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ZHOU Honghao, LIU Yanyang, LI Tao, et al. Structured low-rankness method of joint neighboring pixels for tomographic SAR 3d imaging[J]. Journal of Radars, in press. doi: 10.12000/JR25092
Citation: ZHOU Honghao, LIU Yanyang, LI Tao, et al. Structured low-rankness method of joint neighboring pixels for tomographic SAR 3d imaging[J]. Journal of Radars, in press. doi: 10.12000/JR25092
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Structured Low-Rankness Method of Joint Neighboring Pixels for Tomographic SAR 3D Imaging

DOI: 10.12000/JR25092 CSTR: 32380.14.JR25092
  • 1.

    State Key Laboratory of Millimeter Waves, Southeast University, Nanjing 210096, China

  • 2.

    Shanghai Institute of Satellite Engineering, Shanghai 201109, China

  • 3.

    Land Satellite Remote Sensing Application Center, Ministry of Natural Resources of P.R., Beijing 100048, China

  • 4.

    National Key Laboratory of Radar Signal Processing, Xidian University, Xi’an 710071, China

  • 5.

    Academy of Advanced Interdisciplinary Research, Xidian University, Xi’an 710071, China

Funds:  The National Natural Science Foundation of China (62071113), The Natural Science Foundation of Jiangsu Province (BK20211559), The Fundamental Research Funds for the Central Universities (2242022k60008)
  • Available Online: 2025-09-30
    Abstract
  • Tomographic SAR (TomoSAR) has significant value in scientific research and applications, as it enables three-dimensional (3D) imaging to address limitations such as scene overlay and projection geometric distortion. The elevation resolution of TomoSAR is limited by the aperture in the elevation direction. Consequently, super-resolution algorithms such as compressive sensing (CS) are generally used to enhance the performance of 3D imaging. However, conventional CS methods suffer from grid mismatch issues due to predefined discrete grids, which lead to limited resolution under practical constraints, such as limited channels and low signal-to-noise ratios. To address these limitations, a novel, gridless, super-resolution algorithm based on the structured low-rankness of joint neighboring pixels is proposed herein for tomographic 3D SAR Imaging. By enhancing the intrinsic structural observation, the efficacy of the model for 3D reconstruction can be effectively improved by increasing the number of valid observations. Specifically, a gridless, structured, low-rank, non-convex optimization model is constructed by leveraging the joint sparse characteristics of neighboring pixels, overcoming the limitations of traditional sparse grid-based approaches. Furthermore, an efficient solution is achieved using a projected gradient descent algorithm, and the dependence of the reconstruction performance on the sampling positions is reduced by introducing an incoherent feasible region constraint. Finally, the superiority of the proposed algorithm is validated through both simulation and analysis of real measured datasets, including the SARMV3D-1.0 airborne array dataset and spaceborne LuTan-1 dataset. The proposed algorithm achieves superior 3D reconstruction accuracy and stability compared to most existing state-of-the-art methods.

     

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