Generalized Parametric Iterative Approach for Tomographic SAR Reconstruction

The reconstruction of high-elevation natural and artificial structures through synthetic aperture radar (SAR) tomography has been an active research topic owing to its significance in various earth science applications. However, the complexity of this task arises from inaccuracies in the estimated reconstruction, attributed to factors such as low signal-to-noise ratios, decorrelations, few and uneven measurements, and overlapping scatterers. The utilization of iterative spectral estimation methods has been demonstrated to be beneficial in addressing some of these inaccuracies. Thus, selecting the best method within this class constitutes a challenge. In this context, our letter aims to propose a generalized formula linking the maximum likelihood (ML)-based iterative methods via a regularization parameter. The behavior of the latter is analyzed for several values to unveil the potential of the proposed approach in achieving a balance between noise reduction and detection performance. The experimental study has been conducted on simulated and real SAR data acquired by airborne and spaceborne systems covering tropical forests and build-up areas. The obtained results show the effectiveness and performance of the optimal regularization parameter to eliminate noise while preserving the scatterers’ contribution.