Three-dimensional synthetic aperture radar (SAR) image formation provides the scene reflectivity estimation along azimuth, range, and elevation coordinates. It is based on multipass SAR data obtained usually by nonuniformly spaced acquisition orbits. A common 3-D SAR focusing approach is Fourier-based SAR tomography, but this technique brings about image quality problems because of the low number of acquisitions and their not regular spacing. Moreover, attained resolution in elevation is limited by the overall acquisitions baseline extent. In this paper, a novel 3-D SAR data imaging based on Compressive Sampling theory is presented. It is shown that since the image to be focused has usually a sparse representation along the elevation direction (i.e., only few scatterers with different elevation are present in the same rangeazimuth resolution cell), it suffices to have a small number of measurements to construct the 3-D image. Furthermore, the method allows super-resolution imaging, overcoming the limitation imposed by the overall baseline span. Tomographic imaging is performed by solving an optimization problem which enforces sparsity through ℓ1;-norm minimization. Numerical results on simulated and real data validate the method and have been compared with the truncated singular value decomposition technique
In this paper, a technique that is able to reconstruct highly sloped and discontinuous terrain height profiles, starting from multifrequency wrapped phase acquired by interferometric synthetic aperture radar (SAR) systems, is presented. We propose an innovative unwrapping method, based on a maximum likelihood estimation technique, which uses multifrequency independent phase data, obtained by filtering the interferometric SAR raw data pair through nonoverlapping band-pass filters, and approximating the unknown surface by means of local planes. Since the method does not exploit the phase gradient, it assures the uniqueness of the solution, even in the case of highly sloped or piecewise continuous elevation patterns with strong discontinuities.
In this paper, a generalized likelihood ratio test (GLRT) detector, based on support estimation (Sup-GLRT), for multiple scatterers detection in SAR Tomography, is proposed. It incorporates, in the statistical model, a sparsity assumption of the signal in the elevation direction, which is always verified in practical cases for scarcely vegetated areas. The test consists of sequential steps: first the presence of scatterers is detected; then, the determination of the number of scatterers and the estimation of their positons is performed sequentially, one by one, by means of a signal support detection-estimation operation. The test proposed follows an approach similar to SGLRT, where decisions are taken based on subspace energy measurements, but it is derived following a different testing order and is investigated both at the nominal system resolution and in the super-resolution cases, showing in the latter case, a detection gain with respect to SGLRT. Sup-GLRT performance is evaluated in terms of receiver operating characteristic (ROC) curves for different signal-to-noise ratio values. Experimental results obtained on real COSMO-SkyMed data are shown
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