80 research outputs found
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
Multiplicative Noise Removal: Nonlocal Low-Rank Model and Its Proximal Alternating Reweighted Minimization Algorithm
The goal of this paper is to develop a novel numerical method for efficient
multiplicative noise removal. The nonlocal self-similarity of natural images
implies that the matrices formed by their nonlocal similar patches are
low-rank. By exploiting this low-rank prior with application to multiplicative
noise removal, we propose a nonlocal low-rank model for this task and develop a
proximal alternating reweighted minimization (PARM) algorithm to solve the
optimization problem resulting from the model. Specifically, we utilize a
generalized nonconvex surrogate of the rank function to regularize the patch
matrices and develop a new nonlocal low-rank model, which is a nonconvex
nonsmooth optimization problem having a patchwise data fidelity and a
generalized nonlocal low-rank regularization term. To solve this optimization
problem, we propose the PARM algorithm, which has a proximal alternating scheme
with a reweighted approximation of its subproblem. A theoretical analysis of
the proposed PARM algorithm is conducted to guarantee its global convergence to
a critical point. Numerical experiments demonstrate that the proposed method
for multiplicative noise removal significantly outperforms existing methods
such as the benchmark SAR-BM3D method in terms of the visual quality of the
denoised images, and the PSNR (the peak-signal-to-noise ratio) and SSIM (the
structural similarity index measure) values
Approches tomographiques structurelles pour l'analyse du milieu urbain par tomographie SAR THR : TomoSAR
SAR tomography consists in exploiting multiple images from the same area acquired from a slightly different angle to retrieve the 3-D distribution of the complex reflectivity on the ground. As the transmitted waves are coherent, the desired spatial information (along with the vertical axis) is coded in the phase of the pixels. Many methods have been proposed to retrieve this information in the past years. However, the natural redundancies of the scene are generally not exploited to improve the tomographic estimation step. This Ph.D. presents new approaches to regularize the estimated reflectivity density obtained through SAR tomography by exploiting the urban geometrical structures.La tomographie SAR exploite plusieurs acquisitions d'une mĂȘme zone acquises d'un point de vue lĂ©gerement diffĂ©rent pour reconstruire la densitĂ© complexe de rĂ©flectivitĂ© au sol. Cette technique d'imagerie s'appuyant sur l'Ă©mission et la rĂ©ception d'ondes Ă©lectromagnĂ©tiques cohĂ©rentes, les donnĂ©es analysĂ©es sont complexes et l'information spatiale manquante (selon la verticale) est codĂ©e dans la phase. De nombreuse mĂ©thodes ont pu ĂȘtre proposĂ©es pour retrouver cette information. L'utilisation des redondances naturelles Ă certains milieux n'est toutefois gĂ©nĂ©ralement pas exploitĂ©e pour amĂ©liorer l'estimation tomographique. Cette thĂšse propose d'utiliser l'information structurelle propre aux structures urbaines pour rĂ©gulariser les densitĂ©s de rĂ©flecteurs obtenues par cette technique
Multiplicative Noise Removal: Nonlocal Low-Rank Model and It\u27s Proximal Alternating Reweighted Minimization Algorithm
The goal of this paper is to develop a novel numerical method for efficient multiplicative noise removal. The nonlocal self-similarity of natural images implies that the matrices formed by their nonlocal similar patches are low-rank. By exploiting this low-rank prior with application to multiplicative noise removal, we propose a nonlocal low-rank model for this task and develop a proximal alternating reweighted minimization (PARM) algorithm to solve the optimization problem resulting from the model. Specifically, we utilize a generalized nonconvex surrogate of the rank function to regularize the patch matrices and develop a new nonlocal low-rank model, which is a nonconvex non-smooth optimization problem having a patchwise data fidelity and a generalized nonlocal low-rank regularization term. To solve this optimization problem, we propose the PARM algorithm, which has a proximal alternating scheme with a reweighted approximation of its subproblem. A theoretical analysis of the proposed PARM algorithm is conducted to guarantee its global convergence to a critical point. Numerical experiments demonstrate that the proposed method for multiplicative noise removal significantly outperforms existing methods, such as the benchmark SAR-BM3D method, in terms of the visual quality of the denoised images, and of the peak-signal-to-noise ratio (PSNR) and the structural similarity index measure (SSIM) values
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
Single-Look SAR Tomography of Urban Areas
Synthetic aperture radar (SAR) tomography (TomoSAR) is a multibaseline interferometric technique that estimates the power spectrum pattern (PSP) along the perpendicular to the line-ofsight (PLOS) direction. TomoSAR achieves the separation of individual scatterers in layover areas,
allowing for the 3D representation of urban zones. These scenes are typically characterized by buildings of different heights, with layover between the facades of the higher structures, the rooftop of the smaller edifices and the ground surface. Multilooking, as required by most spectral estimation techniques, reduces the azimuth-range spatial resolution, since it is accomplished through the averaging of adjacent values, e.g., via Boxcar filtering. Consequently, with the aim of avoiding the spatial mixture of sources due to multilooking, this article proposes a novel methodology to perform single-look TomoSAR over urban areas. First, a robust version of Capon is applied to focus the TomoSAR data, being robust against the rank-deficiencies of the data covariance matrices. Afterward, the recovered PSP is refined using statistical regularization, attaining resolution enhancement, suppression of artifacts and reduction of the ambiguity levels. The capabilities of the proposed methodology are demonstrated by means of strip-map airborne data of the Jet Propulsion
Laboratory (JPL) and the National Aeronautics and Space Administration (NASA), acquired by the uninhabited aerial vehicle SAR (UAVSAR) system over the urban area of Munich, Germany in 2015. Making use of multipolarization data [horizontal/horizontal (HH), horizontal/vertical (HV) and vertical/vertical (VV)], a comparative analysis against popular focusing techniques for urban monitoring (i.e., matched filtering, Capon and compressive sensing (CS)) is addressed
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