ERROR, PHASE TREND AND DECORRELATION NOISE IN TERRASAR-X DIFFERENTIAL INTERFEROGRAMS Steffen Knospe () () Institute of Geotechnical Engineering and Mine Surveying, Clausthal University of Technology,
More informationSAR Monitoring of Progressive and Seasonal Ground Deformation Using the Permanent Scatterers Technique
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 41, NO. 7, JULY 2003 1685 SAR Monitoring of Progressive and Seasonal Ground Deformation Using the Permanent Scatterers Technique Carlo Colesanti,
More informationVALIDATION OF THE PERMANENT SCATTERERS TECHNIQUE IN URBAN AREAS
VALIDATION OF THE PERMANENT SCATTERERS TECHNIQUE IN URBAN AREAS Alessandro Ferretti, Claudio Prati, Fabio Rocca, Carlo Colesanti Dipartimento di Elettonica e Informazione Politecnico di Milano Piazza L.
More informationPSI Precision, accuracy and validation aspects
PSI Precision, accuracy and validation aspects Urs Wegmüller Gamma Remote Sensing AG, Gümligen, Switzerland, wegmuller@gamma-rs.ch Contents - Precision - Accuracy - Systematic errors - Atmospheric effects
More information3-Dimension Deformation Mapping from InSAR & Multiaperture. Hyung-Sup Jung The Univ. of Seoul, Korea Zhong Lu U.S. Geological Survey, U.S.A.
3-Dimension Deformation Mapping from InSAR & Multiaperture InSAR Hyung-Sup Jung The Univ. of Seoul, Korea Zhong Lu U.S. Geological Survey, U.S.A. Outline Introduction to multiple-aperture InSAR (MAI) 3-D
More informationSubsidence-induced fault
Surveying Monitoring underground coal mining-induced subsidence by Yaobin Sheng, Linlin Ge, Chris Rizos, University of New South Wales, and Yunjia Wang, China University of Mining and Technology This paper
More informationAtmospheric Phase Screen (APS) estimation and modeling for radar interferometry
Atmospheric Phase Screen (APS) estimation and modeling for radar interferometry Ramon Hanssen¹, Alessandro Ferretti², Marco Bianchi², Rossen Grebenitcharsky¹, Frank Kleijer¹, Ayman Elawar¹ ¹ Delft University
More informationSlow Deformation of Mt. Baekdu Stratovolcano Observed by Satellite Radar Interferometry
Slow Deformation of Mt. Baekdu Stratovolcano Observed by Satellite Radar Interferometry Sang-Wan Kim and Joong-Sun Won Department of Earth System Sciences, Yonsei University 134 Shinchon-dong, Seodaemun-gu,
More informationMulti-Baseline SAR interferometry
ulti-baseline SAR interferometry A. onti Guarnieri, S. Tebaldini Dipartimento di Elettronica e Informazione - Politecnico di ilano G. Fornaro, A. Pauciullo IREA-CNR ESA cat-1 project, 3173. Page 1 Classical
More informationGPS and GIS Assisted Radar Interferometry
GPS and GIS Assisted Radar Interferometry Linlin Ge, Xiaojing Li, Chris Rizos, and Makoto Omura Abstract Error in radar satellite orbit determination is a common problem in radar interferometry (INSAR).
More informationDEMONSTRATION OF TERRASAR-X SCANSAR PERSISTENT SCATTERER INTERFEROMETRY
DEMONSTRATION OF TERRASAR-X SCANSAR PERSISTENT SCATTERER INTERFEROMETRY Fernando Rodriguez Gonzalez (1) Ramon Brcic (1) Nestor Yague-Martinez (1) Robert Shau (1) Alessandro Parizzi (1) Nico Adam (1) (1)
More informationMaximum Likelihood Multi-Baseline SAR Interferometry
Maximum Likelihood Multi-Baseline SAR Interferometry G. Fornaro(*), A. Monti Guarnieri(**), A. Pauciullo(*), F. Rocca(**) (*) Istituto per il Rilevamento Elettromagnetico dell Ambiente (IREA), Consiglio
More informationPERSISTENT SCATTERER INTERFEROMETRY: POTENTIAL AND LIMITS
PERSISTENT SCATTERER INTERFEROMETRY: POTENTIAL AND LIMITS M. Crosetto a, O. Monserrat a, A. Jungner, B. Crippa b a Institute of Geomatics, Av. del Canal Olímpic, s/n, Castelldefels, E-08860, Spain (michele.crosetto,
More informationMeasuring rock glacier surface deformation using SAR interferometry
Permafrost, Phillips, Springman & Arenson (eds) 2003 Swets & Zeitlinger, Lisse, ISBN 90 5809 582 7 Measuring rock glacier surface deformation using SAR interferometry L.W. Kenyi Institute for Digital Image
More informationMAPPING DEFORMATION OF MAN-MADE LINEAR FEATURES USING DINSAR TECHNIQUE
MAPPING DEFORMATION OF MAN-MADE LINEAR FEATURES USING DINSAR TECHNIQUE H. Wu a, *, Y. Zhang a, J. Zhang a, X. Chen b a Key Laboratory of Mapping from Space of State Bureau of Surveying and Mapping, Chinese
More informationIEEE GEOSCIENCE AND REMOTE SENSING LETTERS, VOL. 1, NO. 2, APRIL
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, VOL. 1, NO. 2, APRIL 2004 57 Delta-K Interferometric SAR Technique for Snow Water Equivalent (SWE) Retrieval Geir Engen, Tore Guneriussen, and Øyvind Overrein
More informationSentinel-1A SAR Interferometry Verification
Sentinel-1A SAR Interferometry Verification Dirk Geudtner1, Pau Prats2, Nestor Yaguee-Martinez2, Andrea Monti Guarnieri3, Itziar Barat1, Björn Rommen1 and Ramón Torres1 1ESA ESTEC 2DLR, Microwave and Radar
More informationMEASURING VOLCANIC DEFORMATION AT UNIMAK ISLAND FROM 2003 TO 2010 USING
MEASURING VOLCANIC DEFORMATION AT UNIMAK ISLAND FROM 2003 TO 2010 USING WEATHER MODEL-ASSISTED TIME SERIES INSAR Gong, W. a, Meyer, F. J. a, Lee, C. W. b, Lu, Z. c, Freymueller, J a. a. Geophysical Institute,
More informationIvana Zinno, Francesco Casu, Claudio De Luca, Riccardo Lanari, Michele Manunta. CNR IREA, Napoli, Italy
An Unsupervised Implementation of the P-SBAS DiNSAR Algorithm for Processing Large Data Volumes through Distributed Computing Infrastructures within Operational Environments Ivana Zinno, Francesco Casu,
More informationPROVENCE INTER-COMPARISON. Marta Agudo, Michele Crosetto Institute of Geomatics
PROVENCE INTER-COMPARISON Marta Agudo, Michele Crosetto Institute of Geomatics Final version Castelldefels, 16 March 2007 INDEX INDEX... 2 1. INTRODUCTION... 3 2. PRE-PROCESSING STEPS... 5 2.1 Input data...
More informationEvaluation of subsidence from DinSAR techniques using Envisat-ASAR data at Toluca Valley Basin, Mexico.
Evaluation of subsidence from DinSAR techniques using Envisat-ASAR data at Toluca Valley Basin, Mexico. Norma Angélica Dávila Hernández 1 Delfino Madrigal Uribe 1 Xanat Antonio Némiga 1 1 Autonomous University
More informationAPPEARANCE OF PERSISTENT SCATTERERS FOR DIFFERENT TERRASAR-X ACQUISITION MODES
APPEARANCE OF PERSISTENT SCATTERERS FOR DIFFERENT TERRASAR-X ACQUISITION MODES Stefan Gernhardt a, Nico Adam b, Stefan Hinz c, Richard Bamler a,b a Remote Sensing Technology, TU München, Germany b Remote
More informationCSK AO project workshop. AO CSK 3655 Continuous subsidence monitoring employing Cosmo-Skymed constellation.
CSK AO project workshop AO CSK 3655 Continuous subsidence monitoring employing Cosmo-Skymed constellation. Pablo Blanco Sánchez, Roman Arbiol, Fernando Pérez pablo.blanco@icc.cat Roma 8/5/11 Outline Project
More informationAdvanced interpretation of land subsidence by validating multiinterferometric SAR data: the case study of Anthemountas basin (Northern Greece)
Revision of paper Advanced interpretation of land subsidence by validating multiinterferometric SAR data: the case study of Anthemountas basin (Northern Greece) By Raspini et al. General comments This
More informationAnalysis of ERS Tandem SAR Coherence From Glaciers, Valleys, and Fjord Ice on Svalbard
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 39, NO. 9, SEPTEMBER 2001 2029 Analysis of ERS Tandem SAR Coherence From Glaciers, Valleys, and Fjord Ice on Svalbard Dan Johan Weydahl Abstract
More informationPERSISTENT SCATTERER SAR INTERFEROMETRY APPLICATIONS ON LANDSLIDES IN CARPATHIANS (SOUTHERN POLAND)
Acta Geodyn. Geomater., Vol. 7, No. 3 (159), 1 7, 2010 PERSISTENT SCATTERER SAR INTERFEROMETRY APPLICATIONS ON LANDSLIDES IN CARPATHIANS (SOUTHERN POLAND) Zbigniew PERSKI 1) *, Tomasz WOJCIECHOWSKI 1)
More informationRadar Remote Sensing: Monitoring Ground Deformations and Geohazards from Space
Radar Remote Sensing: Monitoring Ground Deformations and Geohazards from Space Xiaoli Ding Department of Land Surveying and Geo-Informatics The Hong Kong Polytechnic University A Question 100 km 100 km
More informationwhere ν and γ are site- and season- specific constants, and P, T and U are modeled as h h h P h = P0 ( *10 ( h h0 )) T h = T0 k( h h0 )
Ground Fissure Monitoring in China Based on PALSAR Data 430 Zhiwei Li (1) Jianjun Zhu (2), Zhengrong Zou (2), Wujiao Dai (2) (1) School of Geosciences & Info-Physics, Central South University, Changsha
More informationSupplementary information. Analytical Techniques and Measurement Uncertainty
Supplementary information Analytical Techniques and Measurement Uncertainty Interferomeric SAR (InSAR) allows measurement of millimetre-level surface displacements, including land subsidence, in the radar
More informationMitigation of Atmospheric Water-vapour Effects on Spaceborne Interferometric SAR Imaging through the MM5 Numerical Model
PIERS ONLINE, VOL. 6, NO. 3, 2010 262 Mitigation of Atmospheric Water-vapour Effects on Spaceborne Interferometric SAR Imaging through the MM5 Numerical Model D. Perissin 1, E. Pichelli 2, R. Ferretti
More informationPHASE UNWRAPPING. Sept. 3, 2007 Lecture D1Lb4 Interferometry: Phase unwrapping Rocca
PHASE UNWRAPPING 1 Phase unwrapping 2 1D Phase unwrapping Problem: given the wrapped phase ψ=w(φ) find the unwrapped one ψ. The wrapping operator: W(φ)=angle(exp(j φ)), gives always solution -π π and is
More informationSURFACE DEFORMATION OF ALPINE TERRAIN DERIVED BY PS-INSAR TECHNIQUE ON THE SIACHEN GLACIER
SURFACE DEFORMATION OF ALPINE TERRAIN DERIVED BY PS-INSAR TECHNIQUE ON THE SIACHEN GLACIER Junchao Shi and Ling Chang Department of Remote Sensing, Delft University of Technology, the Netherlands. ABSTRACT
More informationP079 First Results from Spaceborne Radar Interferometry for the Study of Ground Displacements in Urban Areas SUMMARY
P079 First Results from Spaceborne Radar Interferometry for the Study of Ground Displacements in Urban Areas C.M. Crosetto (Instituto de Geomatica), C.A. Casas (University of Barcelona), R.G. Ranieri (University
More information3D temporal evolution of displacements recorded on Mt. Etna from the 2007 to 2010 through the SISTEM method
3D temporal evolution of displacements recorded on Mt. Etna from the 2007 to 2010 through the SISTEM method Bonforte A., Guglielmino F.,, Puglisi G. INGV Istituto Nazionale di Gofisica e vulcanologia Osservatorio
More informationSNOW MASS RETRIEVAL BY MEANS OF SAR INTERFEROMETRY
SNOW MASS RETRIEVAL BY MEANS OF SAR INTERFEROMETRY Helmut Rott (1), Thomas Nagler (1), Rolf Scheiber (2) (1) ENVEO, Environmental Earth Observation OEG, Exlgasse 39, A-6020 Innsbruck, Austria E-mail: Helmut.Rott@enveo.at
More informationTHREE DIMENSIONAL DETECTION OF VOLCANIC DEPOSIT ON MOUNT MAYON USING SAR INTERFEROMETRY
ABSTRACT THREE DIMENSIONAL DETECTION OF VOLCANIC DEPOSIT ON MOUNT MAYON USING SAR INTERFEROMETRY Francis X.J. Canisius, Kiyoshi Honda, Mitsuharu Tokunaga and Shunji Murai Space Technology Application and
More informationUSE OF INTERFEROMETRIC SATELLITE SAR FOR EARTHQUAKE DAMAGE DETECTION ABSTRACT
USE OF INTERFEROMETRIC SATELLITE SAR FOR EARTHQUAKE DAMAGE DETECTION Masashi Matsuoka 1 and Fumio Yamazaki 2 ABSTRACT Synthetic Aperture Radar (SAR) is one of the most promising remote sensing technologies
More informationMULTILOOKING is an essential procedure for improving
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, VOL. 8, NO. 3, MAY 011 441 Adaptive InSAR Stack Multilooking Exploiting Amplitude Statistics: A Comparison Between Different Techniques and Practical Results
More information27th Seismic Research Review: Ground-Based Nuclear Explosion Monitoring Technologies
GROUND TRUTH OF AFRICAN AND EASTERN MEDITERRANEAN SHALLOW SEISMICITY USING SAR INTERFEROMETRY AND GIBBS SAMPLING INVERSION Benjamin A. Brooks 1, Francisco Gomez 2, Eric A. Sandvol 2, and Neil Frazer 1
More informationAvailable online at GHGT-9. Detection of surface deformation related with CO 2 injection by DInSAR at In Salah, Algeria
Available online at www.sciencedirect.com Energy Procedia 100 (2009) (2008) 2177 2184 000 000 Energy Procedia www.elsevier.com/locate/procedia www.elsevier.com/locate/xxx GHGT-9 Detection of surface deformation
More informationACHIEVING THE ERS-2 ENVISAT INTER-SATELLITE INTERFEROMETRY TANDEM CONSTELLATION.
ACHIEVING THE ERS-2 ENVISAT INTER-SATELLITE INTERFEROMETRY TANDEM CONSTELLATION M. A. Martín Serrano (1), M. A. García Matatoros (2), M. E. Engdahl (3) (1) VCS-SciSys at ESA/ESOC, Robert-Bosch-Strasse
More informationOn the variations of InSAR-ICA altitudes in a mountain area of the Sele Valley (South Italy)
ANNALS OF GEOPHYSICS, VOL. 52, N. 2, April 2009 On the variations of InSAR-ICA altitudes in a mountain area of the Sele Valley (South Italy) Paola Ballatore Mediterranean Agency for Remote Sensing and
More informationSnow Water Equivalent (SWE) of dry snow derived from InSAR -theory and results from ERS Tandem SAR data
Snow Water Equivalent (SWE) of dry snow derived from InSAR -theory and results from ERS Tandem SAR data Tore Guneriussen, Kjell Arild Høgda, Harald Johnsen and Inge Lauknes NORUT IT Ltd., Tromsø Science
More informationAnalysis of the Temporal Behavior of Coherent Scatterers (CSs) in ALOS PalSAR Data
Analysis of the Temporal Behavior of Coherent Scatterers (CSs) in ALOS PalSAR Data L. Marotti, R. Zandona-Schneider & K.P. Papathanassiou German Aerospace Center (DLR) Microwaves and Radar Institute0 PO.BOX
More informationTwo-pass DInSAR uses an interferometric image pair and an external digital elevation model (DEM). Among the two singlelook complex (SLC) images, one i
DIFFERENTIAL RADAR INTERFEROMETRY AND ITS APPLICATION IN MONITORING UNDERGROUND COAL MINING-INDUCED SUBSIDENCE Yaobin Sheng a, b, c, *, Yunjia Wang a, b, Linlin Ge c, Chris Rizos c a Jiangsu Key Laboratory
More informationERAD Water vapor observations with SAR, microwave radiometer and GPS: comparison of scaling characteristics
Proceedings of ERAD (2002): 190 194 c Copernicus GmbH 2002 ERAD 2002 Water vapor observations with SAR, microwave radiometer and GPS: comparison of scaling characteristics D. N. Moisseev 1, R. F. Hanssen
More informationInvestigating landslides with space-borne Synthetic Aperture Radar (SAR) interferometry
Engineering Geology 88 (2006) 173 199 www.elsevier.com/locate/enggeo Investigating landslides with space-borne Synthetic Aperture Radar (SAR) interferometry Carlo Colesanti 1, Janusz Wasowski a, a CNR-IRPI,
More informationCLOSE FORMATION FLIGHT OF PASSIVE RECEIVING MICRO-SATELLITES
CLOSE FORMATION FLIGHT OF PASSIVE RECEIVING MICRO-SATELLITES Hauke Fiedler (1) and Gerhard Krieger (2) (1) DLR, HR, Weßling, Germany, E-mail:hauke.fiedler@dlr.de (2) DLR, HR, Weßling, Germany, E-mail:gerhard.krieger@dlr.de
More informationGeneration and Validation of Digital Elevation Model using ERS - SAR Interferometry Remote Sensing Data
Jour. Agric. Physics, Vol. 7, pp. 8-13 (2007) Generation and Validation of Digital Elevation Model using ERS - SAR Interferometry Remote Sensing Data SHELTON PADUA 1, VINAY K. SEHGAL 2 AND K.S. SUNDARA
More informationMEASUREMENT OF SURFACE DISPLACEMENT CAUSED BY UNDERGROUND NUCLEAR EXPLOSIONS BY DIFFERENTIAL SAR INTERFEROMETRY
MEASUREMENT OF SURFACE DISPLACEMENT CAUSED BY UNDERGROUND NUCLEAR EXPLOSIONS BY DIFFERENTIAL SAR INTERFEROMETRY X. Cong a, KH. Gutjahr b, J. Schlittenhardt a, U. Soergel c, a Bundesanstalt für Geowissenschaften
More informationIEEE GEOSCIENCE AND REMOTE SENSING LETTERS, VOL. 11, NO. 8, AUGUST
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, VOL. 11, NO. 8, AUGUST 2014 1355 Multitemporal Multitrack Monitoring of Wetland Water Levels in the Florida Everglades Using ALOS PALSAR Data With Interferometric
More informationSentinel-1 IW mode time-series analysis
Fringe 2015 Workshop Sentinel-1 IW mode time-series analysis When / How / Whether to stitch? Petar Marinkovic Yngvar Larsen PPO.labs Norut Objective Sentinel-1 InSAR for routine and operational monitoring
More informationCOAL MINE LAND SUBSIDENCE MONITORING BY USING SPACEBORNE INSAR DATA-A CASE STUDY IN FENGFENG, HEBEI PROVINCE, CHINA
COAL MINE LAND SUBSIDENCE MONITORING BY USING SPACEBORNE INSAR DATA-A CASE STUDY IN FENGFENG, HEBEI PROVINCE, CHINA Li Cao a, Yuehua Zhang a, Jianguo He a, Guang Liu b,huanyin Yue b, Runfeng Wang a, Linlin
More informationInnovative InSAR approach to tackle strong nonlinear time lapse ground motion
FMGM 2015 PM Dight (ed.) 2015 Australian Centre for Geomechanics, Perth, ISBN 978-0-9924810-2-5 https://papers.acg.uwa.edu.au/p/1508_15_salva/ Innovative InSAR approach to tackle strong nonlinear time
More informationStrategies for Measuring Large Scale Ground Surface Deformations: PSI Wide Area Product Approaches
Strategies for Measuring Large Scale Ground Surface Deformations: PSI Wide Area Product Approaches J. Duro (1), R. Iglesias (1), P. Blanco-Sánchez (1), D. Albiol (1), T. Wright (2), N. Adam (3), F. Rodríguez
More informationSpatiotemporal analysis of ground deformation at Campi Flegrei and Mt Vesuvius, Italy, observed by Envisat and Radarsat-2 InSAR during
Spatiotemporal analysis of ground deformation at Campi Flegrei and Mt Vesuvius, Italy, observed by Envisat and Radarsat InSAR during 233 Sergey V. Samsonov, Pablo J. González, Kristy F. Tiampo, Antonio
More informationA Radar Eye on the Moon: Potentials and Limitations for Earth Imaging
PIERS ONLINE, VOL. 6, NO. 4, 2010 330 A Radar Eye on the Moon: Potentials and Limitations for Earth Imaging M. Calamia 1, G. Fornaro 2, G. Franceschetti 3, F. Lombardini 4, and A. Mori 1 1 Dipartimento
More informationInSAR atmospheric effects over volcanoes - atmospheric modelling and persistent scatterer techniques
InSAR atmospheric effects over volcanoes - atmospheric modelling and persistent scatterer techniques Rachel Holley 1,2, Geoff Wadge 1, Min Zhu 1, Ian James 3, Peter Clark 4 Changgui Wang 4 1. Environmental
More informationEvaluation of spatial moisture distribution during CLARA 96 using spaceborne radar interferometry
Evaluation of spatial moisture distribution during CLARA 96 using spaceborne radar interferometry Ramon F. Hanssen and Tammy M. Weckwerth DEOS, Delft Institute for Earth-Oriented Space, Delft University
More informationAPLICATION OF INSAR TO THE STUDY OF GROUND DEFORMATION IN THE MEXICALI VALLEY, B. C., MEXICO.
APLICATION OF INSAR TO THE STUDY OF GROUND DEFORMATION IN THE MEXICALI VALLEY, B. C., MEXICO. O. Sarychikhina (1), R. Mellors (2), E. Glowacka (1). (1) Centro de Investigacion Cientifica y Educaccion Superior
More informationDAMS REGIONAL SAFETY WARNING USING TIME-SERIES INSAR TECHNIQUES
DAMS REGIONAL SAFETY WARNING USING TIME-SERIES INSAR TECHNIQUES Dora Roque *, Daniele Perissin, Ana P. Falcão, Ana M. Fonseca, Maria J. Henriques **, and Jacinto Franco * Laboratório Nacional de Engenharia
More informationInfrastructure monitoring using SAR interferometry
Infrastructure monitoring using SAR interferometry Hossein Nahavandchi Roghayeh Shamshiri Norwegian University of Science and Technology (NTNU), Department of Civil and Environmental Engineering Geodesy
More informationThe financial and communal impact of a catastrophe instantiated by. volcanoes endlessly impact on lives and damage expensive infrastructure every
Chapter 1 Introduction The financial and communal impact of a catastrophe instantiated by geophysical activity is significant. Landslides, subsidence, earthquakes and volcanoes endlessly impact on lives
More informationImpact of the Envisat Mission Extension on SAR data
Impact of the Envisat Mission Extension on SAR data Impact of Envisat extension on SAR data Prepared by nuno miranda Reference Issue 0.9 Revision Date of Issue 23 August 2010 Status Preliminary version
More informationImproved PSI Performance for Landslide Monitoring Applications. J. Duro, R. Iglesias, P. Blanco-Sánchez, F. Sánchez and D. Albiol
Improved PSI Performance for Landslide Monitoring Applications J. Duro, R. Iglesias, P. Blanco-Sánchez, F. Sánchez and D. Albiol Outline Area of Study Previous PSI (and others) studies Main conclusions
More informationInSAR techniques and applications for monitoring landslides and subsidence
Geoinformation for European-wide Integration, Benes (ed.) 2003 Millpress, Rotterdam, ISBN 90-77017-71-2 InSAR techniques and applications for monitoring landslides and subsidence H. Rott & T. Nagler Institut
More informationApplication of satellite InSAR data for hydrocarbon reservoir monitoring
Application of satellite InSAR data for hydrocarbon reservoir monitoring A. Tamburini, A. Belson, A. Ferretti, F. Novali TRE Milano, Italy Copyright - Tele-Rilevamento Europa - 2004 Outline SqueeSAR TM
More informationDETECTING ICE MOTION IN GROVE MOUNTAINS, EAST ANTARCTICA WITH ALOS/PALSAR AND ENVISAT/ASAR DATA
DETECTING ICE MOTION IN GROVE MOUNTAINS, EAST ANTARCTICA WITH ALOS/PALSAR AND ENVISAT/ASAR DATA TIAN Xin (1), LIAO Mingsheng (1), ZHOU Chunxia (2), ZHOU Yu (3) (1) State Key Laboratory of Information Engineering
More informationReport no.: ISSN Grading: Open
Geological Survey of Norway N-7491 Trondheim, Norway REPORT Report no.: 2005.082 ISSN 0800-3416 Grading: Open Title: Subsidence in Trondheim, 1992-2003: Results of PSInSAR analysis Authors: Dehls, J. F.
More informationImpact of the Envisat Mission Extension on SAR data
Impact of the Envisat Mission Extension on SAR data Impact of Envisat Mission Extension on SAR data - 1.0 Prepared by Nuno Miranda, Berthyl Duesmann, Monserrat Pinol, Davide Giudici, Davide D Aria Reference
More informationConstructing high-resolution, absolute maps of atmospheric water vapor by combining InSAR and GNSS observations
Constructing high-resolution, absolute maps of atmospheric water vapor by combining InSAR and GNSS observations Fadwa Alshawaf, Stefan Hinz, Michael Mayer, Franz J. Meyer fadwa.alshawaf@kit.edu INSTITUTE
More informationInSAR measurements of volcanic deformation at Etna forward modelling of atmospheric errors for interferogram correction
InSAR measurements of volcanic deformation at Etna forward modelling of atmospheric errors for interferogram correction Rachel Holley, Geoff Wadge, Min Zhu Environmental Systems Science Centre, University
More informationPublication V Finnish Society of Photogrammetry and Remote Sensing (FSPRS)
Publication V Kirsi Karila, Mika Karjalainen, and Juha Hyyppä. 2005. Urban land subsidence studies in Finland using synthetic aperture radar images and coherent targets. The Photogrammetric Journal of
More informationHaiti Earthquake (12-Jan-2010) co-seismic motion using ALOS PALSAR
Haiti Earthquake (12-Jan-2010) co-seismic motion using ALOS PALSAR Urs Wegmüller, Charles Werner, Maurizio Santoro Gamma Remote Sensing, CH-3073 Gümligen, Switzerland SAR data: JAXA, METI; PALSAR AO Project
More informationExamination Questions & Model Answers (2009/2010) PLEASE PREPARE YOUR QUESTIONS AND ANSWERS BY USING THE FOLLOWING GUIDELINES;
Examination s & Model Answers (2009/2010) PLEASE PREPARE YOUR QUESTIONS AND ANSWERS BY USING THE FOLLOWING GUIDELINES; 1. If using option 2 or 3 use template provided 2. Use Times New Roman 12 3. Enter
More informationEffect of Unmodelled Reference Frame Motion on InSAR Deformation Estimates
Effect of Unmodelled Reference Frame Motion on InSAR Deformation Estimates Hermann Bähr 1, Sami Samiei-Esfahany 2 and Ramon Hanssen 2 1 Karlsruhe Institute of Technology, Germany 2 Delft University of
More information