Taking the advantages of optical camera and synthetic aperture radar (SAR) to perform complementary imaging is a research hot spot in remote sensing image processing, which can be used for target recognition and detection after image registration and fusion. Optical/SAR images acquired by noncommon aperture system have temporal and spatial inconsistencies, there are also significant differences in imaging and radiation mechanism, which make the image-domain alignment process complicated, and the alignment effect and accuracy are also limited. Therefore, this thesis proposes to acquire synchronal optical/SAR heterogeneous data by optics/frequency separation technology under the airborne co-aperture system and puts forward a consistent imaging alignment method based on data domain, which unifies optical photographs and SAR imaging results into the same pixel coordinate system and then establishes the theoretical relationship between the pixel deviation of the two and the position deviation of SAR imaging, finally achieves consistent imaging alignment with higher resolution optical photographs as a reference through the deviation compensation during SAR imaging process. Experimental simulations verified the feasibility of achieving consistent imaging alignment of optical camera/SAR within one pixel in distance and two pixels in azimuth based on SAR imaging compensation, which opens up a new direction for the enhancement of imaging processing efficiency of heterogeneous data and high-precision image processing.

In this letter, an Inverse synthetic aperture radar (ISAR) imaging method uses second harmonic signals to extract motion parameters of multi-rotor autonomous aerial vehicles in an environment containing other natural objects for high-precision ISAR imaging. The core of the drones, which include powerful radio frequency (RF) circuits, is proven to have strong nonlinear effects. Translational motion parameters are estimated from prominent points in second harmonic signals, and the rotational motion of the drone is corrected by instantaneous imaging. An integrated RF system commonly used in drones has been tested, and its nonlinear phase characteristics have been obtained. The real phase error due to the nonlinearity was carried over into the subsequent simulation and compensated, which did not lead to defocusing in the final image. The feasibility of using second harmonic signals to compensate for fundamental frequency motion errors is verified by simulations. The micro-Doppler effect, generated by the high-speed rotation of the blades, has been demonstrated in the simulation images. System integration with a harmonic signal-based motion correction (H-MC) approach for motion compensation is shown to demonstrate a solution for accurate imaging for drones.

Synthetic aperture radar (SAR) plays a crucial role in remote sensing because of its ability to operate in all weather conditions, both day and night. The traditional FFT-based SAR imaging algorithm suffers from severe speckle noise, which is almost inevitable owing to the coherent nature of the SAR system. Recently, the plug-and-play (PnP) SAR imaging method uses a plug-in denoiser as an image prior function to regularize the resulting image, thus suppressing speckle noise while maintaining the useful features of target objects. However, the existing plug-in denoisers used in statistical SAR imaging, either handcrafted or data-driven, are insufficient for complex remote sensing scenarios. More powerful image priors, such as the deep generative model for unconditional image generation, would be a better alternative regularizer for statistical SAR imaging. However, the most powerful diffusion generative model lacks an explicit latent space for conditional optimization to be adopted for SAR imaging from received signals. We propose a novel SAR imaging method based on conditional generation of a diffusion model. In detail, we embed the maximum a posteriori (MAP) formulation of SAR imaging from the received signal as a conditional guidance for diffusion generation, which overcomes the lack of latent space shortage. Compared with these statistical methods, our proposed methods exhibit exceedingly high performance both on simulated experiments and returned data imaging from RadarSat SAR data.

Synthetic aperture radar (SAR) is a widely used active imaging system for remote sensing applications. However, traditional signal processing-based SAR imaging algorithms suffer from coherent speckle problems. Recently, statistical SAR imaging methods such as the FESAR model and plug-and-play (PnP) SAR imaging methods have been applied to suppress the speckle phenomenon. However, they are sometimes unstable and require an elaborate hyperparameter adjustment strategy during the iteration process. We propose extending PnP statistical imaging with Langevin sampling, called the Langevin-PnP algorithm. To construct the Langevin-PnP algorithm, we provide an in-depth analysis of the PnP framework and incorporate Langevin dynamics into its iteration trajectory. We also present a convergence guarantee for Langevin-PnP because the injected stochasticity affects the convergence condition under the law of probability. The experimental results showed that our proposed Langevin-PnP maintained the best performance over other statistical imaging methods, both in the simulated experiments and the RadarSat-SAR data experiments.

Frequency modulation continuous wave (FMCW) synthetic aperture ladar (SAL) system can support the generation and processing of large bandwidth signals, which has strong capabilities in target detection, description and identification. However, frequency modulation nonlinearity of the transmitted signal has become a bottleneck problem that restricts the high-resolution ranging of FMCW SAL. This letter establishes an echo model with nonlinearity, and proposes a nonlinear phase estimation and compensation method based on Wigner-Ville distribution (WVD) with gradient rotation enhancement. Firstly, we establish the nonlinearity model of the internal calibration signal in time-frequency (TF) domain, and use WVD to calculate the time-frequency distribution (TFD). Secondly, the TFD is enhanced by rotating gradient vector, which also can eliminate noises and false components in the TFD. Then, the nonlinear phase of the transmitted signal is estimated by establishing a nonlinearity model in the TF domain. Finally, the residual video phase (RVP) filtering is introduced to eliminate the range-dependent nonlinearity and achieve nonlinearity correction of echoes at any range. Experiments and real data tests show that the method proposed in this letter can accurately estimate and correct the nonlinearity of the dechirp signal, which effectively improves the range resolution of FMCW SAL.

Airborne multichannel circular stripmap synthetic aperture radar (CSSAR)-ground moving target indication (GMTI) has drawn increasing attention in wide-area surveillance, reconnaissance, and traffic monitoring due to its short revisit times. In this article, a novel scheme for CSSAR-GMTI systems is proposed to offer high-resolution focusing of moving targets and enable efficient detection and accurate parameter estimation, which are ensured by integrating moving target imaging with a space–time adaptive processing (STAP) clutter suppression step. The presented scheme develops a target imaging algorithm that forms SAR images focused on a particular 2-D motion parameter to maximize target signal energy and recover its high resolution. Moreover, benefiting from the suitable phase compensation designed in the 2-D frequency domain, moving targets can finally be properly focused in the SAR image without displacement, and their parameters can be uniquely determined. Experiments on an emulated radar dataset are conducted to validate the effectiveness of the proposed scheme.

This letter proposes a machine learning inversion scheme for P-band polarimetric interferometric synthetic aperture radar (Pol-InSAR), which can achieve the single-baseline random volume over ground (RVoG) model inversion without the assumption of the null ground-to-volume ratio. First, the potential variables—including the incidence angle and the PDHigh coherence acquired with phase diversity optimization—that are related to the forest vertical structure are analyzed for their correlations with the extinction coefficient in the RVoG model. Then, the machine learning approach is applied to forecast the extinction coefficient characterized by those potential variables. Ultimately, in the case of fixing the extinction coefficient, the forest height is estimated by a geometric process on the complex plane. The actual Pol-InSAR data verification illustrates that the inversion performance of the proposed scheme overmatches that of the traditional schemes.

This letter focuses on the detection of subtle human activity, from multipass airborne interferometric synthetic aperture radar (InSAR) images, of such activities causing absolute decorrelation between two acquisitions. These activities can be vehicle track, footprint, grazing, and human construction. The millimetric sensitivity of the interferometric phase makes it valuable for detecting such activities. It can bring a large value to civil and military applications. However, there are always high false alarms in the conventional detection method. To solve this problem, a human activity detection method based on the coherence matrix is approached in this letter. In the final part of this letter, a preliminary validation of the method is provided by processing actual multipass airborne InSAR data stacks acquired by the Aerospace Information Research Institute, Chinese Academy of Sciences. It is noted that the proposed method reaches reliable results in comparison with the ground truth.

Frequency-modulated continuous wave synthetic aperture ladar (FMCW-SAL) is an important remote sensing observation method. Sliding spotlight FMCW-SAL can obtain high resolution and wide observation coverage in azimuth simultaneously. This work models and simulates sliding spotlight FMCW-SAL signal. FMCW-SAL beam scanning leads to spectrum aliasing in the azimuth direction, and the linear and second-order position offset errors are introduced in the range direction. Compared with sliding spotlight FMCW synthetic aperture radar (SAR), the linear position offset error is more serious and must be corrected. However, there is no linear position offset correction in previous research studies in sliding spotlight FMCW-SAR. An improved sliding spotlight FMCW-SAL imaging algorithm is proposed for the above problems here. This algorithm includes two new phase compensation factors, which can remove Doppler ambiguity and correct first-order, second-order position offset errors in range simultaneously. Simulation experiments verify the effectiveness of the signal model and the imaging algorithm.

Airborne multichannel high-resolution radar (HRR)-ground moving target indication (GMTI) is of great significance to wide-area surveillance, traffic monitoring and target recognition. The Aerospace Information Research Institute, Chinese Academy of Sciences, produced an advanced airborne digital array radar with high resolution and conducted an experiment with slow and weak cooperative moving targets in 2021. In this paper, an overall processing framework for target detection, parameter estimation and target tracking using this system is introduced. For HRR detection, the echo energy of targets is spread into multiple range units, so-called range-spread targets; thus, using detectors designed for point-like targets will severely degrade the detection performance, especially for slow and weak targets. To address the adaptive detection of such range-spread targets embedded in Gaussian clutter with an unknown covariance matrix, a novel two-step generalized space-time adaptive processing (GSTAP) algorithm is proposed, which offers an enhanced clutter suppression capability compared with natural competitors. Moreover, a tracking method is implemented in the range-Doppler domain to avoid track loss caused by azimuth relocation error and reject discrete false alarms. Both simulation and experimental results are presented to demonstrate the effectiveness of the proposed method and provide a paradigm for further research.

The polarimetric interferometric synthetic aperture radar (Pol-InSAR) model under P-band observations exhibits vertical structure diversity. Compared with the exponential-based random volume over ground (RVoG) model, the Gaussian vertical backscatter volume over ground (GVBVoG) model expresses a more complex forest vertical structure via introducing more parameters. On account of the influence of topographic fluctuation on the model, this article establishes the sloped Gaussian vertical backscatter volume over ground (SGVBVoG) model by drawing into the terrain slope. Based upon the SGVBVoG model, this article develops the 2-D SGVBVoG (2-D-SGVBVoG) model by defining the structure factor, which effectively reduces the model complexity from three to two dimensions. In the 2-D-SGVBVoG model inversion, in view of the diversity of forest species, age, shape, density, etc., in the natural scene and the variation of specific radar systems, a structure factor prediction scheme relying on machine learning is proposed. In the machine-learning model training, the radar incidence angle and the PDHigh coherence acquired by coherence optimization with terrain phase removal are utilized as the variables for characterizing the structure factor. Ultimately, in the case of fixing structure factor, a geometric inversion process on the complex plane is put forward to extract the forest height. The BIOSAR 2008 P-band Pol-InSAR data validation shows that the proposed method achieves an RMSE of 3.07 m, which is 24.0% better than the three-baseline SRVoG inversion.

With the exploration of synthetic aperture radar (SAR), the requirements for its functionality, precision, and response time are inevitably increasing, in which the technical core is the generation, reception, and processing of wideband signals with low time cost. Owing to the excellent performance of modern photonics, such as the ultra-wide bandwidth (UWB), flat response, low transmission loss, fast analog signal processing, and microwave photonics (MWP) promises to be an appropriate solution to improve the capability of SAR in resolution, coverage, and efficiency. However, on the one hand, due to the high sensitivity of optical fiber, moisture, temperature, or physical vibration can produce an unknown extra propagation delay. On the other hand, the wavelength shift effect should be taken into consideration for the UWB system. Thereby, under very high resolution (VHR) circumstance, two-dimensional (2-D) defocus including migration through resolution cells (MTRC) and azimuth phase error (APE) becomes a challenge for MWP SAR imaging. Unfortunately, existing 2-D autofocus approaches concerning motion errors inherently fail for the commonly underlying assumption that the prominent nonsystematic residual range cell migration (RCM) is global in the azimuth time domain. In this article, we analyze the effects of the above-mentioned negative factors in the image processing of range migration algorithm (RMA) and reveal the structural characteristics of the 2-D phase errors. A novel two-step postprocessing compensation strategy is developed, and experiments on real data acquired by an airborne MWP SAR system demonstrate its effectiveness.

Synthetic aperture radar (SAR) installed on unmanned aerial vehicles (UAVs) has drawn increasing attention in the area of remote sensing due to its high spatial resolution and low energy consumption. However, due to the sensitivity toward atmospheric turbulences, strong motion errors can defocus the image in both azimuth and range directions. In this article, based on symmetric triangular linear frequency-modulation continuous microwave (STLFMCW) signals, a novel motion error estimation method is proposed. The core concept is to eliminate the effect of residual range cell migration (RCM) and investigate the motion parameters from azimuth phase history by range-frequency-domain interferometry between the up-ramp and down-ramp chirp sections. With preknowledge of the structural characteristics of the differential signal, we first obtain the high-order component, then estimate the quadratic term with an azimuth profile width minimization (APWM) algorithm on the basis of bisection search, and subsequently remove the linear part through phase gradient matching and joining. Consequently, the desired motion errors can be achieved purely based on raw data. Simulations demonstrate that our proposed algorithm can achieve high accuracy with or without standard prominent point targets. Experimental results on a W-band UAV SAR system in strip-map mode indicate that this approach also outperforms other existing postprocessing autofocus methods.

The frequency modulation continuous wave (FMCW) Laser radar (Ladar) inevitably suffers from vibration and nonlinear modulation, which will reduce ranging accuracy and imaging resolution. We propose a vibration and nonlinearity compensation method using particle filtering (PF) for one- period triangular frequency modulation continuous wave (T-FMCW) signals. We first extend the traditional ranging model to an instantaneous ranging model by a second-order synchro-squeezing transform (SST) which can characterize the local distributions of time-varying signals. We then eliminate the nonlinearity errors from the instantaneous measurement ranges by setting an auxiliary channel. Finally, we built a particle filtering (PF) model using the instantaneous ranges to compensate for the vibration and the residual nonlinearity errors, and estimate the range of target by using the triangular relations of T -FMCW. Experimental tests prove that the proposed method can accurately estimate the range of target by simultaneously compensating for the vibration and nonlinearity errors in one-period T -FMCW.

The application of miniature unmanned aerial platforms has been growing in popularity whether in military reconnaissance or in civilian monitoring systems over the past few years. Installation of synthetic aperture radar (SAR) on board of unmanned aerial vehicle (UAV) is a high-efficient but low-cost remote sensing technology. However, UAV is sensitive to atmospheric turbulences and it may not carry high-accuracy inertial navigation systems (INS) and global positioning system (GPS). These make Mini SAR imagery a challenge, and motion compensation (MoCo) a crucial task. This paper is aimed at studying on the MoCo technology for Mini SAR mounted on UAV. Practical data processing is presented to primarily demonstrate the validity of our proposed approach.

Frequency modulation continuous wave (FMCW) laser radar (Ladar) is a new radar system suitable for long-range detection and high-resolution imaging. However, the transmitted signal inevitably suffers from nonlinear frequency modulation errors which reduce the quality of Ladar imaging. We propose a nonlinear phase estimation and compensation method based on synchrosqueezing wavelet transform (SST). We first use SST to synchrosqueeze the dechirp signal containing a reference range in time-frequency domain and obtain the time-frequency information of the reference dechirp signal. As SST could aggregate the time-frequency distribution of noise, the proposed method is effective even in a noisy environment. A nonlinearity model in time-frequency domain is then built to estimate the nonlinear phase of the transmitted signals by using the time-frequency information of the reference dechirp signal. For the case of long-range detection, we adopt a residual video phase filtering to convert the nonlinear phase of the dechirp signal to range-independent phase errors. Finally, the nonlinearity of the dechirp signal is compensated using the estimated nonlinear phase of the transmitted signals. The experimental and real data tests show that the proposed method effectively improves the resolution of long-range Ladar imaging by compensating for the nonlinearity of the dechirp signal. Its advantage is the effectiveness for noisy and multicomponent FMCW Ladar signals.

Frequency modulation continuous wave (FMCW) Laser radar (Ladar) signals inevitably suffer from nonlinear frequency modulation errors which reduce the quality of Ladar imaging. We propose a time-frequency domain nonlinear phase compensation method based on synchro-squeezing wavelet transform (SST). We first obtain the time-frequency information of the dechirp signal containing a reference range using SST. A nonlinearity model in time-frequency domain is then proposed to estimate the nonlinear phase of the transmitted signals by using the time-frequency information of the reference signal. Finally, the nonlinearity of the dechirp signal is compensated by using the estimated nonlinear phase of the transmitted signals. Numerical tests verify that the proposed method has an advantage of compensating for the nonlinear phase of noisy and multicomponent FMCW Ladar signals.

Frequency-modulation continuous-wave (FMCW) Laser radar (Ladar) signals may be polluted by noise, which reduces the recognition precision of the targets. This letter proposes an ensemble empirical modal decomposition (EEMD) denoising method with singular spectrum constraint for FMCW Ladar signals. In our approach, we apply EEMD to adaptively decompose the noisy FMCW Ladar signals into several intrinsic mode functions (IMFs) and decompose these IMFs into several singular value components by using the singular spectrum analysis. The singular values are then used to calculate the energy probability of each IMF, which serves as an indicator to detect the IMFs containing useful signals. The energy probability represents the coherence of the IMFs such that we could sort these IMFs into noisy and useful components according to their coherence differences. To suppress the residual noise that is interfered with the selected IMFs, we use a low-rank approximation to reconstruct the useful signals. Finally, the reconstructed IMFs are stacked together to obtain the denoised signals. Tests on synthetic and real data demonstrate that the proposed method, compared to the EEMD denoising method, could suppress more noise but filter out less useful signals in the FMCW Ladar signal denoising.

SAR/INS navigation system utilizes SAR radar as an additional sensor to provide the platform trajectory position and compensate an aircraft drift due to Inertial Measurement Unit(IMU) errors. Images obtained by SAR radar are matched with a digital landmark Data. When no landmarks are available, this article presents a novel method of InSAR/INS integrated navigation system based on interferogram matching. Interferogram contains total terrain information and hardly affected by seasonal variations of features compared with SAR/INS system. Interferogram from actual InSAR real time processing and interferogram simulated by additional DEM are matched and platform position and attitude inversion model is constructed to compensate INS drift errors. Real-data experiments show that the approach can compensate INS drift errors well when GPS signal is lost.

A linear frequency modulation continuous-wave mini-synthetic aperture radar (SAR) system mounted on small aircrafts promises a high-flexibility and cost-effective microwave remote sensing technology. However, the mini-SAR system suffers from considerable trajectory deviations due to aircraft's lightweight, low flight height, and limited capacity for a high-accuracy inertial measurement unit (IMU). With the rapid increasing requirements for resolution, the residual range cell migration (RCM) exceeds a single range cell. Under such circumstances, traditional autofocus algorithms fail to guarantee well-focused mini-SAR images and further accurate interferometric SAR (InSAR) applications. To solve these problems, this paper proposed a novel residual RCM correction scheme for a mini-SAR system mounted on small aircrafts without high-accuracy IMU. The core idea is to estimate the misalignments of adjacent range profiles based on fast-time split-band signal interferometry at each azimuth time and integrate them with time to obtain an estimation of residual RCM. The proposed method promises a high-accuracy misalignment estimation result without resampling operation in the traditional cross-correlation methods. Simulation and experimental results show the improvement of mini-SAR image focusing quality and refinement of coherence map between the master and slave images for InSAR applications, which demonstrated the effectiveness and reliability of our proposed residual RCM correction scheme for the mini-SAR system.

The combination of interference and circular SAR (CSAR) can reduce the influence of high sidelobes in the vertical direction of CSAR while retaining the merits of CSAR, such as subwavelength resolution and 3D imaging capability. However, C-band repeated pass interference is difficult due to short wavelength and decorrelation. This paper introduces interferometric processing of CSAR using fully polarimetric C-band circular data which is acquired over a test site in Zhuhai, China. The phase of 2D CSAR images obtained at two different platform height in the same subaperture is different. And the interferogram is acquired based on the phase difference. The experiment verifies the feasibility of repeated pass interference model for CSAR at C-band.

In this paper, to address problems in the registration of synthetic aperture radar (SAR) and optical images due to large gray differences, the scale-invariant feature transform (SIFT) approach based on phase congruency (PC-SIFT) is proposed. This approach is used to address the gradient inversion in multi-source images, and it is based on optimizing the dominant direction interval of the descriptors. We construct a new descriptor by combining phase consistency and the gradient amplitude, which is referred to as PCG-SIFT descriptor. The proposed algorithm is suitable for multi-sensor images with large gray differences and significant edge features The results of experiments show that compared to the traditional gradient-based SIFT descriptor, the PC-SIFT descriptor and PCG-SIFT descriptor improve the robustness and matching probability of the registration algorithm for multi-source images.

This work proposes a forest model based on Gamma distribution to better describe the forest vertical structure for height inversion using P-band polarimetric synthetic aperture radar interferometry (Pol-InSAR) data. The proposed model takes into account the forest vertical heterogeneity and asymmetry, to which volume interferometric coherence is sensitive. The interferometric coherence associated with a volume where the vertical backscattered power varies following a Gamma distribution is derived. The effect of scattering center height standard deviation and mean elevation to the volume interferometric coherence is investigated. Finally, the strategy of multi-baseline on the proposed model for forest height inversion using P-band Pol-InSAR data is proposed.

The performance of FMCW InSAR system mounted on small platforms can be considerably degraded by trajectory and attitude errors, which are mainly caused by platform's small size, low flight height and atmospheric turbulance. Moreover, we cannot use large-volume, heavy-weight and high-accuracy inertial measurement units due to the limit of platform's capability. In view of this problem, we proposed a novel yaw and pitch error estimation method using range-variant Doppler centroid to improve the accuracy of INS. Firstly, apply range compression and range cell migration correction to raw data using low-accuracy INS/DGPS data. Secondly, estimate Doppler centroid for each range gate. Thirdly, extract yaw and pitch error using total least square according to the range-variant model between Doppler centroid and atitude error. The implementation of proposed method on experimental FMCW InSAR data validates the effectiveness and reliability of it.

For the mini-interferometric synthetic aperture radar system mounted on small aircraft or unmanned aerial vehicles, yaw and pitch angle deviations can be considerably high due to their small size and atmospheric turbulence. Moreover, we cannot install a large-volume, heavy-weight, and high-cost inertial navigation system limited by the aircraft's carrying capacity and system cost. In view of the problem, this letter proposes a robust yaw and pitch angle estimation method based on the relationship between range-variant Doppler centroid and attitude angles. For each azimuth moment, estimate the range-variant Doppler centroid for each range gate and solve the range-variant Doppler centroid model using a total least squares method to obtain a robust yaw and pitch angle estimation result. The comparison of the estimated and recorded yaw and pitch angles by a high-accuracy position and orientation system validated the effectiveness and reliability of our proposed yaw and pitch angle estimation method.

Due to the lack of accuracy in the navigation system, deviations in the order of centimeters between the real trajectory and the measured one, called residual motion errors (RMEs), frequently appear in synthetic aperture radar (SAR) images. They usually cause azimuth defocusing and phase errors. For very high-resolution SAR imaging and repeat-pass SAR interferometry, the RME must be estimated and compensated. In the literature, the algorithms used to estimate such errors are mainly through interferometry. The error difference for the SAR image pair is calculated, and then the correction is applied either on one image, or alternatively fractions of it on both images. However, the accuracy of this approach is greatly deteriorated by the decorrelation and ground movements. In this paper, we associate the phase difference between two adjacent subaperture images with the second derivative of the RME, and propose a new algorithm to estimate the RME for individual SAR image. It exploits point-like targets distributed along the azimuth direction, and not only corrects the phase error, but also improves the azimuth focusing. Therefore, besides in airborne repeat-pass interferometry, the algorithm can also be applied in very high-resolution SAR imaging. Simulated and real SAR data are used to demonstrate its feasibility and accuracy. Its limitations and extensions are also discussed at the end of the paper.

In order to reduce the complex image registration process for the airborne dual-antenna interferometric SAR (InSAR) system, a new auto-registration imaging algorithm is presented in the paper, which implements the registration step directly at the SAR raw data processing stage based on the EOK algorithm and the ECS algorithm. For the mismatch caused by the path difference of two antennas, it realizes the precision registration of interferometric image in the range by means of high degree polynomial approximation using scaling principle and shifting compensation in course of the imaging procedure. Finally, through the processing of the real interferometric data, better interferograms are obtained and the validity of the algorithm proposed in the paper is proved.

The first airborne Circular SAR data acquisition experiment of China was carried out in Sichuan by the National Key Laboratory of Science and Technology on Microwave Imaging (MITL), China. Data was acquired using the MITL's P-band, fully polarimetric SAR system along a circular trajectory with the beam spotted on the same area. Compared with the conventional SAR along a straight path, this imaging mode mainly has the following attractive features. First, observing from all directions can help for a better understanding of the scattering properties of targets. Second, the wide angular aperture obtained via flight in a circular track makes possible high resolutions with low frequency band. Third, the aspect angle diversity inherent to the circular track allows for a 3-D target reconstruction. This paper presents the SAR processing of such data, and shows several results to analyze the potentials and limitations of such imaging geometry.

The accuracy of the digital elevation model (DEM) generated by the interferometric synthetic aperture radar (SAR) partly depends on the accuracy of system parameters, so it is necessary to calibrate the system parameters. The traditional calibration method models the elevation error as a linear function of parameter biases, and solves the biases through the sensitivity equations. This paper presents a weighted calibration method applicable to interferometric SAR data. It introduces the weightings to the sensitivity equations to discriminate the ground control points (GCPs) with different correlation coefficients and locations. This weighted calibration method can improve the DEM accuracy, and this paper illustrates its successful application to airborne interferometric SAR data collected by Institute of Electronics, Chinese Academy of Sciences.

The nadir track of geosynchronous satellite can be “figure-8”, circular or ellipse track which could be produced with appropriate orbit parameters. In this paper, the characteristics of the interferometric deformation detection for geosynchronous SAR with circular aperture are analyzed. The connection between the interferometric phase and the deformation of repeat pass geosynchronous SAR is derived, and its potential to get three-dimensional 3D deformation measurement is interpreted.

In airborne two-antenna interferometric SAR system, the returned pulse may be reflected by parts of the aircraft platform into the antennas, and this is called multipath effect which will cause oscillating phase errors and hence height errors. This paper presents a theoretical model to compute the multipath error. In this model the multipath phase error is a function of look angle or ideal phase, and the unknown parameters of the model can be estimated from distributed targets with known elevation. On the basis of the model, a method and processing procedure can be used to correct multipath error effectively, and this paper illustrates its successful application to interferometric SAR data collected by Institute of Electronics, Chinese Academy of Sciences.

A novel approach for the highly squinted airborne InSAR data processing is presented. Using the IMU data to resolve the PRF ambiguity and moving azimuth windows according to the Doppler centroid varying in the different range, as well as combining the auto-registration imaging algorithm, such approach not only can compensate the squint effect and motion error directly at the imaging processing stage, but also can improve the coherence and restrain the interferometric phase error of the image-pair. The simulative and practical results indicate that the proposed approach is very suitable for the processing of the data with a high squint for a dual-antenna airborne InSAR system with its efficiency in improving the image quality and enhancing the interferogram and coherence.

This paper describes a scalable parallel method for interferometric synthetic aperture radar (InSAR) raw data simulation on graphic processing unit (GPU) with common unified device architecture (CUDA). The advantages of the new method rely on the three contributions: GPU hardware provides lots of stream processors for threads calculating, CUDA software environment runs thousands of threads working in parallel for assigned task, raw data simulation adopts the fine-grained task parallelism. Compared with OpenMP, MPI and grid computing, the method not only improves the computational efficiency greatly, but also save the resources such as hardware, electric power and room space. The results show that the method not only ensures accuracy, but also be able to obtain the speedup about 30 times.

An efficient SAR raw signal simulator accounting for antenna attitude variations is presented here, based on the 2-demensional Fourier domain formulation of SAR raw signal in presence of antenna beam pointing errors. It can meet the requirements of InSAR system simulation to deal with the extended scenes. The validity limit is analyzed to show that this algorithm is suit for the simulation of practical systems.

In this paper we present a raw signal simulator based on GPU parallel computation for synthetic aperture radar. We describe a mathematical model of SAR simulation based on FFT in detail and implement it through GPU parallel computation. GPU has a better performance in complex calculation than CPU. It supports parallel computation and raises the speed of algorithms. At the last part of this paper, a simulation comparison is given. The result shows that the simulator base on GPU parallel computation improves the efficiency of algorithm. It is very useful when the algorithm consumes large amount of CPU time.

Aimed at the interferometric calibration problem for the Dual-antenna Airborne InSAR, Considering Ground Control Points(GCPs) are limited, calibration site of area with same height such as flat terrain is advanced. A scheme of establishing parameters bias and building Digital Elevation Model (DEM) is designed. Some airborne InSAR data, derived by Institute Of Electronics, Chinese Academy Of Sciences(IECAS), were used to do calibration experiments with the proposed processor. Their results demonstrated it is efficient.

In this paper, we introduce a new method to locate the remote images. By using the Geographic Information System(GIS) resources, we can extract the coastlines information. At the same time, by using image processing technologies, we can also extract the similar information from remote images. Based on the image matching technologies, we can roughly locate the remote images. Some simulation experiments are implemented on remote sensing images with coastlines. Preliminary results verify the feasibility of the technique.