Either due to the penetration of the transmitted radiation inside the imaged ground objects, or to the slant nature of the imaging geometry, SAR images represents a “projection” of the scene scattering characteristics over the slant-range plane, thus having in each azimuth-range pixel the superposition (in the linear Born approximation) of different scattering mechanisms.

3D SAR Tomography has been recently proposed in literature to allow distinguishing different scattering mechanisms along the third direction (elevation), by using multibaseline acquisitions. Assumption on the absence of any relative motion between the multiple scatterers in the same pixel is done. One of the main difficulties in implementing this technique is related to the fact that acquired data are sampled in space with a non-uniform (baselines) distribution. This fact introduces a high degree of ill conditioning, which may generate severe ambiguities in the elevation focusing. Several techniques have been proposed in order to overcome this problem, fi the interpolated beam-forming method [1]. A new approach, based on the Singular Value Decomposition (SVD) method, widely used in the context of linear inverse problems, has been discussed in [2] and applied to real data in [3]. Beside the robustness with respect to uneven baseline distribution, SVD allows reaching super-resolution by exploiting a priori knowledge on the extension of the scene in the elevation direction (signal support).