Maximum-likelihood-based approach for single-pass synthetic aperture radar tomography over urban areas

Abstract

Synthetic aperture radar (SAR) tomography (TomoSAR) is one of the key techniques in remote sensing for a sophisticated, three-dimensional (3D) analysis of complex scenes such as forests or urban areas. During recent years, much progress has been made in order to deal with unfavourable data conditions and to further enhance the achievable resolutions. Since TomoSAR has essentially be seen as a spectral estimation problem in the context of array signal processing, most of the methods investigated until now rely on a relatively large tomographic aperture and a relatively high number of receiving antennas in order to be able to separately reconstruct scatterers below the Rayleigh resolution limit. In contrast to that, this study proposes a maximum-likelihood-based approach for tomographic SAR inversion, which provides a better height resolution than conventional array signal processing approaches even if the number of antennas or the overall baseline size is limited. The algorithm is tested on real multi-baseline SAR data acquired over an urban area by an airborne single-pass fourantenna system operating in the millimetre-wave domain. First results show the feasibility of the method both with respect to 3D scatterer reconstruction and 3D SAR focusing.