Non-Local Compressive Sensing Based SAR Tomography

Tomographic SAR (TomoSAR) inversion of urban areas is an inherently sparse reconstruction problem and, hence, can be solved using compressive sensing (CS) algorithms. This paper proposes solutions for two notorious problems in this field: 1) TomoSAR requires a high number of data sets, which makes the technique expensive. However, it can be shown that the number of acquisitions and the signal-to-noise ratio (SNR) can be traded off against each other, because it is asymptotically only the product of the number of acquisitions and SNR that determines the reconstruction quality. We propose to increase SNR by integrating non-local estimation into the inversion and show that a reasonable reconstruction of buildings from only seven interferograms is feasible. 2) CS-based inversion is computationally expensive and therefore barely suitable for large-scale applications. We introduce a new fast and accurate algorithm for solving the non-local L1-L2-minimization problem, central to CS-based reconstruction algorithms. The applicability of the algorithm is demonstrated using simulated data and TerraSAR-X high-resolution spotlight images over an area in Munich, Germany.Comment: 10 page

A fast and accurate basis pursuit denoising algorithm with application to super-resolving tomographic SAR

$L_1$ regularization is used for finding sparse solutions to an underdetermined linear system. As sparse signals are widely expected in remote sensing, this type of regularization scheme and its extensions have been widely employed in many remote sensing problems, such as image fusion, target detection, image super-resolution, and others and have led to promising results. However, solving such sparse reconstruction problems is computationally expensive and has limitations in its practical use. In this paper, we proposed a novel efficient algorithm for solving the complex-valued $L_1$ regularized least squares problem. Taking the high-dimensional tomographic synthetic aperture radar (TomoSAR) as a practical example, we carried out extensive experiments, both with simulation data and real data, to demonstrate that the proposed approach can retain the accuracy of second order methods while dramatically speeding up the processing by one or two orders. Although we have chosen TomoSAR as the example, the proposed method can be generally applied to any spectral estimation problems.Comment: 11 pages, IEEE Transactions on Geoscience and Remote Sensin

Building profile reconstruction using TerraSAR-X data time-series and tomographic techniques

This work aims to show the potentialities of SAR Tomography (TomoSAR) techniques for the 3-D characterization (height, reflectivity, time stability) of built-up areas using data acquired by the satellite sensor TerraSAR-X. For this purpose 19 TerraSAR-X single-polarimetric multibaseline images acquired over Paris urban area have been processed applying classical nonparametric (Beamforming and Capon) and parametric (MUSIC) spectral estimation techniques

Single-look light-burden superresolution differential SAR tomography

Research and application is spreading of techniques of coherent combination of complex-valued synthetic aperture radar (SAR) data to extract rich information even on complex observed scenes, fully exploiting existing SAR data archives, and new satellites. Among such techniques, SAR tomography stems from multibaseline interferometry to achieve full-3D imaging through elevation beamforming (spatial spectral estimation). The Tomo concept has been integrated with the mature differential interferometry, producing the new differential tomography (Diff-Tomo) processing mode, that allows `opening' the SAR cells in complex non-stationary scenes, resolving multiple heights and slow deformation velocities of layover scatterers. Consequently, the operational capability limit of differential interferometry to the single scatterer case is overcome. Diff-Tomo processing is cast in a 2D baseline-time spectral analysis framework, with sparse sampling. The use of adaptive 2D spectral estimation has demonstrated to allow joint baseline-time processing with reduced sidelobes and enhanced height-velocity resolution at low computational burden. However, this method requires coherent multilooking processing, thus does not produce full range-azimuth resolution products, as it would be desirable for urban applications. A new single-look adaptive Diff-Tomo processor is presented and tested with satellite data, allowing full range-azimuth resolution together with height-velocity sidelobe reduction and superresolution capabilities and the low computational burden

Support Detection for SAR Tomographic Reconstructions from Compressive Measurements

The problem of detecting and locating multiple scatterers in multibaseline Synthetic Aperture Radar (SAR) tomography, starting from compressive measurements and applying support detection techniques, is addressed. Different approaches based on the detection of the support set of the unknown sparse vector, that is, of the position of the nonzero elements in the unknown sparse vector, are analyzed. Support detection techniques have already proved to allow a reduction in the number of measurements required for obtaining a reliable solution. In this paper, a support detection method, based on a Generalized Likelihood Ratio Test (Sup-GLRT), is proposed and compared with the SequOMP method, in terms of probability of detection achievable with a given probability of false alarm and for different numbers of measurements

Very High Resolution Tomographic SAR Inversion for Urban Infrastructure Monitoring — A Sparse and Nonlinear Tour

The topic of this thesis is very high resolution (VHR) tomographic SAR inversion for urban infrastructure monitoring. To this end, SAR tomography and differential SAR tomography are demonstrated using TerraSAR-X spotlight data for providing 3-D and 4-D (spatial-temporal) maps of an entire high rise city area including layover separation and estimation of deformation of the buildings. A compressive sensing based estimator (SL1MMER) tailored to VHR SAR data is developed for tomographic SAR inversion by exploiting the sparsity of the signal. A systematic performance assessment of the algorithm is performed regarding elevation estimation accuracy, super-resolution and robustness. A generalized time warp method is proposed which enables differential SAR tomography to estimate multi-component nonlinear motion. All developed methods are validated with both simulated and extensive processing of large volumes of real data from TerraSAR-X

Generation of large scale 3-D city models using InSAR and optical data

Interferometric synthetic aperture radar (InSAR) techniquesare powerful tool for reconstructing the 3-D position of scat-terers, especially for the urban areas. Since the estimationaccuracy depends on the inverse of number of interferogramsand signal-to-noise ratio (SNR), it is necessary to use as manyas possible interferograms in order to achieve more accurateresult. However, the number of interferograms of TanDEM-Xdata is generally limited for most areas. Therefore, in orderto maintain the estimation accuracy, one feasible way is toincrease the SNR. In this work, we proposed a novel frame-work, which integrates the non-local procedure into SARtomography inversion and combines the robust estimation.A large-scale demonstration has been carried out with fiveTanDEM-X bistatic data, which covers the entire city of Mu-nich, Germany. Quantitative evaluation of the reconstructedresult with the LiDAR reference exhibits the standard devi-ation of the height difference is within two meters, whichimplies the proposed framework has great potential for highquality large-scale 3-D urban modeling

A sparsity-driven approach for joint SAR imaging and phase error correction

Image formation algorithms in a variety of applications have explicit or implicit dependence on a mathematical model of the observation process. Inaccuracies in the observation model may cause various degradations and artifacts in the reconstructed images. The application of interest in this paper is synthetic aperture radar (SAR) imaging, which particularly suffers from motion-induced model errors. These types of errors result in phase errors in SAR data which cause defocusing of the reconstructed images. Particularly focusing on imaging of fields that admit a sparse representation, we propose a sparsity-driven method for joint SAR imaging and phase error correction. Phase error correction is performed during the image formation process. The problem is set up as an optimization problem in a nonquadratic regularization-based framework. The method involves an iterative algorithm each iteration of which consists of consecutive steps of image formation and model error correction. Experimental results show the effectiveness of the approach for various types of phase errors, as well as the improvements it provides over existing techniques for model error compensation in SAR