Multi-baseline Synthetic Aperture Radar Research Articles

A Novel Phase Compensation Method for Urban 3D Reconstruction Using SAR Tomography

Synthetic aperture radar (SAR) tomography (TomoSAR) has been widely used in the three-dimensional (3D) reconstruction of urban areas using the multi-baseline (MB) SAR data. For urban scenarios, the MB SAR data are often acquired by repeat-pass using the spaceborne SAR system. Such a data stack generally has long time baselines, which result in different atmospheric disturbances of the data acquired by different tracks. These factors can lead to the presence of phase errors (PEs). PEs are multiplicative noise for observation data, which can cause diffusion and defocus in TomoSAR imaging and seriously affect the extraction of target 3D information. In this paper, we combine the methods of the block-building network (BBN) and phase gradient autofocus (PGA) to propose a novel phase compensation method called BBN-PGA. The BBN-PGA method can effectively and efficiently compensate for PEs of the MB SAR data over a wide area and improve the accuracy of 3D reconstruction of urban areas. The applicability of this proposed BBN-PGA method is proved by using simulated data and the spaceborne MB SAR data acquired by the TerraSAR-X satellite over an area in Barcelona, Spain.

An Anti-2D Deceptive Jamming Method for Multi-Baseline Interferometric SAR Based on Co-Localization Jammer

Aimed at the problem that range-azimuth joint jamming can effectively generate high fidelity false targets in synthetic aperture radar (SAR) images, an anti-2D deceptive jamming method for multi-baseline interferometric SAR is proposed in this paper. The method consists of three steps: identification, classification and suppression. Firstly, the target is detected in the sea clutter background using constant false alarm rate algorithm, and then the false target is identified base on the difference of the spatial position of the target in different SAR images. Secondly, through the analysis of the generating principle of deceptive jamming, the false target is classified by multi-baseline interferometric SAR according to partial prior knowledge, which is designed based on the differences between the interferometric phase of the true and false target. Finally, the position of the active jammer is co-located underlying the phase equalization characteristics between multiple baselines and the geometric configuration of the SAR, and the adaptive beamforming method is supplied to suppress the jamming signal. The proposed method has a significantly improved ability to suppress jamming relative to interferometric phase cancellation. The effectiveness of the proposed method is verified by simulation.

Deformation Velocity-Based Regularization of Multibaseline SAR Interferometry

Synthetic aperture radar (SAR) interferometry (InSAR) has shown great potential in the monitoring of Earth's surface and detection of the possible slow temporal deformations. Within the framework of multibaseline SAR interferometry, the availability of multiple interferograms obtained from multipass satellite observations can significantly improve the accuracy of the estimated target parameters, i.e., the residual height and the mean deformation velocity. The parameters can be estimated in the maximum likelihood (ML) sense and through the data covariance matrix. However, the presence of artifact and outliers may impair the parameter estimation, specifically when the candidate cells are subject to temporal decorrelation and atmospheric phase noise effects. In this letter, the exploitation of contextual spatial information is proposed to reduce the possible ambiguity and improve the accuracy of ML-based parameter estimation. The proposed approach adds a regularization term (or a constraint) to the ML's model in order to include the information about the scene velocity variation. Hence, the resulted nonconvex optimization is resolved using the graph-cut concept. The method is evaluated using the simulated and two real data sets acquired by Constellation of Small Satellites for Mediterranean basin Observation (COSMO-SkyMed) and Sentinel-1A sensors over Tehran, Iran; and the results are validated using the global positioning system-based measurements.

Influence analysis of time synchronization error on altimetry accuracy of multibaseline SARs

To verify the necessity of the time synchronization mechanism in a multibaseline synthetic aperture radar (SAR) system and to acquire the allowable magnitude of the residual time synchronization error after compensation, the influence of the time synchronization error on the altimetry accuracy of multibaseline SAR is studied. Based on a time synchronization error model, the influence of the time synchronization error on the interferometric phase and the influence of the interferometric phase error on the altimetry accuracy of the multibaseline SAR are analyzed, and accordingly, an error transfer model from the time synchronization error to the altimetry accuracy of the multibaseline SAR is established. According to the established error transfer model, the necessity of the time synchronization mechanism in the multibaseline SAR system is verified, and a formula of the allowable magnitude of the residual time synchronization error is derived. The formulas of the single-baseline SAR and the multibaseline SAR to evaluate the altimetry accuracy are compared, and the condition that the altimetry accuracy of the multibaseline SAR is higher than that of the single-baseline SAR is clarified. The accuracy of the derived formula is verified by the performed simulation.

Forest height retrieval using P-band airborne multi-baseline SAR data: A novel phase compensation method

Synthetic aperture radar (SAR) tomography (TomoSAR) has been well-established for three-dimensional (3-D) information extraction of forests using the multi-baseline SAR data stacks. The multi-baseline SAR data stacks can be acquired by spaceborne and airborne SAR systems, but for forest scenarios, the data stacks acquired by the airborne SAR system are mostly used. Such a data stack has the advantages of short revisiting time and weak temporal decorrelation. However, due to the baseline errors (caused by the residual platform motion and the measurement errors of the navigation instruments), phase errors (PEs) will occur. PEs are independent of one track to the other, resulting in spreading and defocusing in tomographic imaging. In this paper, we proposed a novel phase compensation method named NC-PGA, which combines the methods of network construction (NC) and phase gradient autofocus (PGA) to estimate and compensate the PEs. The NC method uses the Delaunay triangulation network and beamforming to obtain an accurate elevation estimate of the selected permanent scatterers, which can be used as the prior information for subsequent processing to overcome the shortcomings of the PGA method in PEs estimation. The PGA method uses the spatial invariance of PEs in a limited area to compensate for the PE of each track. The applicability of the NC-PGA method is demonstrated using simulated data and real data. The real data contains two data stacks. The one is acquired by a full-polarization P-band airborne SAR system (developed independently by our project research team) over the study area in Saihanba Forest Farm in Hebei, China. The other one is acquired by ONERA SETHI airborne system over Paracou, French Guiana, in the frame of the European Space Agency’s campaign TropiSAR. We select a test area in the study area and successfully retrieve the height of the forest, and use LiDAR data for results validation and evaluation.

A Modified Capon Method for SAR Tomography Over Forest

The 3-D structure of forests is an important indicator for evaluating forest health and can provide data support for ecological monitoring and protection. Synthetic aperture radar (SAR) tomography (TomoSAR) is an important technology for forest structure estimation using the multibaseline (MB) SAR data stacks. The spectral estimators, such as the Capon method, are usually used to estimate the 3-D reflectivity along elevation direction. In this letter, a modified Capon (M-Capon) method is proposed to improve the estimated profiles of ground and canopy scatterers in the elevation direction, including the estimation accuracy and resolution. This method combines the ideas of the CLEAN algorithm with the Capon method and uses iteration to improve the resolution and accuracy of the estimation. The effectiveness of the M-Capon method is demonstrated using the simulated data and the MB SAR data acquired by the P-band airborne SAR system over the Saihanba Forest Farm in Hebei, China.

Baseline Design for Multibaseline InSAR System: A Review

Multibaseline (MB) synthetic aperture radar (SAR) interferometry (InSAR) is an exciting and growing remote sensing technique, which can improve the traditional InSAR from the ill-posed problem to the well-posed problem. Baseline lengths play an important role in the MB InSAR system design. It is fair to say that the baseline lengths of the MB InSAR system directly determine the quality of the MB InSAR products. In this article, we present an overview of the established MB baseline design criteria and requirements for the MB InSAR digital elevation model (DEM) generation, which includes the effect of the baseline lengths and their estimation errors on ambiguity height, DEM accuracy, decorrelation, and MB phase unwrapping robustness. In addition, we prospectively apply the geometric programming technique to combine those effects together for obtaining the global optimal baseline lengths of the MB InSAR system. It is our hope that this review will provide guidelines to future researchers to enhance further the new airborne and spaceborne MB InSAR system developments.

Extended Phase Unwrapping Max-Flow/Min-Cut Algorithm for Multibaseline SAR Interferograms Using a Two-Stage Programming Approach.

Multi-baseline (MB) phase unwrapping (PU) is a key step of MB synthetic aperture radar (SAR) interferometry (InSAR). Compared with the traditional single-baseline (SB) PU, MB PU is applicable to the area where topography varies violently without obeying the phase continuity assumption. A two-stage programming MB PU approach (TSPA) proposed by H. Yu. builds the link between SB and MB PUs, so many existing classical SB PU methods can be transplanted into the MB domain. In this paper, an extended PU max-flow/min-cut (PUMA) algorithm for MB InSAR using the TSPA, referred to as TSPA-PUMA, is proposed, consisting of a two-stage programming procedure. In stage 1, phase gradients are estimated based on Chinese remainder theorem (CRT). In stage 2, a Markov random field (MRF) model of PUMA is designed for modeling local contextual dependence based on the phase gradients obtained by stage 1. Subsequently, the energy of the MRF model is minimized by graph cuts techniques. The experiment results illustrate that the TSPA-PUMA method can drastically enhance the accuracy of the original PUMA method in the rugged area, and is more efficient than the original TSPA method. In addition, the noise robustness of TSPA-PUMA can be improved through adding more interferograms with different baseline lengths.

Differential SAR Tomography Reconstruction Robust to Temporal Decorrelation Effects

Temporal decorrelation is one of the major problems in synthetic aperture radar (SAR) tomography (TomoSAR) of a natural environment that leads to blurring and spreading in focused image space. In the context of spatiotemporal focusing using the multi-temporal multi-baseline (MB) SAR data, a model-based differential TomoSAR is employed. Along this and with the aim of temporal decorrelation-robust focusing, a differential tomography framework based on generalized Capon estimator is investigated. The method can cope with temporal decorrelation of the distributed environment by spatiotemporal focusing with optimal bandwidth of the distributed signal. In addition, the method employs an additional parameter for coherence channel balancing in the model of generalized Capon that benefits from it in characterizing the spatiotemporal backscattering by mitigating the inconsistency between channels. The analysis is performed with a realistic simulation of temporal decorrelation in the presence of different decorrelation sources and taking into account the dependence on the vertical structure of the forested area. Effectiveness of the proposed framework has been assessed on both simulated and real data sets by evaluation and characterization of the canopy and under foliage ground in terms of deviation between the estimated covariance matrix and one of the generalized TomoSAR models.

Tropical Forest Height Retrieval Based on P-Band Multibaseline SAR Data

In this letter, we present an experimental assessment of vegetation height retrieval in tropical forests based on P-band synthetic aperture radar (SAR) acquisitions. Two approaches are implemented and compared: 1) parametric height estimation by minimizing the least-square problem between random volume over ground (RVoG) model predictions and multibaseline SAR data and 2) thresholding the vertical backscattering profiles that are focused by SAR Beam-forming tomography. The data set under analysis is from the ESA AfriSAR campaign that was flown over Gabon in 2016. Results show that at a resolution of $25~\text{m}\times 25~\text{m}$ , which corresponds to about 80 independent looks, both of the two approaches are able to retrieve forest height to within an accuracy of about 3 m or better over the interval of forest height between 30 and 50 m when compared to Light Detection and Ranging (LiDAR) measurements.

Early Lessons on Combining Lidar and Multi-baseline SAR Measurements for Forest Structure Characterization

The estimation and monitoring of 3D forest structure at large scales strongly rely on the use of remote sensing techniques. Today, two of them are able to provide 3D forest structure estimates: lidar and synthetic aperture radar (SAR) configurations. The differences in wavelength, imaging geometry, and technical implementation make the measurements provided by the two configurations different and, when it comes to the sensitivity to individual 3D forest structure components, complementary. Accordingly, the potential of combining lidar and SAR measurements toward an improved 3D forest structure estimation has been recognised from the very beginning. However, until today there is no established framework for this combination. This paper attempts to review differences, commonalities, and complementarities of lidar and SAR measurements. First, vertical lidar reflectance and SAR reflectivity profiles at different wavelengths are compared in different forest types. Then, current perspectives on their combination for the generation of enhanced structure products are discussed. Two promising frameworks for combining lidar and SAR measurements are reviewed. The first one is a model-based framework where lidar-derived parameters are used to initialize SAR scattering models, and relies on both the validity of the models and on the physical equivalence of the used lidar and SAR parameters. The second one is a structure-based framework based on the ability of lidar and SAR measurements to express physical forest structure by means of appropriate indices. These indices can then be used to establish a link between the two kind of measurements. The review is supported by experimental results achieved using space- and airborne data acquired in recent relevant mission and campaigns.

Regularization of SAR Tomography for 3-D Height Reconstruction in Urban Areas

Height reconstruction in urban areas using multibaseline synthetic aperture radar (SAR) images is still a challenging task. Due to the superimposition of scatterers from different elevations in a same resolution cell, the generation of digital elevation model is not straightforward. Classical SAR interferometry cannot be adopted, while standard SAR tomography (TomoSAR) can fail to well identify and separate the scatterers in case that the relative contribution of a scatterer to the others is insignificant. Moreover, irregular and low number of sampling can increase the ambiguity level and introduce artifacts with strong variance from pixel to pixel. Coping with this problem typically requires large number of datasets with proper sampling. In this paper, contextual information is exploited to reduce the ambiguity and resolve the superimposition of the scatterers regardless of their relative power contributions, even in the case of a limited number of images. In particular, the proposed approach, starting from standard spectral estimators, introduces a regularization term to include the a priori information about scene height variation in the array processing chain. The reconstruction problem is set as an energy minimization problem that is solved using graph-cut-based optimization algorithms, where the solution of localization is the linkage between optimization of signal energy along direction of arrival and controlling height variation within the neighbors of selected pixel. Details of experimental results in the form of tomographic slices as well as three-dimensional point cloud generation, from simulated and real datasets are included to demonstrate the effectiveness of the proposed reconstruction approach.

Multi-baseline extended particle filtering phase unwrapping algorithm based on amended matrix pencil model and quantized path-following strategy

This paper proposes a new multi-baseline extended particle filtering phase unwrapping algorithm which combines an extended particle filter with an amended matrix pencil model and a quantized path-following strategy. The contributions to multi-baseline synthetic aperture radar (SAR) interferometry are as follows: a new recursive multi-baseline phase unwrapping model based on an extended particle filter is built, and the amended matrix pencil model is used to acquire phase gradient information with a higher precision and lower computational cost, and the quantized path-following strategy is introduced to guide the proposed phase unwrapping procedure to efficiently unwrap wrapped phase image along the paths routed by a phase derivative variance map.

Sensitivity of SAR Tomography to the Phenological Cycle of Agricultural Crops at X-, C-, and L-band

Understanding the impact of soil and plant parameter changes in agriculture on Synthetic Aperture Radar (SAR) measurements is of great interest when it comes to monitor the temporal evolution of agricultural crops by means of SAR. In this regard, specific transitions between phenological stages in corn, barley, and wheat have been identified associated to certain dielectric and geometric changes, based on a time series of fully polarimetric multibaseline SAR data and in situ measurements. The data have been acquired in the frame of DLR's CROPEX campaign on six dates between May and July in 2014. The experiments reported in this paper address the sensitivity of X-, C-, and L-band to phenological transitions exploiting the availability of multiple baselines on each acquisition date. The application of tomographic techniques enables the estimation of the three-dimensional (3-D) backscatter distribution and the separation of ground and volume scattering components. Tomographic parameters have been derived at different frequencies, namely the center of mass of the profiles of the total and of the volume-only 3-D backscatter, and the ground and volume powers. Their sensitivity and ability to detect changes occurring on the ground and in the vegetation volume have been evaluated focusing on the added value provided by the 3-D resolution at the different frequencies and polarizations available.

3-D Scattering Characterization of Agricultural Crops at C-Band Using SAR Tomography

The aim of this paper is to interpret and characterize the changes of the 3-D polarimetric scattering signatures of agricultural crops at C-band and to relate them to temporal changes of the soil and plant parameters. For this, a time series of multibaseline (MB) synthetic aperture radar (SAR) data acquired at C-band by the airborne F-SAR system of the German Aerospace Center over the Wallerfing test site in Germany was analyzed. The availability of MB SAR data enables the resolution of scattering contributions in height by means of SAR tomography. The tomographic profiles at different polarizations were analyzed regarding temporal changes for different crop types. First, it was investigated if the center of mass (CoM) of the vertical reflectivity profiles as a single parameter enables the tracking of changes in soil and vegetation. The results show that the vertical reflectivity profiles and their CoM do not allow resolving the ambiguity if a change originates from soil or vegetation dynamics as expected. Thus, the scattering contributions from ground and volume were separated in height, using a filtering approach, and used for the estimation of the ground and volume scattering powers by means of covariance matching. Comparing the outputs with coincident ground measurements showed that dielectric as well as geometric changes in the vegetation are traceable by the separated ground and volume powers. Finally, the estimated powers were analyzed with respect to orientation effects, i.e., to polarimetric anisotropic behavior. They were found to be not significant for the crops under study at C-band.

Multi‐baseline InSAR phase unwrapping method based on mixed‐integer optimisation model

Multi-baseline phase unwrapping operation is a very important step for multi-baseline Synthetic Aperture Radar Interferometry (InSAR). However, the conventional methods applied for single pixel suffer seriously from the phase noise that exists numerously in the interferometric phase images (i.e. interferograms). In order to improve the robustness to noise, this study proposes an innovative multi-baseline InSAR phase unwrapping method based on a mixed-integer optimisation model. The proposed method combines the central and its neighbouring pixels to jointly construct the mixed-integer optimisation model under the assumption that the pixels within a local window can be approximated by a small slant plane terrain. Furthermore, considering the practical case, the optimal window size is estimated according to the deviation from the interferometric wrapped phase to the assumed linear terrain, and the fast Fourier transform technique is adopted to reduce computational cost. The theoretical analysis and computer simulation demonstrate that the proposed method is able to improve the multi-baseline phase unwrapping performance and can be applied to reconstruct the digital elevation models for complicated topographies.

A Unified Algorithm for Channel Imbalance and Antenna Phase Center Position Calibration of a Single-Pass Multi-Baseline TomoSAR System

The multi-baseline synthetic aperture radar (SAR) tomography (TomoSAR) system is employed in such applications as disaster remote sensing, urban 3-D reconstruction, and forest carbon storage estimation. This is because of its 3-D imaging capability in a single-pass platform. However, a high 3-D resolution of TomoSAR is based on the premise that the channel imbalance and antenna phase center (APC) position are precisely known. If this is not the case, the 3-D resolution performance will be seriously degraded. In this paper, a unified algorithm for channel imbalance and APC position calibration of a single-pass multi-baseline TomoSAR system is proposed. Based on the maximum likelihood method, as well as the least squares and the damped Newton method, we can calibrate the channel imbalance and APC position. The algorithm is suitable for near-field conditions, and no phase unwrapping operation is required. The effectiveness of the proposed algorithm has been verified by simulation and experimental results.

Model-Based Target Scattering Decomposition of Polarimetric SAR Tomography

When dealing with forest scenario, target scattering separation using synthetic aperture radar (SAR) tomography is a challenging task for the application of biophysical parameter retrieval approaches. One important and widely popular solution used to investigate the scattering mechanism separation based on multipolarimetric multibaseline (MPMB) SAR data is the sum of Kronecker products (SKPs), which provides the basis for decomposition of the data into ground-only and canopy-only contributions. In this paper, we investigate the possibility of characterizing multiple scattering mechanisms using the SKPs of covariance matrix. In particular, we present a method for characterization of forest structure using MPMB data that adapt SKP with the generalized volume description and the physical model of interferometric cross correlation as the sum of scattering contributions. According to the Freeman–Durden model, the method expresses the estimated covariance matrix in terms of the Kronecker product of polarimetric and interferometric coherence matrices corresponding to direct, double-bounce, and random-volume scattering mechanisms. The proposed method is tested with simulated and P-band MB data acquired by ONERA over a tropical forest in French Guiana in the frame of the European Space Agency’s campaign TROPISAR. Comparison of the retrieved height of trees with a LiDAR-based canopy model as a reference showed that the proposed method has the potential to decrease root-mean-square error of forest height by up to 3.9 m with respect to SKP.

Urban Area Tomography Using a Sparse Representation Based Two-Dimensional Spectral Analysis Technique

Synthetic aperture radar (SAR) tomography (TomoSAR) estimates scene reflectivity along elevation coordinates, based on multi-baseline measurements. Common TomoSAR approaches are based on every single range-azimuth cell or the cell’s neighborhood. By using an additional synthetic aperture for elevation, these techniques have higher resolution power for elevation, to discriminate scatterers with differences in location in the same range-azimuth cell. However, they cannot provide sufficient range resolution power to discriminate these scatterers by joining different spectra to a wider range spectrum, which limits the resolution power of TomoSAR. Therefore, in this paper, we proposed using a compressive sensing (CS) technique to reconstruct range and elevation signals from multi-baseline SAR images. This TomoSAR method not only retrieves the vertical signals but also improves the resolution in the range, which helps improve the resolution power of the TomoSAR technique. We present the theory of the CS-based two-dimensional TomoSAR technique and compare it to the CS-based one-dimensional TomoSAR technique. The excellent resolution power and scatterer localization accuracy of this novel technique are demonstrated using simulations and real data obtained by RADARSAT-2 in Lanzhou, China.

Three‐dimensional SAR imaging of sea targets with low PRF sampling

Three-dimensional (3D) synthetic aperture radar (SAR) image formation provides the scene reflectivity estimation along azimuth, range, and elevation coordinates. A common 3D SAR focusing approach is compressed sensing (CS)-based SAR tomography, but this technique brings image quality problems because of the undesired side-lobes in the focused two-dimensional (2D) images. Moreover, the amount of raw data, which is used for 3D imaging, is still very large. To reduce the amount of raw data and achieve satisfying 3D resolving ability, a novel 3D imaging method based on multidimensional CS (MCS) is proposed for multi-baseline SAR. In this study, the multi-baseline SAR 3D raw signals are presented in space-time domain; and the proposed 3D imaging algorithm based on MCS is used to reduce the amount of raw data. The range, azimuth and elevation profiles can be reconstructed with an extremely low Pulse Recurrence Frequency (PRF) with this algorithm. Comparisons with the existing CS-tomographic focusing method are also presented. Experimental results demonstrate that the proposed algorithm can efficiently solve the 3D imaging task with limited PRF sampling.