Current synthetic aperture radar (SAR) missions with short repeat times bring the opportunity to get large-scale deformations in near real-time. Since nonstationary deformation is more likely to be a risk, postclassification is widely used to identify the temporal deformation patterns within extensive interferometric SAR (InSAR) data. However, conventional 3-D phase unwrapping (PU) with the assumption of stationary deformation behavior has a high probability of unwrapping error over a long time series, leading to more false detected anomalies. Here, we proposed a recursive 3-D PU method to unwrap the 3-D data stack recursively and detect the deformation anomalies concerning the nonstationary deformation. This method involves recursive temporal PU with a temporal smoothness constraint, followed by iterative spatial PU. Then, the multiple hypothesis test (MHT) is used to determine the optimal deformation model. Scatterers with deformation anomalies are identified using a generalized ratio test and assessed by their detectability power based on the predicted phase residuals. The main advantage of the proposed algorithm is the capability of dynamic data processing and decreasing the false alarm in detected deformation anomalies. The experimental results by both the simulated and real data demonstrate that the proposed method achieves reliable 3-D PU concerning nonstationary deformation, which would be beneficial for near real-time evaluation of deformation risks.

Synthetic aperture radar (SAR) altimeter is able to measure height with high precision, which has been extensively applied to airborne aircrafts for positioning. With the assumption of a flat surface following a Gaussian distribution, the height is obtained by retracking the waveform. However, this assumption often fails in a complex terrain, leading to unpredictable height bias. In this article, a slope-based height correction (SHC) toward robust height inversion in complex terrain is proposed using linear terrain decomposition. Based on the radar propagation equation, the impact of the topography on height inversion is investigated. Then, the response from complex topography can be divided into a determined part related to a set of primary slopes and a stochastic part with certain undulation. Additionally, the height bias induced by the slopes is given based on power constraints. The error bound of the height correction is also derived correspondingly. The main advantage of the proposed method is reliable height correction with the prior digital elevation model (DEM). Experimental results based on both simulated and real data demonstrate a significant decrease in height bias, which greatly extends the application of the altimeter.

Synthetic aperture radar (SAR) is an active microwave remote sensing technology, which is widely used in many fields, such as target recognition and 3-D reconstruction. The new SAR system improves the performance in both accuracy and reliability by incorporating multidimensional measurements. The performance of the new system varies with the applications, which requires an optimal system design to achieve a highly efficient acquisition. However, the optimal design of the current multidimensional SAR (MDSAR) for typical applications is not investigated. In this article, an application-aware inversion error bound is proposed to achieve the optimal design of the MDSAR system. Based on the attributed scattering center (ASC) model, the error bounds of the scattering parameters are obtained with a multidimensional configuration. Then, according to the application scenes, the multidimensional application-aware discrete sampling Cramer-Rao lower bounds (DoubleA-DS-CRLB) are derived to analyze the performance of the typical parameters. The simulation-based experiments demonstrate the optimal MD configurations for target recognition and 3-D reconstruction.

SAR image super resolution algorithms have been shown to significantly improve performance in many applications. For these memory-intensive algorithms, large-scale images must be divided into overlapped blocks and finally concatenated back to the final result. Orthogonal subspace decomposition method has better performance, but needs to manually choose the size of noise subspace according to the noise levels. Unsuitabe size of noise subspace results in the block effect. In this work, a relative optimal noise subspace determination algorithm is proposed. A Laplace operator is applied to get the size of the relative optimal noise subspace. Considering large-scale SAR images, a chip-merging approach is designed to eliminate the blocking effect with the relative optimal size of noise subspace. Experiment shows that this algorithm eliminates the blocking effect. This work can also be applied to other methods that need the parameter selection based on data.

SAR Tomograpgy is a standard tool for 3D radar imaging, which overcomes the limitation of SAR 2D geometric distortion by using the baseline diversity. Conventional multi-baseline SAR data by repeat-pass space-borne SAR mission requires a long waiting time. The new airborne array InSAR system can acquire the multi-baseline images in a single flight, which significantly improves the practical capability of SAR based 3-D reconstruction. However, the array InSAR system with many channels has very high complexity. The performance of existing algorithms decrease significantly with such a sparse acquisition. In this paper, we have proposed a Range-Elevation (R-E) combined TomoSAR algorithm aiming to solve this issue. The conventional TomoSAR imaging model is converted to a 2D spectrum estimation. The neighboring pixels in the range domain are jointly processed to get the final height estimation. The experiments using real array-InSAR data show that R-E TomoSAR has a good performance in 3-D reconstruction with few acquisitions.

Synthetic aperture radar (SAR) altimetry has the capability of altimetry with a good accuracy, especially for the ocean scene. The model-based methods are widely used in the height inversion of the altimetry data due to the strong correlation with physical process. However, the bias of the model will significantly decrease performance of height inversion. In this work, we introduce a novel height inversion algorithm based on the closed-form expression, which is an effective model for measuring radar echoes. Effective initialization strategy expands the application scope, and two-step retracking algorithm is designed which could be used in an iterative least-squares algorithm. In essence, this method aims to remove the coupling between the parameters in closed-form expression. Experimental results using the simulated data indicate that this proposed method has wider application range under different signal-to-noise condition. In addition, precision of two-step method remains stable compared with traditional method, which has potential for actual altimetry system.

Spaceborne multitemporal interferometric synthetic aperture radar (MT-InSAR) technique is widely used in mapping large-scale deformation with high precision. As the time series lengthens, radar scatterers with a surface change will suffer from a loss of coherence, denoted as the temporary coherent scatterers (TCSs), which can be identified by analyzing the amplitude time series. However, current amplitude analysis is sensitive to noise, especially for short temporal subsets, which leads to a high probability of false detected candidates. In this article, an amplitude-aided 3-D phase unwrapping (PU) is proposed to achieve a reliable TCS detection. First, the pre-selection of TCS candidates is conducted using two hypothesis tests. Then, an adaptive thresholding approach is proposed to get the relative optimal thresholds for varying lengths of time series. Based on the initial step-times, a hybrid 3-D PU algorithm is developed to jointly separate the noise from the candidates and refine the moments of the step-times. Finally, three application-based taxonomies are given to fuse the TCS temporal subsets and show their different temporal patterns. Experimental results using real TerraSAR-X and COSMO-SkyMed images show that the proposed amplitude-aided 3-D PU has a low probability of false alarming and an increase in the ensemble coherence. The use of different types of TCS would be beneficial to both deformation monitoring and urban change detection.

Synthetic aperture radar (SAR) has become a state-of-the-art technology in many applications without being affected by changes in weather and daylight. Since the detection capability of the single-dimensional SAR is limited, the multidimensional (MD) SAR system, e.g., multibaseline and multipolarization, is used to improve its performance. The design of the MDSAR system should be directly related to the specified applications and a quantitatively analytical theory for bound analysis is required to achieve good efficiency. In this article, a mathematical framework for inversion bounds analysis of MDSAR is proposed. First, based on the attributed scattering center (ASC) model, the Fisher information matrix and its corresponding Cramer–Rao lower bound (CRLB) are used to get the error bound of the estimated parameters. Second, considering the discrete sampling (DS) of the parameters, a probability density function-based conversion is conducted to get the DS CRLB. Finally, the mathematical framework for MD acquisitions is established. The simulation-based experimental results show that the theoretical error bound is consistent with the output of the orthogonal matching pursuit (OMP). The error bound of the parameters obtained by the proposed general mathematical framework can be used to evaluate the performance of inversion algorithms under certain MDSAR configurations.

2D SAR image suffers from distortions such as layover, foreshortening and shadow. These distortions can be well resolved if the surface elevation is determined. However, traditional single-pass InSAR is limited by the phase continuity assumption, which can be mitigated by using the multi-baseline observations. Fortunately, airborne array-InSAR system acquires the multi-baseline in a single flight, but side-lobe will significantly affect the performance of 3D reconstruction. Sparse radar imaging is an efficient way to suppress the sidelobe. In this paper, a capon-based approach is used to reduce the sidelobe and improve the quality of 3D reconstruction. In the practical application, the performance of sidelobe reduction and weak signal protection is contradictory. A trade-off is made by using the similar indicators to get the optimal subaperture size. Experimental results by the real airborne array-InSAR images show that the reducing sidelobe improve the coherence of the weak scatterers, improving the performance of 3D reconstruction.

Current synthetic aperture radar (SAR) missions with the satellite constellation can effectively reduce repeat time, achieving a near real-time deformation monitoring. In the practical application, dynamic InSAR process is a good option to improve the computational efficiency. The proposed amplitude-augmented recursive InSAR time series enables both deformation anomalies and surface changes detection, which gives a new route to analyze the InSAR data. And quality indicators such as detect-ability power and the minimum detectable deformation are proposed to interpret the detected anomalies. However, the the sensitivity of the anomaly detection depends on the choice of the processing strategy. A quantitative analysis of the sensitivity is required. In this work, the key part of the temporal deformation anomaly detection, such as the number of update observation and quality metrics is investigated, which gives the users a reasonable demonstrations in the practical applications.

Simulation of Synthetic Aperture Radar (SAR) image in complex scenes has always been a challenging research. The key step of the simulation is determining the parameters of the bidirectional scattering distribution function (BSDF). However,the lack of BSDF parameters optimization make it difficult to get a good simulation. In this work, an optimal parameter estimation of BSDF based a differentiable ray-tracing is proposed. In this SAR image simulation engine, ray-tracing mapping and projection algorithm (MPA) can be inversely differentiable and thus gradient estimation of parameters can be quickly obtained from simulated SAR images. Additionally, with the help of robust physical scattering model based on small perturbation method (SPM) for microray tracing, a better BSDF is obtained, which improve the performance of SAR simulation.

Array-InSAR system can acquire the multi-baseline images in a single flight, which significantly improves the capability of the practical application. However, the array-InSAR system with many channels has high complexity in both system and data processing due to the cross-channel calibration. Investigation of the 3D reconstruction suitable for sparse array-InSAR images enables the reduction of the number of channels. This work propose evaluates the possibility of the 3D reconstruction using three channel array-InSAR images. To improve the reliability, the proposed 3D phase unwrapping (PU) works on a framework combining short and long baseline interferograms. Additionally, Using a success unwrapping criteria, the bounds of th possible baseline combination is derived. Experimental results by real SAR data show that the proposed method is able to achieve 3D reconstruction in urban areas with high precision using only three channel array-InSAR images and optimize the baseline design of the array-InSAR system.

SAR target detection in high-cluttered environments is a challenging task, especially in medium-resolution SAR images. It is hard to identify targets due to strong background inferences. However, targets often change over time while most background scatterers are temporally stable. With the increasing availability of multi-temporal SAR images, one can separate moving targets from the background by analyzing their temporal behavior. In this paper, a novel method is proposed to remove background clutters by developing a stable background mask. First, we investigate the temporal behavior of amplitude time series (TS) of different scatterers using three typical distributions. Then, temporally stable pixels are extracted to generate a stable background mask for the scene. We block the stable regions for each image in the amplitude TS and obtain the processed images where only unstable regions that may include targets are left. Since most background inferences are eliminated, it will help lower false alarms in the target detection procedure.

Synthetic aperture radar (SAR) missions with short repeat times enable opportunities for near real-time deformation monitoring. Traditional multitemporal interferometric SAR (MT-InSAR) is able to monitor long-term and periodic deformation with high precision by time-series analysis. However, as time series lengthen, it is time-consuming to update the current results by reprocessing the whole dataset. Additionally, the number of coherent scatterers varies over time due to disappearing and emerging scatterers due to inevitable changes in surface scattering, and potential deformation anomalies require changes in the prevailing deformation model. Here, we propose a novel method to analyze InSAR time series recursively and detect both significant changes in scattering as well as deformation anomalies based on the new acquisitions. Sequential change detection is developed to identify temporary coherent scatterers (TCSs) using amplitude time series. Based on the predicted phase residuals, scatterers with abnormal deformation displacements are identified by a generalized ratio test, while the parameters of stable scatterers are updated using Kalman filtering. The quality of the anomaly detection is assessed based on the detectability power and the minimum detectable deformation. This facilitates (near) real-time data processing and decreases the false alarm likelihood. Experimental results show that the technique can be used for the real-time evaluation of deformation risks.

Multi-temporal synthetic aperture radar interferometry (MT-InSAR) is able to reconstruct a 3D surface model with high precision but requires a long waiting time to get the multi-baseline SAR images. Array-InSAR system can acquire multi-baseline images in a single flight, which significantly improves the practical capability of 3D reconstruction. However, the array-InSAR system with many channels has very high complexity in both system and processing because of cross-channel calibration and decoupling. Thus, reducing the number of channels requires the investigation of the 3D reconstruction algorithm to be suitable for sparse array-InSAR images. This work proposed an asymptotic 3D phase unwrapping (PU) algorithm for 3D reconstruction using sparse array-InSAR images, i.e., as few as three or four channels. A 2D (space) + 1D (baseline) PU framework is developed to improve the reliability of the 3D PU and a novel asymptotic strategy is proposed for the combination of the short-long baseline interferogram. Using a successful unwrapping (SU) criteria, the bounds of the possible baseline combination and the expected minimal coherence are derived, respectively. The main advantage of the proposed algorithm is the reliable phase unwrapping with very sparse channels and an analysis of the possible baseline combination. The experimental results by both simulated and real data show that the proposed method can achieve a 3D reconstruction using only three-pass array-InSAR images and optimize the baseline design for the array-InSAR system.

Multitemporal synthetic aperture radar interferometry (MT-InSAR), capable of detecting both surface deformation and elevation with high precision, is used for many applications in earth observation. Conventional synthetic aperture radar (SAR) missions with a single beam only detect deformation along the line of sight (LOS) and relative elevation due to the undetermined model of phase wrapping. In a multisatellite SAR mission, measurements from different SAR geometry improve the sensitivity of the detectable deformation, especially to the deformation along the north–south (N-S) direction. However, it is difficult to combine the measurements from varying viewing angles since the absolute phase cannot be reconstructed without a ground control point. In this article, a tri-beam SAR system is introduced to detect 3-D deformation and derive multiview 3-D surface model from a single spaceborne platform. The accuracy of the 3-D deformation from the tri-beam SAR is exploited with varying squint and incident angles to obtain the optimal parameters of the three beams. Then a multidimensional coherent scattering model is used to simulate the multitemporal SAR data with different viewing angles. Regarding the tri-beam SAR, potential applications in earth observation including 3-D deformation monitoring, geodetic stereo SAR, and multiview 3-D forest reconstruction are investigated subsequently. The results of this study indicate that the tri-beam SAR is able to measure 3-D deformation and reconstruct 3-D surface model without ground control point.

In airborne array interferometric SAR (Array-InSAR) tomography, the measurements acquired by conventional uniform sampling array are always restricted by the number of physical baseline elements and the size of baseline aperture. It is desirable to capture new acquisitions and enlarge the aperture with virtual signal processing instead of actually adding array baselines. For this motivation, we utilize the disparity of a pair of coprime sampling sub-arrays to enlarge the baseline aperture and construct new observations virtually. The generation of virtual measurements is equal to estimating cross-correlation matrices in real SAR data. Due to the spatial target variation, we adopted an adaptive filtering method to estimate the cross-correlation matrix. We call the above-mentioned processing of generating virtual measurements as acoprime sensing technique. The newly generated virtual measurements have more degrees of freedom, a larger baseline aperture, and a higher signal-to-noise ratio (SNR) than the physical measurements. These advantages offer the possibility to obtain competitive three-dimensional (3-D) imaging results without increasing the hardware cost of the Array-InSAR. We demonstrate the effectiveness of the proposed method by the coprime acquisitions selected from AIRCAS Array-InSAR data.

Atmospheric delay induces spatial phase errors and decorrelation in synthetic aperture radar (SAR) interferometry, especially in extreme weather conditions. For SAR missions, the atmosphere is considered to be spatio-temporally frozen during the aperture integration time, which is correct for Low Earth Orbit (LEO) SAR systems. However, this assumption may be inappropriate for Geosynchronous Earth Orbit (GEO) SAR since it can deploy a much longer integration time. Here we simulate a sequence of refractivity distributions with a high spatio-temporal resolution to analyze the spatio-temporal variable troposphere. The impacts of both frozen flow shift and turbulent delay in the time series in-terferograms are obtained, showing that tropospheric delay varies rapidly and may lead to phase decorrelaton within a few minutes.

Multi-temporal interferometric synthetic aperture radar (MT-InSAR) is used for many applications in earth observation. Most MT-InSAR methods select scatterers with high coherence throughout the entire time series. However, as time series lengthen, inevitable changes in surface scattering lead to decorrelation, which systematically decreases the number of coherent scatterers. Here, we propose a novel method to detect and process temporary coherent scatterers (TCS) by subsequently analyzing the amplitude and the interferometric phase. Two hypothesis tests are developed for amplitude analysis in order to identify the moments of appearing and/or disappearing coherent scatterers. Based on the amplitude analysis, the parameters of interest are then estimated using the interferometric phase. An optimized adaptive temporal subset approach is proposed to improve the precision of the estimated parameters. If the scatterers are not evenly distributed over the area, a secondary (support) network is designed to improve the spatial point distribution. The main advantage of this method is the reliable extraction of a subset of time series without using any contextual information. Experimental results show that the TCSs significantly increase the number of observations for displacement monitoring and improve the change detection capability in urban construction areas.

In the multi-temporal InSAR technique, a deformation map is obtained by arc integration. Therefore, the reliability of an arc solution directly influences the precision of the final result. Many methods have been proposed to estimate arc parameters, but few have evaluated the arc quality. In this study, the influence of phase ambiguity is analyzed and two indicators are defined to identify the arcs with ambiguity. Subsequently, phase unwrapping is applied and bad arcs are removed according to other two indicators. In addition, the network is reconstructed based on a simple star network to link most points. Finally, the result obtained by this method is compared with the result by GAMMA software, which generally exhibit good agreement.

In this letter, the permanent scatterer interferometric synthetic aperture radar (PS-InSAR) method is applied to extract ground subsidence using X-band TerraSAR-X images. A zone of 1 km width on both sides of the Shanghai maglev was selected, and the ground subsidence rates were obtained on all permanent scatters or highly coherent targets. The results show that permanent scatters are distributed on the rail of the maglev, and the subsidence rates of these PS points are mostly less than 3 mm/a. A KS-test is formulated to judge the agreements between the subsidence results obtained by PS-InSAR and those obtained by spirit leveling, which indicated that PS-InSAR results are acceptable statistically. In addition, the PS-InSAR results show some ground subsiding troughs near the maglev track, with subsidence rates of more than 10 mm/a.

A method for SAR target recognition using low-frequency sub-band and Gabor filter sub-block statistical feature is proposed. The sub-band image extracted from the pre-processed SAR image is filtered by Gabor filter on different directions and scales. The each filtered sub-band image is divided into different sub-blocks and the statistical features derived from every sub-block of all filtered sub-band images are regarded as the target recognition feature, which can be used to recognize the targets with SVM. The proposed method is validated on MSTAR dataset for 3-type SAR target recognition.