Multipass Synthetic Aperture Radar Research Articles

Performance Improvement for SAR Tomography Based on Local Plane Model

Multilook approaches have been applied in synthetic aperture radar (SAR) tomography (TomoSAR), for improving the density and regularity of persistent scatterers reconstructed from multipass SAR images in both rural and urban regions. Multilook operations assume that all scatterers in a given neighborhood are similar in height, thereby providing additional data for recovering the position and reflectivity of a single scatterer, so that a higher signal-to-noise ratio can be achieved. This is equivalent to assuming that scatterers belonging to a local neighborhood of range–azimuth cells are located on horizontal planes. The present article generalizes this approach by adopting the so-called local plane (LP) model for TomoSAR imaging in urban areas, accounting for local variations in the height of scatterers that are not negligible. Furthermore, an LP-generalized likelihood ratio test (LP-GLRT) algorithm is developed to implement the previous idea. Compared with the multilook generalized likelihood ratio test algorithm, LP-GLRT shows better performance in the case of urban structures and terrains in experiments based on both simulated data and TerraSAR-X images.

Robust Object-Based Multipass InSAR Deformation Reconstruction

Deformation monitoring by multipass synthetic aperture radar (SAR) interferometry (InSAR) is, so far, the only imaging-based method to assess millimeter-level deformation over large areas from space. Past research mostly focused on the optimal retrieval of deformation parameters on the basis of a single pixel or a pixel cluster. Only until recently, the first demonstration of object-based urban infrastructure monitoring by fusing InSAR and the semantic classification labels derived from optical images was presented by Wang et al. Given such classification labels in the SAR image, we propose a general framework for object-based InSAR parameter retrieval, where the parameters of the whole object are jointly estimated by the inversion of a regularized tensor model instead of pixelwise. Our approach does not assume the stationarity of each sample in the object, which is usually assumed in other pixel cluster-based methods, such as SqueeSAR. In addition, to handle outliers in real data, a robust phase recovery step prior to parameter retrieval is also introduced. In typical settings, the proposed method outperforms the current pixelwise estimators, e.g., periodogram, by a factor of several tens in the accuracy of the linear deformation estimates. Last but not least, for a practical demonstration on bridge monitoring, we present a full workflow of long-term bridge monitoring using the proposed approach.

Moving target inference with bayesian models in SAR imagery

This work combines the physical, kinematic, and statistical properties of targets, clutter, and sensor calibration as manifested in multi-channel SAR imagery into a unified Bayesian structure that simultaneously estimates (a) clutter distributions and nuisance parameters and (b) target signatures required for detection/inference. A Monte Carlo estimate of the posterior distribution is provided that infers the model parameters directly from the data with little tuning of algorithm parameters. Per- formance is demonstrated on both measured/synthetic wide-area datasets. Index Terms—synthetic aperture radar, moving target detec- tion, low-rank, hierarchical Bayesian models I. I NTRODUCTION This work provides an algorithm for inference in multi- antenna and multi-pass synthetic aperture radar (SAR) im- agery. Inference can mean many different things in this frame- work, including detection of moving targets, estimation of the underlying clutter distribution, estimation of the target radial velocity, and classification of pixels. To this end, the output of the proposed algorithm is an estimated posterior distribution over the variables in our model. This posterior distribution is estimated through Markov Chain Monte Carlo (MCMC) techniques. Subsequently, the inference tasks listed above are performed by appropriately using the posterior distribution. For example, detection can be done by thresholding the posterior probability that a target exists at any given location. Recently, there has been great interest by Wright et al. (1), Lin et al. (2), Candes et al. (3) and Ding et al. (4) in the so- called robust principal component analysis (RPCA) problem that decomposes high-dimensional signals as

Interrupted SAR persistent surveillance via group sparse reconstruction of multipass data

In this paper we present a new method for synthetic aperture radar (SAR) image formation from interrupted, multipass SAR phase history data, with application to persistent surveillance SAR imaging. We propose a new compressed sensing-motivated approach to reconstruction that jointly processes multipass interrupted data using a sparse recovery technique with a group support constraint and results in improved imagery. We compare our approach, a group sparsity (GS) algorithm, to methods that independently process each data pass, namely the basis pursuit denoising and iterative adaptive approach methods. We find that the joint processing of GS results in coherent change detection gains over the other approaches regardless of interrupt pattern. To illustrate the capabilities of GS, we evaluate coherent change detection performance using images from the Gotcha SAR.

Nonparametric Change Detection in Multitemporal SAR Images Based on Mean-Shift Clustering

A nonparametric method for unsupervised change detection in multipass synthetic aperture radar (SAR) imagery is described. The method relies on a novel feature capturing the structural change between two SAR images and is robust to the statistical change that may be originated by speckle and coregistration inaccuracies. The proposed method starts from the scatterplot of the amplitude levels in the two images and applies the mean-shift (MS) algorithm to find the modes of the underlying bivariate distribution. If we assume that the two images have been preliminarily coregistered and calibrated on one another, then all the modes lying outside the main diagonal correspond to the structural changes across the two observations. The value of the probability density function (PDF) in any of the off-diagonal modes found by the MS algorithm is translated into a value of conditional information. This value is assigned to all image pixels generating the corresponding cluster in the scatterplot. Thus, a feature is obtained on a per-pixel basis. Experimental results on simulated changes and true SAR images acquired by the COSMO-SkyMed satellite constellation show that the proposed feature exhibits significantly better discrimination capability than the classical log-ratio (LR). Advantages over a preliminary version of the method without MS regularization and over another nonparametric method based on Kullback-Leibler divergence are also demonstrated. The method is robust when it is applied to SAR images with different acquisition angles, whose effects are deemphasized compared to the actual scene changes.

ALGAE: A Fast Algebraic Estimation of Interferogram Phase Offsets in Space-Varying Geometries

This paper deals with the estimation of terrain topography from multipass synthetic aperture radar (SAR) interferometry (InSAR), focusing on the case where variation of the system geometry within the imaged swath is relevant. A typical case is represented by airborne multipass interferometric campaigns where, due to the closeness between the radar sensor and the targets, the incidence-angle sensitivity undergoes a dramatic increase with respect to the spaceborne case, resulting in a high spatial variability of the normal baselines. The space-varying nature of the system geometry gives rise to a major issue in multipass InSAR analyses in that it prevents from compensating for the presence of interferogram phase offsets by simply phase locking the data stack to a reference point, therefore hindering the retrieval of terrain topography. To cope with this issue properly, we propose a novel approach that exploits the algebraic properties of the problem. Such an approach allows casting the problem in terms of identification of a null-space component for terrain topography after which both topography and the interferogram phase offsets are quickly obtained without exploiting calibration points. Experimental results are shown based on a P-band data set acquired by the Experimental SAR (E-SAR) airborne system, operated by the German Aerospace Center (DLR), in the framework of the European Space Agency (ESA) campaign BIOSAR 2008.

Three-Dimensional SAR Focusing From Multipass Signals Using Compressive Sampling

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 range-azimuth 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.

Four-Dimensional SAR Imaging for Height Estimation and Monitoring of Single and Double Scatterers

The superposition of contributions from different stable targets within the same pixel is a phenomenon that may impair the imaging and monitoring of ground scatterers via the multipass synthetic aperture radar (SAR) interferometry technique. Three-dimensional SAR imaging, also known as SAR tomography, uses multiple views to profile the scattering power at different heights. This technique has been shown to be capable of separating interfering target responses on real data. Differential SAR tomography has been recently proposed as a technique that extends the potentialities of SAR tomography to the target deformation monitoring. It performs a 4-D space-velocity imaging that enables not only separating interfering targets in elevation but also distinguishing their single slow deformation velocities. This work addresses for the first time the application of 4-D SAR imaging to real data to determine the height and mean deformation velocity of single scatterers and double-scattering mechanisms interfering at high resolution in the same pixel. It also discusses the postprocessing steps required to identify the presence of stable single and double scatterers after elevation-velocity focusing. Moreover, it proposes a technique for the extraction of time series from interfering targets to measure possible nonlinear temporal deformations.

DEM by Ground-Based SAR Interferometry

In this letter, a ground-based synthetic aperture radar (SAR) interferometer was used to generate digital elevation maps (DEMs) of the illuminated area. With respect to other ground-based data processing techniques, here, the effect of the propagation through the atmosphere is considered. An algorithm similar to multipass satellite SAR techniques was developed in accordance with the phase model used in the ground-based interferometry. Many images taken from different viewing angles were collected and combined to form different interferograms at a test site in Austria. Results from this technique have been compared with an existing geographic model of the test area.

Three-dimensional multipass SAR focusing: experiments with long-term spaceborne data

Synthetic aperture radar (SAR) interferometry is a modern efficient technique that allows reconstructing the height profile of the observed scene. However, apart for the presence of critical nonlinear inversion steps, particularly crucial in abrupt topography scenarios, it does not allow one to separate different scattering mechanisms in the elevation (height) direction within the ground pixel. Overlay of scattering at different elevations in the same azimuth-range resolution cell can be due either to the penetration of the radiation below the surface or to perspective ambiguities caused by the side-looking geometry. Multibaseline three-dimensional (3-D) SAR focusing allows overcoming such a limitation and has thus raised great interest in the recent research. First results with real data have been only obtained in the laboratory and with airborne systems, or with limited time-span and spatial-coverage spaceborne data. This work presents a novel approach for the tomographic processing of European Remote Sensing satellite (ERS) real data for extended scenes and long time span. Besides facing problems common to the airborne case, such as the nonuniformly spaced passes, this processing requires tackling additional difficulties specific to the spaceborne case, in particular a space-varying phase calibration of the data due to atmospheric variations and possible scene deformations occurring for years-long temporal spans. First results are presented that confirm the capability of ERS multipass tomography to resolve multiple targets within the same azimuth-range cell and to map the 3-D scattering properties of the illuminated scene.

Three-dimensional focusing with multipass SAR data

Deals with the use of multipass synthetic aperture radar (SAR) data in order to achieve three-dimensional tomography reconstruction in presence of volumetric scattering. Starting from azimuth- and range-focused SAR data relative to the same area, neglecting any mutual interaction between the targets, and assuming the propagation in homogeneous media, we investigate the possibility to focus the data also in the elevation direction. The problem is formulated in the framework of linear inverse problem and the solution makes use of the singular value decomposition of the relevant operator. This allows us to properly take into account nonuniform orbit separation and to exploit a priori knowledge regarding the size of the volume interested by the scattering mechanism, thus leading to superresolution in the elevation direction. Results obtained on simulated data demonstrate the feasibility of the proposed processing technique.

Comparison of SAR amplitude vs. coherence flood detection methods - a GIS application

Flood area detection from multipass Synthetic Aperture Radar (SAR) data can be performed via amplitude change detection techniques. These methods allow flooded zones to be discriminated only when they are flooded at the time of the second passage, and not at the time of the first one. Coherence derived from multipass SAR interferometry can be used instead, as an indicator of changes in the electromagnetic scattering behaviour of the surface, thus potentially revealing all the areas affected by the flood event at any time between the two passes. The paper presents a prototype application of such techniques, that is, a flood map obtained from ERS-1/2 data taken over Beziers (Southern France), through proper thresholding of a combination of amplitude and coherence information. Produced in the framework of an ESA project, the map consists of a DXF vector file which can be imported directly into most commercial GIS software.