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| Citation: | ZHAO Yao, XU Juncong, QUAN Xiangyin, et al. Tomographic SAR imaging method based on sparse and low-rank structures[J]. Journal of Radars, 2022, 11(1): 52–61. doi: 10.12000/JR21210
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Tomographic SAR Imaging Method Based on Sparse and Low-rank Structures
doi: 10.12000/JR21210
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Abstract
This paper proposes a three-dimensional tomographic SAR imaging method based on a combined sparse and low-rank structures. The traditional Compressed Sensing (CS) based tomographic SAR imaging methods only utilize the sparse representation and reconstruct along the elevation axis of a given azimuth-distance unit. Considering that the target distributions in cities, forests, and other cases are relatively similar, the elevation backscattering patterns of adjacent azimuth-range cells (pixels) are expected to be highly correlated. The proposed method introduces the Karhunen-Loeve transform to characterize the low-rank structures of the elevation of the target areas and constructs a tomographic SAR imaging model that combines sparse and low-rank structures. The ADMM algorithm is applied to solve the tomographic SAR imaging model, the complex original optimization problem is decomposed into several relatively simple sub-problems, and the tomographic SAR imaging results are obtained by the alternate projection of optimization variables. This method improves the reconstruction accuracy in the case of a few interferograms or channels and has better imaging performance. Simulations and real data experiments show that the reconstruction method can effectively separate the scatterers and ensure the accuracy of the reconstruction energy, maintain a good imaging performance under the condition of reducing the number of interferograms or channels, and effectively suppress the artifacts. -
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References
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- Figure 1. Algorithm flowchart
- Figure 2. Normalized energy distribution in the elevation direction
- Figure 3. Optical image of F-SAR measured scene
- Figure 4. Image of Emei area
- Figure 5. Comparison of the simulation result of backscattered energy distribution of the elevation obtained by each algorithm with 9 interferograms
- Figure 6. Comparison of the simulation result of backscattered energy distribution of the elevation obtained by each algorithm with 6 interferograms
- Figure 7. Mean square error of each algorithm at different SNR values with 9 interferograms
- Figure 8. Mean square error of each algorithm at different SNR values with 6 interferograms
- Figure 9. Reconstruction results of the forest area by each algorithm with 9 interferograms
- Figure 10. Reconstruction results of the forest area by each algorithm with 6 interferograms
- Figure 11. Reconstruction results of Emei area by each algorithm with 12 channels
- Figure 12. Reconstruction results of Emei area by each algorithm with 8 channels
- Figure 13. Comparison of range slices of imaging results of each method