SAR tomography in urban areas: an approach based on sparse spectrum fitting

Tomography Synthetic Aperture Radar (TomoSAR) technology, as an extension of Interferometric SAR (InSAR), solves the inherent layover problem and provides three-dimensional (3D) imaging ability. Now, it is a promising technique in urban management and critical situation detection. However, in practical applications, the sparse and non-uniform distribution characteristics of the repeat-pass baselines make TomoSAR face the problem of low resolution and high sidelobes, which affect the 3D imaging effect. To address the above problems, a sparse spectrum fitting (SpSF) based TomoSAR processing approach is proposed in this paper. Compared with the traditional spatial spectrum estimation method, this method can achieve higher resolution and lower sidelobes, which is a significant advantage. The proposed method is also validated using computer simulation and the TerraSAR-X real data.