Efficient SAR Tomographic Inversion via Sparse Bayesian Learning

SAR tomographic inversion (TomoSAR) has been widely employed for 3-D urban mapping. Existing algorithms are mostly based on an explicit inversion of the SAR imaging model, which are often computationally expensive for large scale processing. This is especially true for compressive sensing-based TomoSAR algorithms. Previous literature showed perspective of using data-driven methods like PCA and kernel PCA to decompose the signal and reduce the computational complexity of parameter inversion. This paper gives a preliminary demonstration of a data-driven TomoSAR method based on sparse Bayesian learning. Experiments on simulated data show the proposed algorithm can provide moderate detection rate and super-resolution power, comparing to the state-of-the-art compressive sensing based algorithms. As the proposed algorithm is purely based on conventional (non-superresolving) estimators, it is much more computationally efficient than compressive sensing based ones. This gives us a perspective of employing it for large scale TomoSAR processing. Experiments on real data will be given in the final paper.