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Compressed sensing application in interferometric synthetic aperture radar

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A novel interferometric synthetic aperture radar (InSAR) signal processing method based on compressed sensing (CS) theory is investigated in this paper. InSAR image formation provides the scene reflectivity estimation along azimuth and range coordinates with the height information. While surveying the height information of the illuminated scene, the data volume enlarges. CS theory allows sparse sampling during the data acquisition, which can reduce the data volume and release the pressure on the record devices. InSAR system which configures two antennas to cancel the common backscatter random phase in each resolution element implies the sparse nature of the complex-valued InSAR image. The complex-valued image after conjugate multiplication that only a phase term proportional to the differential path delay is left becomes sparse in the transform domain. Sparse sampling such as M-sequence can be implemented during the data acquisition. CS theory can be introduced to the processing due to the sparsity and a link between raw data and interferometric complex-valued image can be built. By solving the CS inverse problem, the magnitude image and interferometric phase are generated at the same time. Results on both the simulated data and real data are presented. In comparison with the conventional SAR interferometry processing results, CS-based method shows the ability to keep the imaging quality with less data acquisition.

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  1. 1

    Graham L C. Synthetic interferometer radar for topographic mapping. Proc IEEE, 1974, 62: 763–768

  2. 2

    Bamler R, Hartl P. Synthetic aperture radar interferometry. Inverse Problem, 1998, 14: 1–54

  3. 3

    Allen C T. Interferometric synthetic aperture radar. IEEE Geosci Remote Sens Soc Newslett, 1995, 96: 6–13

  4. 4

    Berardino P, Fornaro G, Lanari R, et al. A new algorithm for surface deformation monitoring based on small baseline differential sar interferograms. IEEE Trans Geosci Remote Sens, 2002, 40: 2375–2383

  5. 5

    Donoho D L. Compressed sensing. IEEE Trans Inf Theory, 2006, 52: 1289–1306

  6. 6

    Candes E J, Romberg J, Tao T. Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information. IEEE Trans Inf Theory, 2006, 52: 489–509

  7. 7

    Baraniuk R, Steeghs P. Compressive radar imaging. In: Prcoeedings of IEEE Radar Conference, Boston, 2007. 128–133

  8. 8

    Zhang L, Xing M D, Qiu C W, et al. Achieving higher resolution isar imaging with limited pulses via compressed sampling. IEEE Geosci Remote Sens Lett, 2009, 6: 567–571

  9. 9

    Schmitt M, Stilla U. Compressive sensing based layover separation in airborne single-pass multi-baseline InSAR data. IEEE Geosci Remote Sens Lett, 2013, 10: 313–317

  10. 10

    Budillon A, Evangelista A, Schirinzi G. Three-dimensional sar focusing from multipass signals using compressive sampling. IEEE Trans Geosci Remote Sens, 2011, 49: 488–499

  11. 11

    Fornaro G, Lombardini F, Pauciullo A, et al. Tomographic processing of interferometric sar data: developments, applications, and future research perspectives. IEEE Signal Process Mag, 2014, 31: 41–50

  12. 12

    Zhu X X, Bamler R. Superresolving sar tomography for multidimensional imaging of urban areas: compressive sensingbased tomosar inversion. IEEE Signal Process Mag, 2014, 31: 51–58

  13. 13

    Li J, Xing M D, Wu S. Application of compressed sensing in sparse aperture imaging of radar. In: Proceedings of the 2nd Asian-Pacific Conference on Synthetic Aperture Radar, Xi’an, 2009. 651–655

  14. 14

    Austin C D, Ertin E, Moses R. Sparse signal methods for 3-D radar imaging. IEEE J Sele Topics Signal Process, 2011, 5: 408–423

  15. 15

    Potter L C, Ertin E, Parker J T, et al. Sparsity and compressed sensing in radar imaging. Proc IEEE, 2010, 98: 1006–1020

  16. 16

    Hou X, Yang J, Jiang G, et al. Complex sar image compression based on directional lifting wavelet transform with high clustering capability. IEEE Trans Geosci Remote Sens, 2013, 51: 527–538

  17. 17

    Samadi S, Cetin M, Masnadi-Shirazi M A. Sparse representation-based synthetic aperture radar imaging. IET Radar Sonar Nav, 2011, 5: 182–193

  18. 18

    Samadi S, Cetin M, Masnadi-Shirazi M A. Sparse signal representation for complex-valued imaging. In: Proceedings of IEEE 13th Digital Signal Processing Workshop and the 5th IEEE Signal Processing Education Workshop, Marco Island, 2009. 365–370

  19. 19

    Ramakrishnan N, Ertin E, Moses R. Enhancement of coupled multichannel images using sparsity constraints. IEEE Trans Image Process, 2010, 19: 2115–2126

  20. 20

    Rosen P A, Hensley S, Joughin I R, et al. Synthetic aperture radar interferometry. Proc IEEE, 2000, 88: 333–382

  21. 21

    Needell D, Vershynin R. Uniform uncertainty principle and signal recovery via regularized orthogonal matching pursuit. Found Comput Math, 2009, 9: 317–334

  22. 22

    Xu Z B, Zhang H, Wang Y, et al. L 1/2 regularization. Sci China Inf Sci, 2010, 53: 1159–1169

  23. 23

    Zeng J, Xu Z, Jiang H, et al. SAR imaging from compressed measurements based on L 1/2 regularization. In: Proceedings of IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Vancouver, 2011. 625–628

  24. 24

    Bioucas-Dias J M, Figueiredo M A T. A new twist: two-step iterative shrinkage/thresholding algorithms for image restoration. IEEE Trans Image process, 2007, 16: 2992–3004

  25. 25

    Zeng J, Xu Z, Zhang B, et al. Accelerated regularization based sar imaging via bcr and reduced newton skills. Signal Process, 2013, 93: 1831–1844

  26. 26

    Zeng J S, Fang J, Xu Z B. Sparse sar imaging based on L 1/2 regularization. Sci China Inf Sci, 2012, 55: 1755–1775

  27. 27

    Cohn M, Lempel A. On fast M-sequence transforms. IEEE Trans Inf Theory, 1977, 23: 135–137

  28. 28

    Candes E, Romberg J. Sparsity and incoherence in compressive sampling. Inverse Problem, 2007, 23: 969–985

  29. 29

    Xu G, Xing M D, Xia X G, et al. Sparse regularization of interferometric phase and amplitude for insar image formation based on bayesian representation. IEEE Trans Geosci Remote Sens, 2015, 53: 2123–2136

  30. 30

    Zeng J, Lin S, Wang Y, et al. L 1/2 regularization: convergence of iterative half thresholding algorithm. IEEE Trans Signal Process, 2014, 62: 2317–2329

  31. 31

    Costantini M, Iodice A, Magnapane L, et al. Monitoring terrain movements by means of sparse SAR differential interferometric measurements. In: Proceedings of IEEE 2000 International Geoscience and Remote Sensing Symposium, Honolulu, 2000. 3225–3227

  32. 32

    Meng D, Sethu V, Ambikairajah E, et al. A novel technique for noise reduction in insar images. IEEE Geosci Remote Sens Lett, 2007, 4: 226–230

  33. 33

    Stevens D, Cumming I G, Gray A. Options for airborne interferometric sar motion compensation. IEEE Trans Geosci Remote Sens, 1995, 33: 409–420

  34. 34

    Moreira A, Mittermayer J, Scheiber R. Extended chirp scaling algorithm for air- and spaceborne sar data processing in stripmap and scansar imaging modes. IEEE Trans Geosci Remote Sens, 1996, 34: 1123–1136

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This work was supported by National Natural Science Foundation of China (Grant No. 61271422).

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Correspondence to Liechen Li.

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Li, L., Li, D. & Pan, Z. Compressed sensing application in interferometric synthetic aperture radar. Sci. China Inf. Sci. 60, 102305 (2017). https://doi.org/10.1007/s11432-016-9017-6

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  • synthetic aperture radar (SAR)
  • interferometric synthetic aperture radar (InSAR)
  • compressed sensing (CS)
  • sparse sampling
  • sparsity in the transform domain