220 research outputs found
Urban Deformation Monitoring using Persistent Scatterer Interferometry and SAR tomography
This book focuses on remote sensing for urban deformation monitoring. In particular, it highlights how deformation monitoring in urban areas can be carried out using Persistent Scatterer Interferometry (PSI) and Synthetic Aperture Radar (SAR) Tomography (TomoSAR). Several contributions show the capabilities of Interferometric SAR (InSAR) and PSI techniques for urban deformation monitoring. Some of them show the advantages of TomoSAR in un-mixing multiple scatterers for urban mapping and monitoring. This book is dedicated to the technical and scientific community interested in urban applications. It is useful for choosing the appropriate technique and gaining an assessment of the expected performance. The book will also be useful to researchers, as it provides information on the state-of-the-art and new trends in this fiel
Polarization Optimization for the Detection of Multiple Persistent Scatterers Using SAR Tomography
The detection of multiple interfering persistent scatterers (PSs) using Synthetic Aperture Radar (SAR) tomography is an efficient tool for generating point clouds of urban areas. In this context, detection methods based upon the polarization information of SAR data are effective at increasing the number of PSs and producing high-density point clouds. This paper presents a comparative study on the effects of the polarization design of a radar antenna on further improving the probability of detecting persistent scatterers. For this purpose, we introduce an extension of the existing scattering property-based generalized likelihood ratio test (GLRT) with realistic dependence on the transmitted/received polarizations. The test is based upon polarization basis optimization by synthesizing all possible polarimetric responses of a given scatterer from its measurements on a linear orthonormal basis. Experiments on both simulated and real data show, by means of objective metrics (probability of detection, false alarm rate, and signal-to-noise ratio), that polarization waveform optimization can provide a significant performance gain in the detection of multiple scatterers compared to the existing full-polarization-based detection method. In particular, the increased density of detected PSs at the studied test sites demonstrates the main contribution of the proposed method
Remote Monitoring of Civil Infrastructure Based on TomoSAR
Structural health monitoring and damage detection tools are extremely important topics nowadays with the civil infrastructure aging and deteriorating problems observed in urban areas. These tasks can be done by visual inspection and by using traditional in situ methods, such as leveling or using traditional mechanical and electrical sensors, but these approaches are costly, labor-intensive and cannot be performed with a high temporal frequency. In recent years, remote sensing has proved to be a very promising methodology in evaluating the health of a structure by assessing its deformation and thermal dilation. The satellite-based Synthetic Aperture Radar Tomography (TomoSAR) technique, based on the exploitation of a stack of multi-temporal SAR images, allows to remotely sense the movement and the thermal dilation of individual structures with a centimeter-to millimeter-level accuracy, thanks to new generation high-resolution satellite-borne sensors. In this paper, the effectiveness of a recently developed TomoSAR technique in assessing both possible deformations and the thermal dilation evolution of man-made structures is shown. The results obtained using X-band SAR data in two case studies, concerning two urban structures in the city of Naples (Italy), are presented
A Sequential MUSIC algorithm for Scatterers Detection 2 in SAR Tomography Enhanced by a Robust Covariance 3 Estimator
Synthetic aperture radar (SAR) tomography (TomoSAR) is an appealing tool for
the extraction of height information of urban infrastructures. Due to the
widespread applications of the MUSIC algorithm in source localization, it is a
suitable solution in TomoSAR when multiple snapshots (looks) are available.
While the classical MUSIC algorithm aims to estimate the whole reflectivity
profile of scatterers, sequential MUSIC algorithms are suited for the detection
of sparse point-like scatterers. In this class of methods, successive
cancellation is performed through orthogonal complement projections on the
MUSIC power spectrum. In this work, a new sequential MUSIC algorithm named
recursive covariance canceled MUSIC (RCC-MUSIC), is proposed. This method
brings higher accuracy in comparison with the previous sequential methods at
the cost of a negligible increase in computational cost. Furthermore, to
improve the performance of RCC-MUSIC, it is combined with the recent method of
covariance matrix estimation called correlation subspace. Utilizing the
correlation subspace method results in a denoised covariance matrix which in
turn, increases the accuracy of subspace-based methods. Several numerical
examples are presented to compare the performance of the proposed method with
the relevant state-of-the-art methods. As a subspace method, simulation results
demonstrate the efficiency of the proposed method in terms of estimation
accuracy and computational load
SAR Tomography via Nonlinear Blind Scatterer Separation
Layover separation has been fundamental to many synthetic aperture radar
applications, such as building reconstruction and biomass estimation.
Retrieving the scattering profile along the mixed dimension (elevation) is
typically solved by inversion of the SAR imaging model, a process known as SAR
tomography. This paper proposes a nonlinear blind scatterer separation method
to retrieve the phase centers of the layovered scatterers, avoiding the
computationally expensive tomographic inversion. We demonstrate that
conventional linear separation methods, e.g., principle component analysis
(PCA), can only partially separate the scatterers under good conditions. These
methods produce systematic phase bias in the retrieved scatterers due to the
nonorthogonality of the scatterers' steering vectors, especially when the
intensities of the sources are similar or the number of images is low. The
proposed method artificially increases the dimensionality of the data using
kernel PCA, hence mitigating the aforementioned limitations. In the processing,
the proposed method sequentially deflates the covariance matrix using the
estimate of the brightest scatterer from kernel PCA. Simulations demonstrate
the superior performance of the proposed method over conventional PCA-based
methods in various respects. Experiments using TerraSAR-X data show an
improvement in height reconstruction accuracy by a factor of one to three,
depending on the used number of looks.Comment: This work has been accepted by IEEE TGRS for publicatio
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
-Net: Superresolving SAR Tomographic Inversion via Deep Learning
Synthetic aperture radar tomography (TomoSAR) has been extensively employed
in 3-D reconstruction in dense urban areas using high-resolution SAR
acquisitions. Compressive sensing (CS)-based algorithms are generally
considered as the state of the art in super-resolving TomoSAR, in particular in
the single look case. This superior performance comes at the cost of extra
computational burdens, because of the sparse reconstruction, which cannot be
solved analytically and we need to employ computationally expensive iterative
solvers. In this paper, we propose a novel deep learning-based super-resolving
TomoSAR inversion approach, -Net, to tackle this
challenge. -Net adopts advanced complex-valued learned
iterative shrinkage thresholding algorithm (CV-LISTA) to mimic the iterative
optimization step in sparse reconstruction. Simulations show the height
estimate from a well-trained -Net approaches the
Cram\'er-Rao lower bound while improving the computational efficiency by 1 to 2
orders of magnitude comparing to the first-order CS-based methods. It also
shows no degradation in the super-resolution power comparing to the
state-of-the-art second-order TomoSAR solvers, which are much more
computationally expensive than the first-order methods. Specifically,
-Net reaches more than detection rate in moderate
super-resolving cases at 25 measurements at 6dB SNR. Moreover, simulation at
limited baselines demonstrates that the proposed algorithm outperforms the
second-order CS-based method by a fair margin. Test on real TerraSAR-X data
with just 6 interferograms also shows high-quality 3-D reconstruction with
high-density detected double scatterers
Multiresolution Detection of Persistent Scatterers: A Performance Comparison Between Multilook GLRT and CAESAR
Persistent scatterers (PS) interferometry tools are extensively used for the monitoring of slow, long-term ground deformation. High spatial resolution is typically required in urban areas to cope with the variability of the signal, whereas in rural regions, multilook shall be implemented to improve the coverage of monitored areas. Along this line, SqueeSAR and later Component extrAction and sElection SAR (CAESAR) were introduced for the monitoring of both persistent and (decorrelating) distributed scatterers (DS). Multilook generalized likelihood ratio test (MGLRT) is a detector derived in the context of tomographic SAR processing that has been investigated for a fixed multilook degree. In this work, we address MGLRT and CAESAR in the multiresolution context characterized by a spatially variable multilook degree. We compare the two schemes for the multiresolution selection of PS and DS, highlighting the pros and cons of each scheme, particularly the peculiarities of CAESAR that have important implications at the implementation stage. A performance analysis of both detectors in case of model mismatch is also addressed. Experiments carried out with data acquired by the COSMO-SkyMed constellation support both the theoretical argumentation and the results achieved by resorting to Monte Carlo simulations
A general framework and related procedures for multiscale analyses of DInSAR data in subsiding urban areas
In the last decade Differential Synthetic Aperture Radar (DInSAR) data were successfully tested in a number of case studies for the detection, mapping and monitoring of ground displacements associated with natural or anthropogenic phenomena. More recently, several national and regional projects all around the
world provided rich data archives whose confident use, however, should rely on multidisciplinary experts in order to avoid misleading interpretations. To this aim, the present work first introduces a general framework for the use of DInSAR data; then, focusing on the analysis of subsidence phenomena and the related consequences to the exposed facilities, a set of original procedures is proposed. By drawing a multiscale approach the study highlights the different goals to be pursued at different scales of analysis via high/very high resolution SAR sensors and presents the results with reference to the case study of the
Campania region (southern Italy) where widespread ground displacements occurred and damages of different severity were recorded
Study of the speckle noise effects over the eigen decomposition of polarimetric SAR data: a review
This paper is focused on considering the effects of
speckle noise on the eigen decomposition of the co-
herency matrix. Based on a perturbation analysis of the
matrix, it is possible to obtain an analytical expression for
the mean value of the eigenvalues and the eigenvectors,
as well as for the Entropy, the Anisotroopy and the dif-
ferent a angles. The analytical expressions are compared
against simulated polarimetric SAR data, demonstrating
the correctness of the different expressions.Peer ReviewedPostprint (published version
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