Height estimation of scatterers in complex environments via the Tomographic Synthetic Aperture Radar (TomoSAR) technique is still a valuable research field. The parametric spectral estimation approach constitutes a powerful tool to identify the superimposed scatterers with different complex reflectivities, located at different heights in the same range–azimuth resolution cell. Unfortunately, this approach requires prior knowledge about the number of scatterers for each pixel, which is not possible in practical situations. In this paper, we propose a method that analyzes the scree plot, generated from the spectral decomposition of the multidimensional covariance matrix, in order to estimate automatically the number of scatterers for each resolution cell. In this context, a properly improved regularization step is included during the reconstruction process, transforming the parametric MUSIC estimator into a non-parametric method. The experimental results on two data sets covering high elevation towers, with different facade coating characteristics, acquired by the TerraSAR-X satellite highlighted the effectiveness of the proposed regularized MUSIC for the reconstruction of high man-made structures compared with classical approaches.
The progressive development of Synthetic Aperture Radar (SAR) systems diversify the exploitation of the generated images by these systems in different applications of geoscience. Detection and monitoring surface deformations, procreated by various phenomena had benefited from this evolution and had been realized by interferometry (InSAR) and differential interferometry (DInSAR) techniques. Nevertheless, spatial and temporal decorrelations of the interferometric couples used, limit strongly the precision of analysis results by these techniques. In this context, we propose, in this work, a methodological approach of surface deformation detection and analysis by differential interferograms to show the limits of this technique according to noise quality and level. The detectability model is generated from the deformation signatures, by simulating a linear fault merged to the images couples of ERS1 / ERS2 sensors acquired in a region of the Algerian south.
Given its efficiency and its robustness in separating the different scatterers present in the same resolution cell, SAR tomography (TomoSAR) has become an important tool for the reflectivity reconstruction of the observed complex structures scenes by exploiting multi-dimensional data. By its principle, TomoSAR reduces geometric distortions especially the layover phenomenon in radar scenes, and thus reconstruct the 3D profile of each azimuth-range pixel. In this paper, we present the results and the comparative study of six tomographic reconstruction methods that we have implemented. The analysis is performed with respect to the separability and location of scatterers by each method, supplemented by the proposal of a quantitative analysis using metrics (accuracy and completeness) to evaluate the robustness of each method. The tests were applied on simulated data with TerraSAR-X sensor parameters.
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