Sparse representation-based synthetic aperture radar imaging

Sparse representation-based synthetic aperture radar imaging

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There is increasing interest in using synthetic aperture radar (SAR) images in automated target recognition and decision-making tasks. The success of such tasks depends on how well the reconstructed SAR images exhibit certain features of the underlying scene. Based on the observation that typical underlying scenes usually exhibit sparsity in terms of such features, this paper presents an image formation method that formulates the SAR imaging problem as a sparse signal representation problem. For problems of complex-valued nature, such as SAR, a key challenge is how to choose the dictionary and the representation scheme for effective sparse representation. Since features of the SAR reflectivity magnitude are usually of interest, the approach is designed to sparsely represent the magnitude of the complex-valued scattered field. This turns the image reconstruction problem into a joint optimisation problem over the representation of magnitude and phase of the underlying field reflectivities. The authors develop the mathematical framework for this method and propose an iterative solution for the corresponding joint optimisation problem. The experimental results demonstrate the superiority of this method over previous approaches in terms of both producing high-quality SAR images and exhibiting robustness to uncertain or limited data.


    1. 1)
      • Spotlight synthetic aperture radar: signal processing algorithms
    2. 2)
      • Feature-enhanced synthetic aperture radar image formation based on nonquadratic regularization
    3. 3)
    4. 4)
      • Principles of computerized tomographic imaging
    5. 5)
      • Çetin, M., Moses, R.L.: `SAR imaging from partial-aperture data with frequency-band omissions', SPIE Defense and Security Symposium, Algorithms for Synthetic Aperture Radar Imagery XII, March 2005, Orlando, FL, USA, p. 32–43
    6. 6)
    7. 7)
      • Uncertainty principles and ideal atomic decomposition
    8. 8)
      • Maximal sparsity representation via l1 minimization
    9. 9)
    10. 10)
      • Exact reconstruction of sparse signals via nonconvex minimization
    11. 11)
      • Malioutov, D.M., Cetin, M., Willsky, A.S.: `Optimal sparse representations in general overcomplete bases', IEEE Int. Conf. Acoustics, Speech, and Signal Processing (ICASSP), May 2004, p. 793–796
    12. 12)
      • Fundamentals of statistical signal processing: estimation theory
    13. 13)
      • Maximum entropy regularization in inverse synthetic aperture radar imagery
    14. 14)
      • High resolution radar imaging
    15. 15)
    16. 16)
      • Iterative methods for total variation denoising
    17. 17)
      • Matrix computations
    18. 18)
      • Feature-preserving regularization method for complex-valued inverse problems with application to coherent imaging
    19. 19)
      • Nonlinear image recovery with half-quadratic regularization
    20. 20)
    21. 21)
      • Ideal spatial adaptation via wavelet shrinkage
    22. 22)
      • SAR image data compression using a tree-structured wavelet transform
    23. 23)
      • Rilling, G., Davies, M., Mulgrew, B.: `Compressed sensing based compression of SAR raw data', Signal Processing with Adaptive Sparse Structured Representations Workshop, April 2009, Saint-Malo, France
    24. 24)
      • The curvelet transform for image denoising
    25. 25)
      • Batu, O., Çetin, M.: `Hyper-parameter selection in non-quadratic regularization-based radar image formation', SPIE Defense and Security Symp., Algorithms for Synthetic Aperture Radar Imagery XV, March 2008, Orlando, FL, USA
    26. 26)
      • Speckle removal from SAR images in the undecimated wavelet domain
    27. 27)
      • Feature enhancement and ATR performance using non-quadratic optimization-based SAR imaging
    28. 28)
      • Image characterization for automatic target recognition algorithm evaluations
    29. 29)
      • High-definition vector imaging
    30. 30)
      • SAR minimum-entropy autofocus using an adaptive-order polynomial model
    31. 31)
      • Air Force Research Laboratory, Model Based Vision Laboratory, Sensor Data Management System ADTS:
    32. 32)
      • Backhoe Data Sample & Visual-D Challenge Problem, Air Force Research Laboratory, Sensor Data Management System:
    33. 33)
      • Moses, R.L., Potter, L.C., Cetin, M.: `Wide angle SAR imaging', Proc. SPIE, Algorithms for Synthetic Aperture Radar Imagery XI, April 2004, Orlando, FL, USA, p. 164–175
    34. 34)
      • Probability, random variables, and stochastic processes

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