| Summary: | 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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