The digital elevation model (DEM) reconstruction accuracy of single-channel interferometric synthetic aperture radar (SC-InSAR) is limited by the SAR side-looking imaging geometry, decorrelations, phase unwrapping (PU), and so on. With the availability of increasing InSAR data, to overcome the limitations of SC-InSAR, a multi-source InSAR DEM reconstruction framework based on a complexity factor is proposed in this article. To simultaneously take the effects of noise level and terrain slope into account, a complexity factor for each interferometric pair is constructed. Next, to reduce the PU failure rate for each pair, this factor is used to guide the two-stage programming approach (TSPA) PU method. Then, to avoid the adverse effects of PU failure on elevation fusion, unreliable pixels of each pair are detected by exploiting the complexity factor. Finally, after multiple elevations from different side-looking directions are obtained, the elevation-weighted fusion is performed to reconstruct the final DEM in the map projection coordinate system. Experimental results on real multi-source InSAR data demonstrate that the complexity factor can effectively guide the steps of TSPA PU, detection of unreliable pixels, and elevation-weighted fusion in the proposed framework, thereby improving the DEM reconstruction accuracy for mountainous areas with complex and steep terrain.

Tomographic SAR (TomoSAR) at low frequency, i.e., P/L band, has become a promising tool for forest structure study. Forest canopy height and underlying topography are two of the most important parameters one can estimate using TomoSAR technique. One simple way to estimate these two parameters is to detecting the peaks of the tomographic profile, which, however, can lead to large biases due to complicated forest structure, sidelobes or insufficient TomoSAR resolution. Polarimetric TomoSAR (Pol-TomoSAR) provides a solution to this by exploring the polarimetric diversity to separate the ground and canopy components and then conduct independent TomoSAR analysis. However, Pol-TomoSAR technique suffers from low ground-to-volume ratio (GVR), which often leads to unsuccessful ground and canopy separation. To mitigate this propblem, in this paper, we provide a deep-learning solution to ground and canopy height estimation from 3D tomographic profile through the identification of the patterns of ground and canopy peaks. A 3D U-net model is introduced in our solution to grasp as much three-dimensional characteristics of the tomographic profile as possible. Moreover, our model can be well trained using only synthetic TomoSAR dataset, making it easy to implement when we don’t have enough real data with LiDAR references. The proposed method is validated on P-band real TomoSAR dataset from multiple test sites in AfriSAR campaign, showing that it can achieve more accurate ground and canopy height estimation than the state-of-the-art Pol-TomoSAR techniques. The maximum RMSE improvement reaches as high as 66.4% and 63.2% for ground and canopy top height, respectively.

The coherence level and number of selected interferometric pairs are directly related to the interferometric synthetic aperture radar (InSAR) phase estimation accuracy. In the Multi-Channel InSAR and Multi-Temporal InSAR, it is essential to select high-coherence interferometric pairs and remove low-coherence ones from the massive SAR singlelook complex (SLC) image data. The existing selection method, which based on basic coherence spectral clustering, may become increasingly computationally intensive when processing real-time data. To make a trade-off between the calculation cost and selection accuracy, a novel SAR interferometric pairs selection method based on incremental coherence spectral clustering is proposed. Experimental results demonstrate that the proposed method, which involves selecting a representative SAR SLC image for each cluster rather than re-constructing the adjacency matrix and re-estimating the number of clusters, can yield similar interferometric pairs selection results at a reduced computational cost.

The strong sea clutter and noise in synthetic aperture radar (SAR) images will cover weak ship wakes, making it difficult for conventional ship wake detection methods to accurately detect ship wakes. A ship wake detection method based on joint sparse representation is proposed in this paper to improve the detection effect of ship wakes in complex backgrounds. Firstly, the sparse representation model of SAR images based on the Radon transform is constructed. Secondly, using dual-domain images in the spatial and gradient domains, the optimization function for ship wake detection based on joint sparse representation is constructed and solved iteratively. Then, the weighted clustering criterion based on sparse coefficient and Radon coordinates is used to perform sparse coefficient clustering, and the centerline detection of the ship wake is realized by the inverse Radon transformation of the cluster center. Finally, the experimental results of TerraSAR-X images are used to verify the effectiveness of the proposed method.

Baseline errors is the main error source of airborne multi-baseline SAR tomography. P-band SAR can penetrate into deep vegetation layer even in tropical forests and therefore offers huge potentials in forest structure study. This paper introduces a novel method to estimate and compensate these baseline errors based on small baseline subset interferometric network, which is, compared to the existing methods, (1) less prone to heavy decorrelation noise induced by forest volume scattering and (2) easy to implement without pixel-by-pixel optimization. Numerical experiments conducted on real airborne P-band multi-baseline SAR dataset demonstrate that the proposed method can effectively estimate and correct the baseline errors.

The time bandwidth product (TBP) of Chirp signal has experienced a sustainable growth over the past decades. However, whether to increase TBP can improve information acquisition ability of the interested targets needs to be analyzed. To this end, the relationship between information acquisition and TBP is derived for Chirp signal. Firstly, a quantitative metric for evaluating information acquisition capability is provided. Secondly, the analytical expressions of information acquisition and TBP are derived, under different target scattering models. A larger TBP indicates higher information acquisition ability with a point-like target, while there exists an optimal value of TBP for an extended target as information acquisition increases at first and then declines with the increase of TBP. Finally, numerical examples are employed to verify the theoretical analysis.

Multibaseline interferometric synthetic aperture radar (MB-InSAR) has attracted widespread attention as it can improve the measurement accuracy of elevation or deformation by exploring baseline diversity. However, the interferometric phase is normally contaminated by phase noise, which directly affects the measurement accuracy. In this article, an MB-InSAR interferometric phase noise suppression method based on BM4D (MBInSAR-BM4D) is proposed. To increase the number of similar cuboids for grouping, a topographic phase compensation strategy is introduced, which can reduce fringe density in complex topography. In addition, to accurately select similar cuboids from residual interferometric phase stack and collect them into 4-D groups, the generalized likelihood-ratio (GLR) test is applied, in which the observed amplitude, coherence, and phase are utilized simultaneously for improving the grouping accuracy. After performing collaborative filtering and aggregation on 4-D groups, the MB-InSAR interferometric phase stack noise suppression results are obtained by adding the reference phase back to the corresponding filtered residual interferometric phase. All interferometric phases are fully exploited to facilitate MB-InSAR phase stack filtering performance enhancement. Experimental results on both the simulated and real MB-InSAR data demonstrate that the proposed MBInSAR-BM4D provides superior noise suppression and fringe detail preservation for the MB-InSAR interferometric phase stack.

The carrier frequency and bandwidth are vital parameters of radar transmit signals. In this article, a joint design of frequency and bandwidth in multifrequency synthetic aperture radar (SAR) is proposed to improve the target information acquisition capability. First, the target detection mutual information (MI) is considered as the performance metric, and its mathematical expression is derived using the example of decision-level fusion and hypothesis testing. The design is formulated as an optimization problem with multiple engineering constraints based on MI maximization. Then, a modified genetic algorithm (GA) is proposed to find the optimal solution satisfying the constraints via a code adjustment operator. It is shown by simulation results that for a specific scene, the multifrequency SAR with its frequencies and bandwidths designed by the proposed method has higher target information acquisition capability and more accurate target detection performance than existing spaceborne SAR systems.

Baseline error correction is critical for airborne synthetic aperture radar (SAR) tomography as the actual flight trajectory often deviates from the designed one due to turbulence, which may lead to large sidelobes or even complete defocusing in the tomograms. Current baseline error correction methods, however, are susceptible to heavy decorrelation noise. To mitigate the adverse effect of decorrelation noise, in this article, a novel method for airborne SAR tomography baseline errors correction driven by small baseline interferometric phase is proposed. In this method, a novel mathematical model that relates interferometric phase to the baseline error differences is first derived; then, a small baseline interferometric pairs selection strategy is employed to estimate the baseline error differences through an alternate iterative algorithm, and finally, the baseline errors are obtained through accumulating summation of the baseline error differences. The use of small baseline interferograms can avoid the phase linking processing and thereby greatly alleviate the heavy decorrelation effect. Both simulated and real airborne P-band SAR tomography experiments have demonstrated that the proposed method can achieve more accurate and robust estimation of baseline errors and is more tolerant to decorrelation noise than the well-known phase center double localization (PCDL) method.

Polarimetric synthetic aperture radar (SAR) tomography (Pol-TomoSAR) can be used for global forest digital terrain model (DTM) and canopy height model (CHM) mapping with high spatial and temporal resolution at low economic cost. However, the performance of DTM and CHM inversion in current Pol-TomoSAR methods is often compromised when the ground-to-volume ratio (GVR) is low, which usually happens in complicated terrain where large negative slope angles are commonly present, or in dense tropical forest where the ground visibility is low due to strong attenuation by the dense vegetation layer. In this work, a novel method for the separation of ground and volume scattering, aiming at robust and accurate DTM and CHM inversion in dense tropical forest and complicated terrain, is proposed. By fully exploiting multibaseline polarimetric SAR data, the proposed method can perform a more effective separation of ground and volume scattering. Subsequently, by applying the SAR tomography technology on the separated ground and volume scattering, the proposed method can retrieve accurate DTM and CHM information even at low GVR areas. Numerical experiments conducted on both simulated data and P-band airborne F-SAR data show that, compared to the most commonly used two-component algebraic synthesis method, the proposed one has a much better performance in terms of separation of ground and volume scattering. Furthermore, the inversed DTM and CHM present better agreement with light detection and ranging (LiDAR) measurements, especially in large negative slope angle terrain where the GVR is rather low.

An imaging radar is expected to provide high-quality images for interesting targets. To this end, an information-theoretic approach is used in this article to jointly optimize waveform and receive filter with desired cross-correlation properties. First, the problem formulation is achieved by maximizing the mutual information (MI), subject to constant modulus, high resolution, and low peak sidelobe ratio (PSLR) constraints. Second, to solve the resultant problem with a fractional quadratic objective function and various nonconvex constraints, four customized iterative loops are performed to transform the problem into a series of solvable subproblems, via minorization-maximization (MM), alternate direction penalty method (ADPM), and feasible point pursuit successive convex (FPP-SCA) approximation. Convergence of every iterative loop is proved, resulting in guaranteed convergence of the whole procedure with polynomial-time complexity. Finally, numerical examples are presented to demonstrate that the proposed method can construct a unimodular waveform and filter with better information acquisition ability and more desirable cross-correlation function.

An anomaly-detection-based method is proposed to improve the performance of ship wake detection in synthetic aperture radar (SAR) images of different sea states. The dictionaries learned by sea clutter images are poor to sparsely reconstruct wake images, since the nature of the sea clutter and ship wake is different. Therefore, the image reconstruction errors based on the sea clutter dictionaries are introduced to separate ship wake and sea clutter. The proposed method transforms the wake detection into an anomaly detection problem in the image reconstruction error feature space. First, the dictionaries are learned by a large number of sea clutter samples. Second, taking the sea clutter image reconstruction errors as discriminatory feature, errors of the wake will be larger than the threshold and the detection threshold is obtained by anomaly detectors. Finally, the ship wake detection is made by comparing the reconstruction errors from the test image samples with the detection threshold. Experimental results demonstrated the proposed method can improve the ship wake detection probability compared with the exiting method.

As the availability of SAR images continues to grow, efficient coregistration of massive SAR images presents a greater challenge. Traditional serial coregistration methods impose an unbearable time overhead. To reduce this overhead and make full use of computing resources, a parallel coregistration strategy based on Hadoop is proposed for SAR images. The Hadoop Distributed File System (HDFS) is used to store SAR image data in chunks, and Hadoop's distributed computing strategy MapReduce is used to realize distributed parallel processing of SAR images. Two distributed parallel coregistration methods are presented with the proposed parallel strategy: one based on the maximum correlation method and the other on the DEM-assisted coregistration method. These methods are evaluated through coregistration experiments on the same dataset, and they are verified by comparing the coregistration results and processing time.

Aiming at the problem of insufficient target information acquisition caused by the experience of frequencies selection in the existing multiband SAR system, the optimal frequencies design method of multi-frequency SAR under the maximum mutual information criterion (MMIC) is proposed. After constructing the multi-frequency SAR information acquisition system model, the frequencies design objective function of discrete signal under MMIC is derived. With the limit of engineering practice—frequencies number, range and interval, the constrained optimization problem is constructed, and the optimal frequencies is solved by genetic algorithm. Simulation experiments for specific target verify the effectiveness of the proposed frequencies design method.

To achieve accurate interferometric synthetic aperture radar (SAR) phase estimation, it is essential to select appropriate high-coherence interferometric pairs from massive SAR single-look complex (SLC) image data. The selection should include as many high-coherence interferometric pairs as possible while avoiding low-coherence pairs. By combining coherence and spectral clustering, a novel selection method for SAR interferometric pairs is proposed in this paper. The proposed method can be adopted to classify SAR SLC images into different clusters, where the total coherence of interferometric pairs in the same cluster is maximized while that among the different clusters is minimized. This is implemented by averaging the coherence matrices of representative pixels to construct an adjacency matrix and performing eigenvalue decomposition for estimating the number of clusters. The effectiveness of the proposed method is demonstrated using 33 TerraSAR-X and 38 dual-polarization Sentinel-1A data samples, yielding improved topography and deformation monitoring results.

Multitemporal synthetic aperture radar interferometry (MTInSAR) is an efficient geodetic tool for Earth surface displacement measurement, and the polarimetric capability of current and upcoming SAR satellites offers a new opportunity to further improve MTInSAR phase series estimation. However, none of the existing estimators for multipolarimetric MTInSAR phase series of distributed scatters (DSs) is derived under the minimum root-mean-square error (RMSE) criterion. In this work, a maximum likelihood estimator for multipolarimetric phase linking (MLE-MPPL) is proposed and the corresponding Cramer–Rao lower bound (CRLB) is also derived by modeling the polarimetric interferometric coherence matrix as the Kronecker product of polarimetric coherence matrix and interferometric coherence matrix. In addition, a new metric called Pol-detR is proposed for the performance evaluation of multipolarimetric MTInSAR phase series estimation in practical scenarios where the RMSE is not feasible any more. The experimental results based on both simulated and real data show that the proposed MLE-MPPL achieves the best estimation performance and is more robust against interchannel interference than existing methods.

A joint design of the transmit weight sequence and receive filter is proposed to improve target information acquisition in high-resolution radar. First, using the criterion for target information acquisition maximization, the design is cast as a nonconvex fractional quadratically constrained quadratic problem (QCQP). Then, by employing a bivariate auxiliary function introduced in Dinkelbach’s algorithm to decouple the fractional objective function, an algorithm with polynomial computational complexity is developed to solve the QCQP using a cyclic maximization procedure alternating between two semidefinite relaxation (SDR) problems. Through exploiting a suitable rank-one decomposition, it is verified that the optimal solution obtained from the alternative iterative process is also optimal to the original QCQP. Finally, numerical examples are presented to demonstrate the performance of the proposed design.

Baseline is an essential parameter in Interferometric SAR (InSAR). It's directly related to the estimation accuracy of elevation, so precise baseline estimation is required. Aiming at the existing baseline estimation methods based on interferometric fringe frequency, this paper proposes an improved baseline estimation method based on least square. Compared with the existing methods, the proposed method can fully exploit the potential of the interferogram data by applying the least square method to solve the formula between fringe frequency and baseline parameter to achieve baseline estimation. The simulated results show that the proposed method has improved performance in both accuracy and robustness for InSAR baseline estimation.

Layover and shadow areas in SAR image cause InSAR interferometric phase unwrapping errors in adjacent area. Therefore, they should be detected and marked. Existing detection methods mostly use single feature, exhibiting poor universality in complicated scenes. In this paper, a joint detection method integrating multi-feature is proposed. It uses local frequency estimation and improved eigenvalue decomposition to make preliminary judgments, and then performs joint detection on the results of both. Its effectiveness is experimentally demonstrated from the detected results of simulated and real data: compared with existing methods, the joint detection greatly reduces false-alarm in problem areas detection while improves the accuracy.

Benefiting from the advantages of sparse signal recovery, SAR imaging methods based on sparse signal recovery can achieve low sampling rates and high resolutions, and have developed rapidly in recent years. However, most of the currently proposed imaging methods cannot suppress the coherent speckles in SAR images. As we know, the recovery methods based on TV norm minimization can increase the continuity and is widely used in image denoising field. Therefore, following the idea of range doppler algorithm, this paper adopts sparse signal recovery for azimuth compression and proposes a SAR imaging method based on Lpand TV norm regularization. The proposed method can combine the high resolution, low sidelobes of Lpnorm minimization and the noise suppression of TV norm minimization to obtain high quality SAR images.

The residues have brought great difficulties to phase unwrapping. Conventional interferometric synthetic aperture radar (InSAR) processing suppresses residues by phase filtering. However, residues in layover areas cannot be removed well and will cause new incorrectness. In this paper, deep learning technology is introduced to InSAR processing. Firstly, the modified Fully Convolutional Network (FCN) is used to segment the layover from the normal pixels. Then, the Denoising Convolutional Neural Network (DnCNN) is modified to estimate the phase noise in normal pixels and remove it from the interferogram. Finally, the denoised phase in non-layover regions is unwrapped. Both simulation and real data experiments have verified that the proposed algorithm can effectively avoid the error propagation of residues and layovers, significantly improving the precision of phase unwrapping.

The classical linear frequency modulated waveform (LFMW) for synthetic aperture radar (SAR) can achieve a high range resolution and a superior signal-to-noise ratio (SNR) for point targets. Therefore, numerous researches got increasingly passionate about increasing resolution and SNR, rather than information acquisition of LFMW. This paper quantitatively analyzes and evaluates target information acquisition ability of LFMW, which is conducive to target perception. Firstly, a factor contributing to information acquisition of LFMW, is obtained under the discretized signal model. Secondly, a connection between LFMW and eigenvectors of target feature matrix is derived through the discrete Fourier bases, thus the information acquisition of LFWM is presented. Finally, the numerical simulations are made for the stochastic extended targets information acquisition of LFMW, and the results are employed to verify the theory analysis.

Multi - frequency interferometric synthetic aperture radar (InSAR) has drawn more attention in reconstructing height profile. In this paper, a novel unwrapped phase estimation method based on split-spectrum strategy and maximum likelihood (ML) criterion is proposed for multi-frequency InSAR. Firstly, multiple range subband interferograms are generated with the split-spectrum strategy to form a multifrequency configuration. Then, unwrapped phase of the reference frequency is estimated via ML method modeled by the proposed normalized probability density function (pdf) which is able to remove the phase ambiguity. Finally, simulation experiments are carried out to evaluate algorithm performance. Smaller unwrapped phase error standard deviation verifies the effectiveness and exactness of the proposed method.

The joint probability density functon (pdf) of interferometric phases, used for the height estimation of multibaseline interferometric synthetic aperture radar (InSAR), was derived under the limited conditions, e.g., the real parts and imaginary parts of SAR images are uncorrelated, or the number of looks for each SAR image is not less than the number of observations which sacrifices spatial resolution. In this paper, firstly, the joint pdf of multibaseline interferometric phases with a general correlation between real parts and imaginary parts is given in case of unlimited number of looks. Then, the correctness of the expression is verified by comparing the proposed derivation with the conventional one through the single-look single-baseline system. Additionally, the correlation between interferometric phases is demonstrated by comparing the proposed joint pdf with the one under the independent interfero-grams assumption through single-look dual-baseline system. Finally, the exactness of the proposed joint pdf of mulitibase-line interferometric phases is confirmed through the real data.

Due to the low correlation of SAR data in Yunnan mountainous area, special interferometric processing method is proposed in this paper. Initially, two SLC images are analyzed and co-registrated by using different correlation functions. The best registration result is selected through correlation evaluation. Then, the noisy phase is multi-looked and smoothed by four phase filtering algorithms. The most appropriate filtering method is chosen by evaluating the filtered phase. Subsequently, the Goldstein's branch cut method guided by residues grouping is employed to derive the unwrapped phase. Ultimately, both the gradient-jump point number and its distribution are utilized to evaluate the unwrapped results and identify errors in unwrapped phase.

Deceptive jamming, covering the true targets and producing false targets in the SAR image, has been widely applied in electronic warfare. To effectively suppress the deceptive jamming generated by detecting parameters of target echoes, this work proposes a novel anti-jamming approach. Firstly, the chirp signal with varying initial phase is predesigned and transmitted. Then, the random initial phases of received echoes are removed before imaging. The imaging outputs of the deceptive jamming and real signal are derived through theoretical analysis. By comparing those signal models, we obtain that the proposed method induces the defocusing of false signal while the real signals are well focused. Finally, the performance of deceptive jamming suppression method is validated by simulation.

The image registration is one of the key steps to achieve three-dimensional (3D) localization and the other image fusion processes. This article presents a registration method based on the combination of edge feature and corner feature. The processing steps include image segmentation, corner detection, edge detection, extraction of interested region, and correspondence points matching. The algorithm flowchart and implementation of every step are presented. The proposed method makes the image registration with the multiple features of two images and avoids the inaccuracy of the SAR image information. Therefore, it has a better robustness and registration precision. A TerraSAR-X SAR image and a GeoEye-1 optical image of the Water Cube in Beijing are processed and the results validate the effectiveness of the proposed method.

The cycle-property of probability density function (pdf) of the interferometric phase is demonstrated in brief. The normalized baseline pdf is presented in terms of reference baseline to conquer the constant 2π-cycle. Accordingly, a novel unwrapped phase estimation method, based on a maximum likelihood (ML) principle technique, is imported for multi-baseline SAR interferometry. Simulations corroborate the validity of maximum likelihood unwrapped phase estimation method proposed in this work.

This paper investigates the method for 3-D buildings reconstruction using the fusion of SAR and optical sensors. First, the joint positioning equation is described as the foundation of this method. Then, the principal steps of this method are recommended. Image registration is employed to obtain a mapping function between the two images as the first step. And the 3-D coordinates are calculated with the joint positioning equation. Afterwards, the 3-D buildings are reconstructed using the 3-D coordinates obtained above. Finally, an experiment is conducted to show the reconstruction result of this method.

Spaceborne D-InSAR system is a kind of SAR satellite system aiming at differential interferometry application. It provides us a capability of measuring subtle deformation on the ground with phase difference. In this work, we focused on the system performance analysis theory as a first step to launch the research work on the overall spaceborne D-InSAR system. After the theoretical model and major error analysis work, we realized that coherence is really important. The qualitative and quantitative analysis showed that the situation becomes worse due to volumetric and temporal decorrelations compared with single-pass InSAR system such as TanDEM-X system. The in-orbit SAR satellite data experiment study validated the quantitative analysis. In order to obtain high-precision D-InSAR measurement, there are some new problems to solve rather than just using an existing SAR satellite to implement D-InSAR.

This paper investigates the joint use of SAR image and optical image for stereo positioning. To obtain the 3D information of target in the real scene, the geometric relationship between optical radar and SAR are initially established. Then, two angle equations of optical data and a slant range equation of SAR data are jointly utilized to derive the 3D coordinate of target. To further analyze the impact of uncertain measurer errors such as range measurement, orbit, and angle measurement on the novel location model, the partial derivatives of resultant elevation are mainly discussed. Finally, computer simulations are provided to show the error range of the proposed method according to different measurer errors.

In this paper, a novel multi-baseline phase unwrapping approach is proposed based on maximum likelihood estimation (MLE) method. Topography phase gradient is acquired by fusing multi-baseline InSAR data. It is convenient to obtain unwrapped phase by using gradient integral. Since search interval is critical for the uniqueness and accuracy of solution, low-precision digital elevation model (DEM) is imported to improve the estimation interval. Simulations corroborate the validity of maximum likelihood unwrapped phase estimation method proposed in our work.

The Multi-baseline synthetic aperture radar interferometry (InSAR) is capable of solving layover phenomena because of its resolving capability along the elevation dimension. Using baseline diversity of a multi-baseline InSAR system to overcome layover is essentially a matter of spectral estimation problem. The spectrum's period and the estimation resolution are influenced by the InSAR system parameters. This paper presents a thorough analysis of the influence of system parameters on the unambiguous height difference and the resolution of height retrieval of layover scenarios. The largest unambiguous height difference is derived and it is related to the baseline number and length. The resolution of height retrieval is determined by the signal-to-noise ratio, baseline number and multilook number. At the end, some useful advices on designing multi-baseline InSAR system parameters are presented.

In synthetic aperture radar interferometry, past studies of interferometric phase statistics are mainly based on the assumption that the interferogram cell size is much larger than the wavelength of the incident radiation and the scene is a homogeneously distributed scatterer. However, strong scatterers are often present in the scene, and in this work, the interferometric phase statistics are studied for this case for single-look interferograms. Its closed-form probability density function is first derived by approximating the complex interferogram signals to two correlated Gaussian random variables with nonzero-mean values. The closed-form mean value and variance are then derived with the assumption that the intensity of the strong scatterer is much larger than that of its background. It is shown that the phase statistics of strong scatterers are related not only to the correlation but also to the intensity of the dominant point and the phase difference variation between the dominant point and the remaining points.

The polar format algorithm (PFA) is a wavenumber domain imaging method for spotlight synthetic aperture radar (SAR). The classic fixed-parameter PFA employs interpolation technique for data correction. However, such an operation will induce heavy computational load and cause degradation in computation precision. To optimize image formation processing performance, this study presents a novel parameter-adjusting PFA, which can implement SAR image formation at an extremely highly squint angle with obviously improved computation efficiency and imaging precision. In the parameter-adjusting PFA, radar parameters, such as center frequency, chirp rate, pulse duration, sampling rate, and pulse repeat frequency (PRF), vary for each azimuth sampling position. Due to the parameter adjusting strategy, the echoed signal can be acquired directly in keystone format with uniformly distributed azimuth intervals. In this case, range interpolation, which is necessary in the fixed-parameter PFA to convert data from polar format to keystone format, can be eliminated. Chirp z-transform (CZT) can be employed to focus SAR data along the azimuth direction. Compared with truncated sinc-interpolation, CZT was found to perform better in inducing less phase and amplitude errors in data processing. When residual video phase (RVP) compensation was accomplished for dechirped signal, the processing steps of the parameter-adjusting PFA were simplified as azimuth CZTs and range inverse fast Fourier transforms (IFFT). Lastly, computer simulation of multiple point targets validated the presented approach.

Typical features in SAR image like the bright line cause by double scattering are important datum in building height extraction, especially for the extracting method form a single SAR imagery. Its accuracy will affect the extraction result. In this paper, against to the imprecise of double scattering model, we modified its geometrical relationship. By using this model, we proposed a method with the usage of pixel interpolation and image correlation to extract the height of rectangular and cylindrical buildings.

The interpolation-based Polar Format Algorithm (PFA) can be used in bistatic Synthetic Aperture Radar (SAR) image processing while suffering from heavy interpolation computation load and complicated space-dependent resolution. To decrease computation load, this paper presents a nonlinear trajectory-based PFA, in which sensors are designed to fly on conical surface to avoid range interpolation. Due to this conical bistatic geometry, two advantages can be achieved. First, part of computation load can be converted to navigation system and processing speed can be improved. Second, a space-independent range resolution can be approached. A following multi-scatter simulation validates the presented approach.

Spotlight synthetic aperture radar (SAR) can obtain high resolution SAR image but the observation area is restricted. This paper presents a new imaging mode named jumping spotlight SAR to achieve high resolution and wide swath simultaneously. In jumping spotlight mode, high resolution SAR image is acquired by antenna beams steering, and meanwhile the wide observation swath is realized through antenna beams scanning in range and azimuth directions. The imaging theory of jumping spotlight SAR is given based on the data acquisition geometry and some of the imaging difficulties are analyzed. Then the design principle of the antenna beams scanning order is proposed in this imaging mode. Simulation results show that the scanning order design rule can simplify the processing of squint spotlight imaging.

This paper improved polar format algorithm (PFA) based on nonuniform azimuth sampling for squint spotlight synthetic aperture radar (SAR) imaging. The range resampling for chirped signal is carried out by chirp z transform (CZT). The novel nonuniform azimuth sampling during the echo data collection enables the utilizing of CZT in azimuth resampling. According to the data collection geometry, the precise expression of phase error induced by wavefront curvature is deduced, and then the overlapped subaperture approach (OSA) is adopted to correct the geometric distortion and defocus arising from the effect of wavefront curvature. Simulation results confirm the validity of the presented approach.

Optimal segmentation results can be obtained by synthetic aperture radar (SAR) image segmentation based on the Markov Random Field (MRF) model. However, MRF segmentation is very time-consuming because of employing the simulated annealing algorithm to optimize energy function. To speed up SAR image segmentation based on the MRF model, this paper investigates a fast segmentation approach for SAR imagery by fusing optical imagery. First, the optical image is applied to accelerate SAR image segmentation by selecting the uncertain pixels which only attend the SAR image segmentation and marking the certain pixels which don't attend the SAR image segmentation. Second, a fast annealing strategy is proposed to shorten the annealing time under low temperature. Finally, Computer simulation is given to validate the effectiveness of the proposed method.

There are two main geolocation models for spaceborne synthetic aperture radar (SAR) interferometry (InSAR): range Doppler (RD) and direct geocoding (DG) models. The RD model gets the target position by combining and solving the Doppler equation, slant-range equation, and the modified Earth model equation. The DG location model gets the target position through its 3-D coordinates by the Doppler equation and two slant-range equations. Usually, the geolocation accuracy analyses of these two models are discussed separately. It is confused which one is more precise and we should use during InSAR system designing. This paper deduced and compared the geolocation accuracies of these two models in the same frame-matrix. According to the matrix theory, the explicit expressions of the geolocation uncertainty of RD and DG models were deduced through the use of parameters in matrix form. After defining a new slant-range plane coordinate system, the precision of RD geolocation model and that of geocoding location model were compared quantificationally. It was presented that the geolocation uncertainty formulas between RD and DG models were the same. Then, the conclusion that these two models would lead to the same precision in geolocation measurement was obtained. At last, computer simulation results were employed to confirm the mathematical analysis.

The DSS-INSAR (Distributed Small Satellite INSAR) applies the interferometry to the carrier platform which is made up of small satellites flying in a certain formation. It aims to retrieve DEM (Digital Elevation Map) with quite high precision globally. In fact, every small satellite in the flying formation is dynamically circling around a reference central point along a certain track. Therefore, the baselines generated by the flying satellites are also unstable. However, the stability of these baselines is very crucial to get elevation measurement with high precision. Therefore, it is valuable to analyze the influences of the above-mentioned baselines on unwrapped phases. This article demonstrates the errors of unwrapped phases introduced by dynamical baselines of DSS-INSAR and provides effective solutions to compensate those errors in detail. In the first beginning, this article deduces the relationship between the across-track baseline, the along-track baseline and the orbit elements, respectively. Then, it analyzes the unwrapped phase error introduced by the dynamical baseline of DSS-INSAR. Finally, it provides a very effective model which can be used to compensate the error.