Regularization of SAR Tomography for 3-D Height Reconstruction in Urban Areas

Height reconstruction in urban areas using multi-baseline synthetic aperture radar (SAR) images is still a challenging task. Due to the superimposition of scatterers from different elevations in a same resolution cell, the generation of digital elevation model is not straightforward. Classical SAR interferometry cannot be adopted, while standard SAR tomography (TomoSAR) can fail to well identify and separate the scatterers in case that the relative contribution of a scatterer to the others is insignificant. Moreover, irregular and low number of sampling can increase the ambiguity level and introduce artifacts with strong variance from pixel to pixel. Coping with this problem typically requires large number of datasets with proper sampling. In this paper, contextual information is exploited to reduce the ambiguity and resolve the superimposition of the scatterers regardless of their relative power contributions, even in the case of a limited number of images. In particular, the proposed approach, starting from standard spectral estimators, introduces a regularization term to include the a priori information about scene height variation in the array processing chain. The reconstruction problem is set as an energy minimization problem that is solved using graph-cut-based optimization algorithms, where the solution of localization is the linkage between optimization of signal energy along direction of arrival and controlling height variation within the neighbors of selected pixel. Details of experimental results in the form of tomographic slices as well as three-dimensional point cloud generation, from simulated and real datasets are included to demonstrate the effectiveness of the proposed reconstruction approach.