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  1. 1. Processing of MEMPHIS mmW multi-baseline InSAR data Christophe Magnard, Erich Meier and David Small Remote Sensing Laboratories Department of Geography University of Zurich, Switzerland Honolulu, July 30, 2010 Helmut Essen and Thorsten Brehm Fraunhofer-Institut für Hochfrequenzphysik und Radartechnik FHR Wachtberg, Germany
  2. 2. Overview <ul><li>MEMPHIS SAR system </li></ul><ul><li>Processing method </li></ul><ul><ul><li>Data focusing </li></ul></ul><ul><ul><li>Multi-baseline processing </li></ul></ul><ul><ul><li>Phase to digital surface model conversion </li></ul></ul><ul><ul><li>Correction of azimuth phase undulations </li></ul></ul><ul><li>Experimental results </li></ul><ul><li>Discussion </li></ul>1/15
  3. 3. MEMPHIS SAR system (I) <ul><li>MEMPHIS (Multi-frequency Experimental Monopulse High-resolution Interferometric SAR) developed by Fraunhofer-FHR </li></ul><ul><li>Multi-baseline cross-track interferometric system </li></ul><ul><li>Longest baselines: 0.275 m for the 35 GHz antenna, 0.16 m for the 94 GHz antenna </li></ul>MEMPHIS 35 GHz antenna 2/15 Platform C-160 Transall Available frequencies 35 and 94 GHz (Ka- and W- bands) Bandwidth 800 MHz (stepped frequency chirp) PRF 1500 Hz Typical velocity 75 m/s Typical near range distance 1.5 km Flying altitude 300 – 1000 a.g.l. Depression angle 20 ° – 35° Typical scene dimension 600 m swath width x 3000 m azimuth length
  4. 4. MEMPHIS SAR system (II) <ul><li>Experimental, removable system </li></ul><ul><li>Inclined depending on the data take requirements </li></ul><ul><li>Navigation data: </li></ul><ul><ul><li>Differential GPS (dGPS) </li></ul></ul><ul><ul><li>Aircraft INS </li></ul></ul><ul><ul><li>GPS & INS operate remotely from the SAR antenna </li></ul></ul><ul><ul><li>Lever arms measured with accuracy ~ a few centimeters </li></ul></ul><ul><li>Kalman filtering used to combine dGPS and INS data </li></ul><ul><li>Corner reflectors placed at all test sites for data calibration </li></ul>3/15
  5. 5. Data focusing <ul><li>The SAR data are processed and focused to obtain single look complex (SLC) images </li></ul><ul><li>The same parameters are used for each receiver (horn) </li></ul><ul><li>Typical procedure for high resolution processing with  -k: </li></ul><ul><li>Extraction of the SAR raw data </li></ul><ul><li>Range compression of data from each stepped chirp (8 stepped chirps) </li></ul><ul><li>Stepped frequency chirp processing </li></ul><ul><li>First order motion compensation </li></ul><ul><li>Azimuth compression </li></ul>Ka-band SAR image (resolution 0.2 m) 4/15 range azimuth
  6. 6. Multi-baseline processing (I) <ul><li>Two multi-baseline processing algorithms were tested: </li></ul><ul><li>Coarse to fine phase unwrapping process: shorter baselines used exclusively to assist in unwrapping of the longest-baseline interferogram 1, 2 </li></ul><ul><li>Interferograms are calculated for each possible combination of SLC data (i.e. for each possible baseline) </li></ul><ul><li>Clock analogy: </li></ul><ul><ul><li>Interferogram with the smallest baseline (i.e. with the largest height ambiguity) used as the reference unwrapped phase ( ≈ hour hand) </li></ul></ul><ul><ul><li>Interferograms with longer baselines are used to refine the unwrapped phase ( ≈ minute, second hand) </li></ul></ul>1 H. Essen, T. Brehm, S. Boehmsdorff, U. Stilla, “Multibaseline Interferometric SAR at Millimeterwaves, Test of an Algorithm on Real Data and a Synthetic Scene”, Proceedings of SPIE , vol. 6746, Sept 2007. 2 C. Magnard, E. Meier, M. Rüegg, T. Brehm, H. Essen, “High Resolution Millimeter Wave SAR Interferometry”, Proceedings of the IEEE International Geoscience and Remote Sensing Symposium IGARSS , pp. 5061 – 5064, Barcelona, July 2007. 5/15
  7. 7. Multi-baseline processing (II) <ul><li>Maximum Likelihood (ML) 1 : </li></ul><ul><ul><li>Direct use of SLC data array </li></ul></ul><ul><ul><li>Find optimum interferometric phase using ML estimator </li></ul></ul><ul><li>In both cases, remaining height ambiguities (related to the smallest baseline, given significant altitude changes within the scene) can be unwrapped with a conventional phase unwrapping algorithm. </li></ul><ul><li>We use the statistical-cost network-flow algorithm for phase unwrapping ( SNAPHU 2 ) </li></ul>1 P. Lombardo, F. Lombardini, “Multi-baseline SAR Interferometry for Terrain Slope Adaptivity”, IEEE Proc. Nat. Radar Conf. 1997 , pp. 196-201, New York, May 1997. 2 C.W. Chen, “Statistical-Cost Network-Flow Approaches to Two-Dimensional Phase Unwrapping for Radar Interferometry”, PhD Dissertation , Department of Electrical Engineering, Stanford University, 2001. 6/15
  8. 8. Phase to digital surface model conversion <ul><li>Conversion of range and azimuth positions and their phase differences into digital surface model (DSM) with help of linearized flight track and tie points. </li></ul><ul><li>Data processed in a zero-Doppler geometry </li></ul><ul><ul><li>an appropriate coordinate system transformation enables simplifications </li></ul></ul><ul><li>Tie points used to determine constant phase offset  const </li></ul><ul><li>The geographical position and height of each point can then be computed </li></ul><ul><li>Equations and approach are derived from 1 and 2 </li></ul><ul><li>Regridding subsequently required to rasterize the DSM </li></ul>1 P. Rosen, S. Hensley, I. Joughin, F. Li, S. Madsen, E. Rodriguez, R. Goldstein, “Synthetic Apertur Radar Interferometry”, Proceedings of the IEEE , vol. 88, no. 3, pp. 333-381, March 2000. 2 D. Small, C. Werner, D. Nüesch, “Baseline modelling for ERS-1 SAR interferometry”, Proceedings of the IEEE International Geoscience and Remote Sensing Symposium IGARSS , vol. 3, pp. 1204-1206, Tokyo, August 1993. 7/15
  9. 9. Correction of azimuth phase undulations (I) <ul><li>Aircraft attitude variations cause fluctuations in the baseline components </li></ul><ul><ul><li>Phase undulations in interferograms </li></ul></ul><ul><li>Two possible methods to handle this: </li></ul><ul><li>Use attitude data measured by aircraft INS system to correct baseline for each pixel position </li></ul><ul><ul><li>INS accuracy usually acceptable, but sampling rate insufficient </li></ul></ul><ul><ul><li>Results are not accurate enough; method not applicable </li></ul></ul><ul><li>Use interferometric data to correct azimuth phase undulations 1, 2 </li></ul>1 P. Prats, J.J. Mallorqui, “Estimation of azimuth phase undulations with multisquint processing in airborne interferometric SAR images”, IEEE Transactions on Geoscience and Remote Sensing , vol. 41, no. 6, pp. 1530-1533, 2003. 2 P. Prats, A. Reigber, J.J. Mallorqui, “Interpolation-Free Coregistration and Phase-Correction of Airborne SAR Interferograms”, IEEE Geoscience and Remote Sensing Letters , vol. 1, no. 3, pp. 188 - 191, 07/2004. 8/15
  10. 10. Correction of azimuth phase undulations (II) <ul><li>Azimuth focusing performed with two different Doppler centroids, use of a reduced azimuth bandwidth to avoid overlapping spectra </li></ul><ul><li>Interferograms generated for both Doppler centroids, followed by a flat Earth removal and phase unwrapping </li></ul><ul><li>Integration of the phase difference between the two interferograms </li></ul><ul><li>Small squint angle difference (narrow azimuth beam width) can cause large integration errors </li></ul><ul><ul><li>Linear correction of integrated phase correction using INS attitude data </li></ul></ul><ul><ul><li>Kalman filtering to combine phase corrections calculated from INS and InSAR data </li></ul></ul><ul><li>Correction applied to range compressed data </li></ul>9/15
  11. 11. Correction of azimuth phase undulations (III) Interferogram without phase correction Interferogram with phase correction 10/15 range azimuth range azimuth +  +  0 -  - 
  12. 12. Experimental results <ul><li>InSAR flight campaign in 2009 over test sites in Switzerland </li></ul><ul><li>Results presented: highway roundabout near Zurich, Ka-band antenna </li></ul>Aerial imagery © swisstopo 11/15
  13. 13. Experimental results 12/15 1'239'830 1'238'030 2'703'980 2'704'650 590 m 520 m
  14. 14. Experimental results 13/15 1'239'830 1'238'030 2'703'980 2'704'650 +5 m -5 m 0 m +5 m -5 m 0 m
  15. 15. Experimental results <ul><li>Comparison of the masked InSAR DSM with a reference LIDAR DTM </li></ul><ul><li>Important: measured heights not identical (different physical phenomena, different vegetation states) </li></ul><ul><ul><li>Histogram not a perfect normal distribution </li></ul></ul><ul><li>For statistical information, negative side of the histogram is mirrored and standard deviation calculated: 0.672 m for this dataset. </li></ul><ul><li>Similar results obtained with other Ka-band datasets </li></ul>14/15
  16. 16. Discussion <ul><li>Main difficulty with MEMPHIS: producing precise and accurate products with imperfect navigation data and movable hardware components </li></ul><ul><li>Methods to overcome uncertainties: </li></ul><ul><ul><li>Tiepoints for calibrating navigation data and calculating  const </li></ul></ul><ul><ul><li>Correction of azimuth phase undulations using spectral diversity (or multi-squint) method combined with attitude data </li></ul></ul><ul><li>Use of the ML algorithm more difficult than expected: measurement of smallest baselines associated with high uncertainty </li></ul><ul><ul><li>Difficult to assess if ML phase is meaningful or corrupted by measurement uncertainties in smaller baselines </li></ul></ul><ul><ul><li>Results presented obtained with “coarse to fine” method </li></ul></ul><ul><li>Results in good agreement with LIDAR height models; standard deviation of their filtered difference ~0.6 – 0.7 m </li></ul>15/15
  17. 17. Thank You!