SCIENTIA SINICA Informationis, Volume 48, Issue 8: 1051-1064(2018) https://doi.org/10.1360/N112017-00023

A novel 3-D reconstruction approach based on group sparsity of array InSAR

Hang LI1,2,3, Xingdong LIANG1,2,3,*, Fubo ZHANG1,2, Chibiao DING1,2,3, Yirong WU1,2,3
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  • ReceivedMar 10, 2017
  • AcceptedMay 17, 2017
  • PublishedJan 18, 2018


As an advanced technology, synthetic aperture radar tomography (Tomo SAR) provides the feasibility to solve the layover problem caused by the inherent side-view mode in SAR sensors. However, the conventional nonparametric spectral estimation methods, such as truncated singular value decomposition (TSVD), are limited by the poor elevation resolution and cannot meet the needs of practical applications. Currently, SAR 3-D imaging methods based on compressive sensing are typically used; nevertheless, classic compressive sensing algorithms such as BP and OMP still exhibitproblems such as low efficiency, weak super-resolution performance, and poor anti-interference ability. Therefore, an algorithm with high robustness and super-resolution performance is significantly demanded.In this paper, a novel array InSAR 3-D reconstruction algorithm based on group-sparsity is proposed. It is improved based on the existing compressive sensing algorithm and exhibits better super-resolution and robustness. Based on the simulation data and the actual data of the first domestic 3-D imaging experiment by an airborne array InSAR, the super-resolution ability ofthe algorithm is verified, and the 3-D imaging results of the buildings are obtained.

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  • Figure 1

    (a) The imaging geometry of array InSAR; (b) the layover model of array InSAR

  • Figure 2

    Workflow of the proposed approach of $3$-D reconstruction with array InSAR

  • Figure 3

    Comparison of scatters reconstruction with methods of TSVD, BP and our proposed algorithm. Interval of scatters is (a) 80 m, (b) 40 m, (c) 10 m, (d) 2 m, (e) 1 m, respectively; (f) magnification of part of (e)

  • Figure 4

    Performance evaluation of our proposed algorithm. Detection rate with different (a) super-resolution factors, (b) SNR, (c) array numbers; (d) detection rate with fixed $N~\times~$SNR; (e) super-resolution ability of different $N~\times~$SNR

  • Figure 5

    (a) Laying of corner reflectors with layover phenomenon; (b) SAR image of layover area

  • Figure 6

    Comparison of reconstruction of layover calibration pionts between methods of BP and our proposed algorithm. distance of 15 m with methods of (a) BP, (b) our proposed algorithm; distance of 1.8 m with methods of (c) BP, (d) our proposed algorithm

  • Figure 7

    (a) $2$-D SAR image of building areas; (b) result of $3$-D reconstruction with point clouds

  • Figure 8

    Result of $3$-D reconstruction of building areas with optical patch

  • Table 1   The system parameters of array InSAR
    Item Parameter Item Parameter
    Frequency 15 GHz Velocity 70 m/s
    Bandwidth 500 MHz Azimuth beam width 2$^{\circ}$
    PRF 1 kHz Range beam width 27$^{\circ}$
    Height 600 m Down-view angle 25$^{\circ}$

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