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Interferometric Synthetic Aperture Radar (InSAR) enables the efficient retrieval of surface elevation and has extensive applications in terrain mapping. Dual/multi-channel InSAR techniques utilize the differences in the elevation ambiguity of different InSAR channels (i.e., baselines and frequencies) to perform Phase Unwrapping (PU). This enables the effective application of InSAR in regions with abrupt terrain changes. In response to the growing demand for efficient and precise PU, this study leverages deep learning and proposes a dual/multi-channel joint PU network, i.e., Multi-Channel-Joint-UNet (MCJ-UNet), which effectively combines multi-channel phase characteristics and their mutual constraint relationships. The proposed network is constructed based on the dual-channel (i.e., dual-frequency and dual-baseline) InSAR observation configuration. It can also be extended to multi-channel InSAR. The core concept of the proposed method can be summarized as follows. First, the method transforms the elevation ambiguity estimation problem in PU into semantic segmentation, and the UNet network is employed to accomplish the segmentation processing. Second, the squeeze-and-excitation module is introduced to dynamically adjust the information weights, enhancing the network’s perception of the required information across different channels. Third, a phase residual optimization loss function is employed in the context of multi-channel joint constraints to achieve network tuning. In addition, to mitigate the effect of edge detail errors in semantic segmentation results on PU performance, a self-correcting approach for PU errors based on multi-channel joint constraints is proposed. The proposed MCJ-UNet is verified by computer simulations based on simulated and real terrains and experiments based on real TerraSAR-X data.

Lunar tidal deformation (LTD), characterized by the vertical (${{{\bm{h}}}_2}$) and horizontal (${{{\bm{l}}}_2}$) displacement Love numbers, is a key to deciphering the interior structure of the Moon. However, the small deformation amplitude of only up to 10 cm makes their measurements very challenging. In this article, we propose a novel method named Earth-based repeat-pass SAR interferometry (EBRP-InSAR) to measure the LTD and displacement Love numbers. We analyzed the potential and performance of EBRP-InSAR in detail based on the LTD model and existing planetary radar capabilities. The error budget and simulation results show that the relative measurement accuracy of LTD could be better than 2 mm. Furthermore, compared with lunar laser ranging (LLR) and lunar orbiter laser altimeter (LOLA), the two-pass EBRP-InSAR cannot only directly estimate ${{{\bm{h}}}_2}$ but also ${{{\bm{l}}}_2}$ with accuracies better than ${{\text{10}}^{ - 3}}$ and${{\text{10}}^{ - 4}}$, respectively, which is comparable to that of LLR and LOLA methods. In addition, through long-term time-series observations, it will be possible to assess the spatial inhomogeneity of LTD response to the forcing potential in the near side with time-series EBRP-InSAR. After spatial smoothing, a space variation as small as on the order of ${{\text{10}}^{ - 4}}$ and ${{\text{10}}^{ - 5}}$ in ${{{\bm{h}}}_2}$ and ${{{\bm{l}}}_2}$, respectively, can be distinguished on the lunar near side. As another measurement technology independent of LLR and LOLA, the EBRP-InSAR is expected to explain the difference between the observations from LLR and LOLA and the modeled lunar interior structure, and first estimate the lunar horizontal displacement Love number.

Frequency stepping is a widely used technique for ultrahigh-resolution synthetic aperture radar (SAR). Although reducing the burden of hardware, this technique increases the complexity of imaging algorithms due to the intersubband time offsets and intersubband errors of delay, amplitude, and phase. To address the above problems, an improved ultrahigh-resolution stepped-frequency spaceborne SAR imaging algorithm is proposed in this article. By generating subband images individually, performing intersubband error estimation based on primary points, and then synthesizing the subband images in the imaging domain, the proposed algorithm effectively avoids the problem of time offsets and significantly improves intersubband error compensation accuracy benefiting from the high SNR in the imaging domain. Besides, considering the characteristics of nonideal factors in frequency-stepped SAR, a series of error compensation methods aiming at stop-and-go approximation, ionospheric error, and tropospheric delay are integrated to the proposed algorithm. The effectiveness of the proposed algorithms is verified via computer simulations, and real data experiments are also conducted based on both an X-band spaceborne SAR system, Taijing 4-01, and a Ka-band spaceborne SAR system, Luojia 2-01.

Multichannel spaceborne synthetic aperture radar (SAR) is a useful approach to overcome minimum antenna area constraint and achieve high-resolution and wide-swath imaging. For multichannel SAR imaging, the azimuth ambiguity is the principal problem, which should be suppressed by azimuth spectrum reconstruction. However, the traditional azimuth spectrum reconstruction algorithm requires too large onboard resources to realize real-time imaging. To solve the problem, this article proposes a simplified azimuth spectrum reconstruction algorithm. By updating the factors regionally in azimuth, they only need to be calculated and stored once in each region. Hence, the onboard storage and computational resources can be effectively reduced. However, it will bring in the azimuth ambiguity to some extent, and further cause the spurious targets in the image. Fortunately, the deterioration of image quality is slight enough to be neglected if the region width (called focusing depth in this article) is properly chosen. Thus, this article also quantitatively analyzes the relationship between focusing depth and azimuth ambiguity, and further gives the focusing depth selection criterion to ensure that the spurious targets can be submerged in the clutter and noise. The results of computer simulations and real data experiment demonstrate the effectiveness of proposed algorithm.

Spaceborne Synthetic Aperture Radar (SAR) can achieve various performance combinations of resolution and observation bandwidth by adjusting the working modes. The imaging swath of the traditional spaceborne SAR working mode is along the satellite orbit, and the geographical trend is single; however, the geographical shape and direction of the surface scene are diverse and generally do not match the imaging swath along the orbit, resulting in a long data acquisition period, low azimuth resolution, and storage waste of computing resources. To this end, the spaceborne SAR Non-along-track imaging mode is a new method for spaceborne SAR scene matching that is characterized by an imaging zone that is no longer mechanically along the satellite orbit but is generated according to the actual geographical direction of the scene to achieve “customization” that matches the scene imaging. In this paper, the main opportunities and challenges faced by the new mode of spaceborne SAR scene matching are discussed from the aspects of information acquisition and imaging processing, and the principled verification of the spaceborne SAR Non-along-track imaging mode is provided through a computer simulation.

Abstract The dechirp‐receiving radar measures range by the frequency difference between the target echo and the reference signal. It can operate at a low sampling frequency while transmitting a wideband signal, which simplifies the radar hardware. However, when detecting high‐speed and highly maneuvering targets, residual video phase (RVP), frequency mismatch, frequency modulation (FM) rate mismatch, and across range‐Doppler unit (ARDU) problems occur after dechirp receiving, which cause difficulties in integrating the target echo and result in severe performance loss. To solve these problems, the dechirp generalized Radon‐Fourier transform (DGRFT) and an algorithm for its fast implementation are proposed. The DGRFT compensates for the frequency and FM rate mismatch in the fast‐time domain and the RVP in the fast‐time frequency domain, and then implements joint envelope and phase compensations to overcome the ARDU phenomenon. After these compensations, the target echo can be effectively integrated. Experiments with simulated and measured data show that the performance of the DGRFT is better than that of the conventional moving target detector.

Due to its rapid deployment, high-flexibility, and high-accuracy advantages, the unmanned-aerial-vehicle (UAV)-based differential synthetic aperture radar (SAR) tomography (D-TomoSAR) technique presents an attractive approach for urban risk monitoring. With its sufficiently long spatial and temporal baselines, it offers elevation and velocity resolution beyond the dimensions of range and azimuth, enabling four-dimensional (4D) SAR imaging. In the case of P-band UAV-SAR, a long spatial-temporal baseline is necessary to achieve high enough elevation-velocity dimensional resolution. Although P-band UAV-SAR maintains temporal coherence, it still faces two issues due to the extended spatial baseline, i.e., low spatial coherence and high sidelobes. To tackle these problems, we introduce a multi-master (MM) D-TomoSAR approach, contributing three main points. Firstly, the traditional D-TomoSAR signal model is extended to a MM one, which improves the average coherence coefficient and the number of baselines (NOB) as well as suppresses sidelobes. Secondly, a baseline distribution optimization processing is proposed to equalize the spatial–temporal baseline distribution, achieve more uniform spectrum samplings, and reduce sidelobes. Thirdly, a clustering-based outlier elimination method is employed to ensure 4D imaging quality. The proposed method is effectively validated through computer simulation and P-band UAV-SAR experiment.

Severe phenomena of across range-Doppler unit (ARDU) and decoherence occur when radar detects high-speed and high-maneuvering targets, causing degradation in detection performance of traditional FFT radar detection methods. The improvement in radar resolution causes a multi-dimensional spread phenomenon, where different scattering centers of the target are distributed on different range units, along with motion parameters such as velocity and acceleration. Unfortunately, current radar detection methods focus solely on range spread targets and cannot handle multi-dimensional spread, leading to a significant decline in detection performance. To overcome this problem, this paper proposes several methods to achieve high detection performance for multi-dimensional spread target detection with ARDU phenomenon. Firstly, the generalized likelihood ratio test (GLRT) is derived, and the energy integration generalized Rayleigh Fourier transform (EI-GRFT) is introduced to improve the detection performance of range spread cross-unit targets. Additionally, the double-threshold based hybrid GRFT (DT-HGRFT) is presented as an enhancement of EI-GRFT, enabling long-time integration along slow time and integration among multiple scatters by using HGRFT and multi-dimensional sliding double-threshold detection, respectively. Furthermore, a method for joint detections of multiple DT-HGRFTs is provided to handle the case where the number of scattering centers of multi-dimensional spread targets is unknown. Finally, a detailed theoretical analysis of the performance of the proposed method is presented, along with extensive simulations and practical experiments to demonstrate the effectiveness of the proposed methods.

Under the constraints of the point scattering model, traditional Synthetic Aperture Radar (SAR) imaging algorithms can be regarded as a mapping from data space to image space. However, most objects in the real scene are extended targets, which are mismatched with the point scattering model in traditional linear imaging algorithms. The abovementioned reasons lead to the distortion of SAR image representation. A common phenomenon is that the extended targets appear as isolated scattered points, which hinder the application of target recognition on the basis of SAR images. SAR parametric nonlinear imaging techniques are established to solve the abovementioned model mismatch problem. Such methods are characterized by the scattering models that consider point targets and extended targets. Specifically, by using the sensitivity of the phase and amplitude characteristics of the echoes or images to the observation angles, SAR parametric imaging methods can first identify the target type and estimate the scattering parameters, and then reconstruct the target image on the basis of the scattering model. SAR parametric imaging methods can obtain better image quality than traditional linear methods for extended targets. This article mainly introduces the parametric imaging methods of linear extended targets, which correspond to the isolated strong points and continuous edges of objects in the real scene, and discusses the parametric imaging methods on the basis of the echo and image domains and experimental results. Last, the future development trends of SAR parametric imaging methods are discussed.

Traditional synthetic aperture radar (SAR) autofocusing methods are based on the point-scattering model, which assumes the scattering phases of a target to be a constant. However, as for the distributed target, especially the arc-scattering target, the scattering phase changes with the observation angles, i.e., its scattering phase is time-varying. Hence, the compensated phases are a mixture of the time-varying scattering phases and the motion error phases in the traditional autofocusing methods, which causes the distributed target to be overfocused as a point target. To solve the problem, in this paper, we propose a SAR parametric autofocusing method with generative adversarial network (SPA-GAN), which establishes a parametric autofocusing framework to obtain the correct focused SAR image of the distributed targets. First, to analyze the reason for the overfocused phenomenon of the distributed target, the parametric motion error model of the fundamental distributed target, i.e., the arc-scattering target, is established. Then, through estimating the target parameters from the defocused SAR image, SPA-GAN can separate the time-varying scattering phases from the motion error phases with the proposed parametric motion error model. Finally, by adopting the traditional autofocusing method directly, SPA-GAN can obtain the correct focused image. Extensive simulations and practical experiments are carried out to demonstrate the effectiveness of the proposed method.

Persistent Scatterer Interferometry (PSI) has been widely used for monitoring land deformation in urban areas with millimeter accuracy. In natural terrain, combining persistent scatterers (PSs) and distributed scatterers (DSs) to jointly estimate deformation, such as SqueeSAR, can enhance PSI results for denser and better coverage. However, the phase quality of a large number of DSs is far inferior to that of PSs, which deteriorates the deformation measurement accuracy. To solve the contradiction between measurement accuracy and coverage, a Bayesian estimation method of land deformation combining PSs and DSs is proposed in this paper. First, a two-level network is introduced into the traditional PSI to deal with PSs and DSs. In the first-level network, the Maximum Likelihood Estimation (MLE) of deformation parameters at PSs and high-quality DSs is obtained accurately. In the secondary-level network, the remaining DSs are connected to the nearest PSs or high-quality DSs, and the deformation parameters are estimated by Maximum A Posteriori (MAP) based on Bayesian theory. Due to the poor phase quality of the remaining DSs, MAP can achieve better estimation results than the MLE based on the spatial correlation of the deformation field. Simulation and Sentinel-1A satellite data results verified the feasibility and reliability of the proposed method. Regularized by the spatial deformation field derived from the high-quality PSs and DSs, the proposed method is expected to achieve robust results even in low-coherence areas, such as rural areas, vegetation coverage areas, or deserts.

Distributed synthetic aperture radar (SAR) is a system in which transmitting or receiving arrays are distributed on multiple platforms or at different locations on one platform. Distributed SAR can be used for high-resolution wide-swath (HRWS) imaging. The typical platform used for distributed SAR is a satellite constellation, which has long baselines and an ideal trajectory. Instead of satellite constellations, this paper focuses on distributed SAR on a single airborne platform, for which the channel error and motion error are coupled. Furthermore, the traditional channel error estimation methods are invalid. Thus, based on the time-domain correlation method (TDCM), this article proposes a joint estimation method of the channel phase error and motion error for the distributed SAR on a single airborne platform. Firstly, a channel error and motion error coupled phase error model of the distributed SAR is constructed. A joint estimation method of the channel phase error and motion error is then proposed. Finally, a simulation and real data processing are provided to demonstrate the effectiveness of the proposed method.

Extremely-high-squint (EHS) geometry of the traditional constant-parameter synthetic aperture radar (SAR) induces non-orthogonal wavenumber spectrum and hence the distortion of point spread function (PSF) in focused images. The method invented to overcome this problem is referred to as new-concept parameter-adjusting SAR. It corrects the PSF distortion by adjusting radar parameters, such as carrier frequency and chirp rate, based on instant data acquisition geometry. In this case, the characteristic of signal is quite different from the constant-parameter SAR and therefore, the traditional imaging algorithms cannot be directly applied for parameter-adjusting SAR imaging. However, the existing imaging algorithm for EHS parameter-adjusting SAR suffers from insufficient accuracy if a high-resolution wide-swath (HRWS) performance is required. Thus, this paper proposes a multi-layer overlapped subaperture algorithm (ML-OSA) for EHS HRWS parameter-adjusting SAR imaging with three main contributions: First, a more accurate signal model with time-varying radar parameters in high-squint geometry is derived. Second, phase errors are compensated with much higher accuracy by implementing multiple layers of coarse-to-fine spatially variant filters. Third, the analytical swath limit of the ML-OSA is derived by considering both the residual errors of signal model and phase compensations. The presented approach is validated via both the point- and extended-target computer simulations.

The point target model is widely used in the synthetic aperture radar (SAR) imaging simulation. Based on this model, the radar echo of a continuously distributed target can be expressed as the linear summation of the echo of many point targets which are known as scatters. However, the discretisation strategy is problematic. The discretisation grid must be very fine to assure convergence, thus the computation burden is heavy. Moreover, the result depends on how the target is discretised. It is shown that the SAR image generated from this model of the same target is inconsistent when using different sampling strategy. To overcome these drawbacks, a parameterised model is proposed in this study, which not only greatly reduce the amount of calculation, but also guarantee consistency of the results. Finally, the correctness of the proposed model is verified by SAR imaging results from the generated echo data.

Deceptive jamming is an effective jamming method for synthetic aperture radar (SAR). Time-delay repeater jamming, which is an important method of deceptive jamming because of its simplicity and effectiveness, can generate false targets in the SAR image and further affect the image interpretation. To detect the false target, this study first establishes the multi-angle SAR jamming signal model. Subsequently, the representations of the false targets and real targets in SAR image are analysed. Based on the aforementioned analysis, a SAR deceptive jamming target detection method based on multi-angle SAR images is proposed. Through the registration of multi-angle SAR images and the change detection, the false targets generated by the time-delay repeater jamming can be detected, and the SAR anti-jamming can be realised correspondingly. Computer simulation verifies the effectiveness of the proposed method.

Due to the influence of air currents and the control of the platform's own power system, acceleration is often present in the motion platform equipped with synthetic aperture radar (SAR) system, which causes the actual trajectory of the platform to be curved. When the acceleration is large or the azimuth accumulation time is long, a large envelope error or phase error will be caused. This problem will be more obvious in highly squint mode SAR. On the other hand, the acceleration may also cause the situation where the absolute value of the Doppler frequency rate is extremely small, resulting in the azimuthal time-bandwidth product that is too small to satisfy the requirements of the stationary phase principle. To solve the above problems, a novel acceleration compensation method based on inertial navigation information is proposed in this article. By integrating the acceleration and velocity information provided by the inertial navigation system, the position information of maneuvering platform is obtained. Then the actual trajectory of the platform is constructed. On this basis, the envelope and phase errors caused by the acceleration are estimated. The results of real data processing verify the correctness of the analysis and the effectiveness of the proposed method.

A ground moving target is always defocused in the synthetic aperture radar (SAR) image domain due to the along-track velocity. In the image domain, a moving target's complex response along the azimuth direction can be modelled as a linear frequency-modulated signal, therefore, its Doppler rate, as well as the along-track velocity, can be estimated based on the recently proposed second-order Wigner–Ville (SoWVD) distribution transform. The proposed method has a low computational complexity without the search procedure and can be well combined with the SAR ground moving target detection algorithms in image domain, where the signal to clutter-plus-noise ratio is higher than other domains. Finally, the effectiveness of this method is verified with some numerical experiments.

The wide swath synthetic aperture radar (SAR) imaging with a fine resolution is fundamentally a contradiction in conventional SAR imaging. To meet the demand of high-resolution width swath (HRWS) imaging, multi-channel scan mode has received a great deal of attention as a promising concept to realise HRWS observation. In this article, based on extended Chirp Scaling algorithm, an imaging algorithm for multi-channel scan SAR is proposed. Furthermore, channel error estimation and compensation are the key point in multi-channel scan imaging and are integrated into the proposed imaging algorithm. The computer simulation is shown to verify the presented imaging algorithm.

Due to the high orbital altitude, the large-area antenna and solar array will be utilised in geosynchronous synthetic aperture radar (GEO SAR) to meet the requirement of high transmitting power. However, the large-area antenna and solar array can easily cause antenna translation vibration (ATV) and further result in paired echo, which will seriously affect the GEO SAR image quality. Existing effect analyses of antenna vibration for low-earth-orbit SAR (LEO SAR) based on the linear trajectory and the corresponding second-order Doppler spectrum cannot be applied in GEO SAR because of its extremely long synthetic aperture time (SAT), curved trajectory, and corresponding high-order Doppler spectrum. To analyse the effects of ATV on GEO SAR image, the ideal echo signal model and the corresponding point spread function (PSF) of GEO SAR are first established and deducted, respectively. Then, the echo signal model and PSF affected by ATV are obtained. Based on the affected PSF, the effects of the ATV on GEO SAR image are analysed in detail, and the basis for subsequent ATV suppression is also obtained. Computer simulations validate the correctness of the analyses shown here.

Wall-penetrating radar (WPR) is the device to image very small objects buried inside the wall. The small objects usually a few millimetres, whereas the bandwidth of WPR limits its resolution up to several centimetres. As a result, the target imaging result of the traditional method is ‘defocused’ in the range direction. To solve this problem, a near-field three-dimensional imaging algorithm based on matching filtering with multiple reference signals and selection processing is proposed. Instead of obtaining a focused image via coherent superposition processing, a focused image is determined by selecting an image from a plurality imaging results of different reference depths according to the minimum entropy criterion. The proposed algorithm is validated by imaging the computer simulation technology simulation data and the measured data.

Inverse synthetic aperture radar (ISAR) is an important tool for non-cooperative ship imaging, and one key step of ISAR imaging is range alignment. To achieve the range alignment with high precision, efficiency and robustness, an improved ISAR range alignment method based on maximum contrast is proposed in this paper, which takes the maximum contrast of average range profile as the optimisation criterion to perform the range alignment. Compared with the minimum entropy method, this method is with lower computation complexity and faster convergence rate. Thus the method proposed in this article is more effective and more robust. Computer simulation and real data experiment validate the effectiveness and robustness of the proposed method.

Synthetic aperture radar (SAR) can provide high-resolution two-dimensional radar images of the target area. The error of the altitude between aircraft and the target area causes geometrical distortion of the SAR ground image, which is unfavourable for scene matching and target recognition. Existing methods, such as radar altimeters, work in the case of vertical beam irradiation. However, the highly accurate aircraft altitude estimation for SAR (under oblique irradiation) is still a challenge. In this paper, an aircraft altitude estimation method based on multi-beam pitching sum and difference channels radar echoes is proposed. The pitching sum and difference channels angle measurement principle is used to achieve accurate beam centre distance estimation, and the cyclic multiple beams are used to realise the estimation of the grazing angle deviation. Therefore, this method can realise the accuracy estimation of the aircraft altitude under oblique beam irradiation conditions. Then, the effects of various error sources on the accuracy of altitude estimation are analysed. The results of simulation and real data verify the correctness of the analysis and the validity of the proposed method.

Owing to the spatial separation of transmitter and receiver, bistatic SAR is able to image the area in front of the moving direction of transmitter or receiver, but the geometry model is more complex than that of monostatic SAR, which brings great difficulties to system design and imaging process. Besides, the special geometry configuration of geostationary spaceborne-airborne bistatic forward-looking SAR results in non-orthogonal and non-uniform ground resolution, which is not considered in the traditional range and azimuth resolution. To solve this problem, the resolution ellipse based on the generalised ambiguity function (GAF) is used to maintain the best and worst ground resolution in the resolution cell. The receiver's flight direction is designed to obtain the optimal uniform ground resolution distribution, which has great value for system design and the receiver's path planning. Finally, the accuracy of the resolution ellipse and correctness of the deduced formulas are verified by simulation.

There are a large number of continuous line targets in the synthetic aperture radar (SAR) images of land scenes. The traditional SAR processing, which makes the continuous line targets becomes two point targets in traditional SAR images, cannot reveal the complete features of continuous line targets and cause enormous difficulties for SAR image interpretation. This study analyses the reason why traditional SAR processing cannot embody the edge characteristics of continuous line targets based on the sidelobe features of the SAR echo. The authors propose a new identification method based on the difference of the echo amplitude between continuous line target and point targets, which will help to interpret the continuous line targets in SAR images. Numerical simulation experiment based on the classical strip SAR validates the effectiveness of the proposed method.

Geosynchronous earth orbit synthetic aperture radar (GEO SAR) has the advantages of short revisit period and wide observation range. However, compared with low earth orbit SAR (LEO SAR), GEO SAR has the distinct characteristics of long synthetic aperture time, large amount of data, curvilinear motion trajectory, slow satellite speed, and long signal return delay etc. Hence, in this paper, a generalisation design technique of real-time quick look imaging systems for both GEO SAR and LEO SAR is first proposed. Then, we present the corresponding system structure and its components. Finally, based on back projection algorithm (BPA), the validity of this quick look imaging system is verified by the simulation.

Through-the-wall radar has been used in rescue missions, behind-the-wall targets detection, surveillance and reconnaissance etc. However, through-the-wall radar encounters two major problems in complex environment. One is that the refraction of the unknown wall would distort the through-the-wall image. The other is that the multi-reflection between wall and walls as well as wall and targets would lead to a lot of fake targets. To solve these problems, a multi-path suppression algorithm is proposed in this study. The simulation results show that proposed algorithm can automatically estimate the thickness and dielectric parameter of the unknown wall and give an undistort image, and remove the fake targets due to the multi-path effect.

The linear structures in synthetic aperture radar (SAR) images can provide important geometric information regarding the illuminated objects. However, the linear structures of various objects sometimes disappear in traditional SAR images, which severely affects the application in automated scene analysis and information extraction techniques. This paper proposes a parametric SAR image recovery method for linear structures of extended targets. By extracting the spatial phase distribution feature in image domain, the proposed method is used to identify the endpoints among all of the scattered points in SAR images and reconstruct the disappeared linear structures based on the scattering model. The method can be generally divided into three procedures: endpoints and point targets classification, linear structures recognition, and linear structures reconstruction. In the first step, the endpoints of linear structures and point targets are classified by setting a threshold related to the spatial phase distribution feature. Afterwards, the linear structures recognition method is used to determine which two endpoints can be formed into a linear structure. Finally, the parametric scattering models are used to reconstruct the disappeared linear structures. Experiments are conducted on both computer simulations and the data that were acquired by microwave anechoic chamber experiment, tower crane radar experiment, and unmanned vehicle radar experiment in order to validate the effectiveness and robustness of the proposed method.

Synthetic aperture radar (SAR) sliding spotlight work mode can achieve high resolutions and wide swath (HRWS) simultaneously by steering the radar antenna beam. This paper aims to obtain well focused images using sliding spotlight mode with the Chinese Gaofen-3 SAR sensor. We proposed an integrated imaging scheme with sliding spotlight echoes. In the imaging scheme, the two-step approach is applied to the spaceborne sliding spotlight SAR imaging algorithm, followed by the Doppler parameter estimation algorithm. The azimuth spectral folding phenomenon is overcome by the two-step approach. The results demonstrate a high Doppler parameter estimation accuracy. The proposed imaging process is accurate and highly efficient for sliding spotlight SAR mode.

In this paper, a modified two-layer scattering model is applied to a three-stage algorithm for high-precision retrieval of forest parameters from Polarimetric Synthetic Aperture Radar Interferometry (Pol-InSAR) data. Traditional Random-Volume-over-Ground (RVoG) model considers forest target as a two-layer combination of flat ground and volumetric canopy. However, when it comes to sloped terrain, the inversion accuracy of three-stage process deteriorates with the ascending estimation error in volume correlation which is mainly caused by the existence of underlying terrain slope. Aiming at this problem, a Range-sloped RVoG (R-RVoG) model is presented in this paper. By modifying the ground layer as a range-sloped plane, the complex correlation of R-RVoG model can be amended as a function of ground phase, ground-to-volume scattering ratio, forest height, mean extinction and range slope. The introduction of range slope variable makes this modified model better resemble to real scene and thus improves the performance of three-stage algorithm. Both of the simulated data with different terrain slopes and the Space-borne Imaging Radar-C (SIR-C) real data in Tianshan test area are processed to verify the validity of this modification.