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  • 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. Overview
    • MEMPHIS SAR system
    • Processing method
      • Data focusing
      • Multi-baseline processing
      • Phase to digital surface model conversion
      • Correction of azimuth phase undulations
    • Experimental results
    • Discussion
  • 3. MEMPHIS SAR system (I)
    • MEMPHIS (Multi-frequency Experimental Monopulse High-resolution Interferometric SAR) developed by Fraunhofer-FHR
    • Multi-baseline cross-track interferometric system
    • Longest baselines: 0.275 m for the 35 GHz antenna, 0.16 m for the 94 GHz antenna
    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. MEMPHIS SAR system (II)
    • Experimental, removable system
    • Inclined depending on the data take requirements
    • Navigation data:
      • Differential GPS (dGPS)
      • Aircraft INS
      • GPS & INS operate remotely from the SAR antenna
      • Lever arms measured with accuracy ~ a few centimeters
    • Kalman filtering used to combine dGPS and INS data
    • Corner reflectors placed at all test sites for data calibration
  • 5. Data focusing
    • The SAR data are processed and focused to obtain single look complex (SLC) images
    • The same parameters are used for each receiver (horn)
    • Typical procedure for high resolution processing with  -k:
    • Extraction of the SAR raw data
    • Range compression of data from each stepped chirp (8 stepped chirps)
    • Stepped frequency chirp processing
    • First order motion compensation
    • Azimuth compression
    Ka-band SAR image (resolution 0.2 m) 4/15 range azimuth
  • 6. Multi-baseline processing (I)
    • Two multi-baseline processing algorithms were tested:
    • Coarse to fine phase unwrapping process: shorter baselines used exclusively to assist in unwrapping of the longest-baseline interferogram 1, 2
    • Interferograms are calculated for each possible combination of SLC data (i.e. for each possible baseline)
    • Clock analogy:
      • Interferogram with the smallest baseline (i.e. with the largest height ambiguity) used as the reference unwrapped phase ( ≈ hour hand)
      • Interferograms with longer baselines are used to refine the unwrapped phase ( ≈ minute, second hand)
    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. Multi-baseline processing (II)
    • Maximum Likelihood (ML) 1 :
      • Direct use of SLC data array
      • Find optimum interferometric phase using ML estimator
    • 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.
    • We use the statistical-cost network-flow algorithm for phase unwrapping ( SNAPHU 2 )
    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. Phase to digital surface model conversion
    • Conversion of range and azimuth positions and their phase differences into digital surface model (DSM) with help of linearized flight track and tie points.
    • Data processed in a zero-Doppler geometry
      • an appropriate coordinate system transformation enables simplifications
    • Tie points used to determine constant phase offset  const
    • The geographical position and height of each point can then be computed
    • Equations and approach are derived from 1 and 2
    • Regridding subsequently required to rasterize the DSM
    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. Correction of azimuth phase undulations (I)
    • Aircraft attitude variations cause fluctuations in the baseline components
      • Phase undulations in interferograms
    • Two possible methods to handle this:
    • Use attitude data measured by aircraft INS system to correct baseline for each pixel position
      • INS accuracy usually acceptable, but sampling rate insufficient
      • Results are not accurate enough; method not applicable
    • Use interferometric data to correct azimuth phase undulations 1, 2
    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. Correction of azimuth phase undulations (II)
    • Azimuth focusing performed with two different Doppler centroids, use of a reduced azimuth bandwidth to avoid overlapping spectra
    • Interferograms generated for both Doppler centroids, followed by a flat Earth removal and phase unwrapping
    • Integration of the phase difference between the two interferograms
    • Small squint angle difference (narrow azimuth beam width) can cause large integration errors
      • Linear correction of integrated phase correction using INS attitude data
      • Kalman filtering to combine phase corrections calculated from INS and InSAR data
    • Correction applied to range compressed data
  • 11. Correction of azimuth phase undulations (III) Interferogram without phase correction Interferogram with phase correction 10/15 range azimuth range azimuth +  +  0 -  - 
  • 12. Experimental results
    • InSAR flight campaign in 2009 over test sites in Switzerland
    • Results presented: highway roundabout near Zurich, Ka-band antenna
    Aerial imagery © swisstopo 11/15
  • 13. Experimental results 12/15 1'239'830 1'238'030 2'703'980 2'704'650 590 m 520 m
  • 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. Experimental results
    • Comparison of the masked InSAR DSM with a reference LIDAR DTM
    • Important: measured heights not identical (different physical phenomena, different vegetation states)
      • Histogram not a perfect normal distribution
    • For statistical information, negative side of the histogram is mirrored and standard deviation calculated: 0.672 m for this dataset.
    • Similar results obtained with other Ka-band datasets
  • 16. Discussion
    • Main difficulty with MEMPHIS: producing precise and accurate products with imperfect navigation data and movable hardware components
    • Methods to overcome uncertainties:
      • Tiepoints for calibrating navigation data and calculating  const
      • Correction of azimuth phase undulations using spectral diversity (or multi-squint) method combined with attitude data
    • Use of the ML algorithm more difficult than expected: measurement of smallest baselines associated with high uncertainty
      • Difficult to assess if ML phase is meaningful or corrupted by measurement uncertainties in smaller baselines
      • Results presented obtained with “coarse to fine” method
    • Results in good agreement with LIDAR height models; standard deviation of their filtered difference ~0.6 – 0.7 m
  • 17. Thank You!