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This letter proposes a new method for focusing ground maneuvering targets and estimating the motion parameters with a synthetic aperture radar (SAR) system. In this method, the Hough transform is applied to estimate the cross-track velocity from the slope of the range walk (RW) trajectory, and the RW and Doppler centroid shift are compensated. The second-order Keystone transform is performed to correct the additional range curve caused by the along-track velocity and cross-track acceleration. Then, we adopt the adaptive polynomial Fourier transform to estimate the second- and third-order Doppler parameters from a 1-D parameter interval, and the corresponding motion parameters are calculated. Finally, the moving target is well focused after the motion parameters compensation because the second- and third-order Doppler parameters are efficiently eliminated. Both the simulated and real data processing results are presented to demonstrate the validity of the proposed algorithm.

In this letter, a new technique based on genetic algorithm and support vector machine (GA-SVM) is proposed to effectively estimate the 3-D deformations of the earth's surface by integrating sparse global navigation satellite system (GNSS) deformation measurements and interferometric synthetic aperture radar (InSAR) maps. The genetic algorithm (GA) is used to search the optimal supported vector machine (SVM) control parameters, considering the control parameters have an important influence on the prediction. Based on advanced machine learning theory, the proposed method has at least two main advantages over traditional methods: 1) it does not need to preinterpolate the displacements of GNSS points, and 2) it does not need to estimate the variance components of GNSS and InSAR point by point. Both the simulated and real experiments are implemented to prove the effectiveness of GA-SVM. In the real case of the Los Angeles, the root mean square errors of GA-SVM at 14 checkpoints are 7.92, 2.05, and 5.43 mm/a in the east-west, north-south, and vertical directions, respectively.

Spaceborne synthetic aperture radar (SAR) can operate at various modes, including stripmap mode, spotlight mode, sliding spotlight mode, and Terrain observation by progressive scans (TOPS) mode. These four imaging modes can be regarded as unified, differing in rotation-center ranges. To uniformly focus the data of these four imaging modes in real-time, this article proposes a real-time unified focusing algorithm (RT-UFA) for the multi-mode SAR via azimuth sub-aperture complex-valued image combining and scaling. The imaging processing can be performed while the data are being recorded. In the first stage of imaging, sub-aperture complex-valued images with relative low-resolution can be obtained by the cascade of the extended chirp scaling (ECS) and azimuth dechirp. Then, these complex-valued images are coherently combined by shifting the integer number of pixels, and thus the full-resolution image of all the recorded data can be obtained. The azimuth scaling and the pixels shifting in the RT-UFA are analyzed in detail. Simulation and SAR data results are presented to validate the analysis and RT-UFA.

Considering that synthetic aperture radar (SAR) images obtained directly after signal processing are in the form of complex matrices, we propose a complex convolutional network for SAR target recognition. In this article, we give a brief introduction to complex convolutional networks and compare them with the real counterpart. A complex activation function is applied to analyze the influence of phase information in complex neural networks. Inspired by the theory of network visualization, a special kind of transfer learning based on the electromagnetic property from the attributed scattering center model is applied in our networks to modulate the first convolutional layer. The experiment shows a better performance in terms of classification accuracy compared to random weight initialization.

Automatic target recognition has been one of the hottest research in synthetic aperture radar (SAR) data processing. Noticing that popular recognition methods cannot utilize multiple features of SAR complex data, a method fused scattering center feature and deep convolutional neural network (CNN) feature is proposed in this letter. This method contains three key parts, namely, scattering center extraction and reconstruction block, CNN feature extraction block, and final feature fusion and classification block. In this process, the scattering center feature and CNN feature are fused at the level of feature maps, which retain the space information of 2-D feature maps. What is more, the proposed half end-to-end strategy realizes the automatic update of weighting parameters in feature extraction network and subnetwork, which promotes a better recognition efficiency. Experimental results on measured SAR data show that the proposed method can achieve better accuracy than other single feature-based methods and feature fusion methods.

Multiple azimuth channels (MACs) synthetic aperture radar (SAR) can theoretically achieve high azimuth resolution and wide swath (HRWS). Nevertheless, in practice, channel mismatch will lead to ghost or azimuth ambiguities, which will degrade the imaging quality. This article proposes a novel idea for estimating the channel mismatch of MACs SAR in the image domain. First, we found that the degree of freedom (DOF) of MACs signals doubles after signal reconstruction and imaging. As a result, when the channel number is not great enough, the subspace method for error estimation is unable to be implemented. To deal with this problem, we introduce a DOF compression method based on spectral filtering. This method can decrease the image-domain DOF. Finally, an image-domain subspace method is proposed to estimate the channel phase error, using the focused data and selecting the high SNR region of SAR images. The proposed method has advantages for the channel phase error estimation. Simulated space-borne MACs SAR data and real measured airborne SAR data are processed to demonstrate the effectiveness of the proposed method.

With the ability to locate subtle trace objects in the large-scale region, coherent change detection (CCD) has been vital research for a synthetic aperture radar (SAR) system. Finding the difference between repeat-pass repeat-geometry SAR image pair and extracting impressive trace pixels from difference image, the SAR CCD methods consist of a difference generation module and a difference analysis module. The previous CCD methods mainly pay attention to devising a sophisticated working system or an appropriate statistic model to generalize a well difference image. In this article, we introduce the deep learning method into the CCD algorithm and propose a novel trace detection paradigm, which works by hierarchically fusing the unsupervised coherent statistics model and supervised deep learning model. To be specific, the complex reflectance change detection estimator is introduced to generate a difference image and reduce the false alarm in the low clutter-to-noise region. Since the low correlation in a difference image caused by the natural factors severely affects the detection performance, the multiple statistics based on intensity summation and intensity difference are, respectively, proposed to extract water region and vegetation region and suppress the corresponding false alarm. Then the construction of the coarse-to-fine image makes use of land cover information and trace features while the compressed Unet improves the utilization efficiency of trace samples. Meanwhile, the inductive transfer learning based on unsupervised pretraining and few labeled trace samples helps to train a well detection model. Experiments on measured SAR data demonstrate the effectiveness of proposed methods.

High resolution and wide-swath imaging always suffer channel errors of the multiple azimuth channels (MACs) SAR. This paper presents an image-domain channel error estimation algorithm based on image subspace least square (ISPLS) method, and a post-imaging reconstruction algorithm for MACs SAR. The proposed method mainly consists of three parts: firstly, preprocessing and SAR imaging; secondly, the ISP-LSbased channel error estimation and calibration algorithm; thirdly, post-imaging reconstruction and ambiguity suppression. The channel phase and baseline errors are joint-estimated based on image subspace after SAR imaging, providing advantages that the higher SNR regions SAR images and the subspace method can be used to achieve a more accurate estimate with a relatively low computational load. We also propose a post-imaging reconstruction method for ambiguity suppression, which can realize imaging each channel data and then combining the multichannel SAR images. Simulated and acquired airborne SAR data are processed to demonstrate the effectiveness of the proposed method.

As an important remote sensing means, synthetic aperture radar (SAR) has many superiorities to other sensors. How to effectively detect and locate ships in SAR images is also a popular field. In previous ship detection research, most algorithms focus on detecting the horizontal bounding box of ship targets, which ignore the rotation angle of each ships. Thus, too much background noise in the horizontal detection results makes them difficult to describe each ship accurately. Inspired by the powerful feature representation ability of convolutional neural networks (CNNs), a novel anchor-free and keypoint-based deep learning method is proposed for oriented ship detection in multiresolution SAR images. Our detector first extracts multilevel features from the input SAR image with a backbone network and feature pyramid network. Next, considering multiscale ships in multiresolution SAR images, we detect different sizes of ships on different levels of feature maps with identical head network structures. In each head network, the classification subnetwork determines each pixel in feature maps as the central pixel of this ship or not, and the regression subnetwork regresses the oriented bounding box for each ship. In the training process, the proposed oriented nonnormalized Gaussian function is used to describe the center point of ship targets, while the nonuniform weighting of the different level loss functions is used to suppress the imbalanced sample distribution. Experimental results on two authoritative SAR-oriented ship detection datasets and two Gaofen-3 images demonstrate the effectiveness and robustness of the proposed methods.

Geosynchronous-earth-orbit (GEO) SAR provides excellent continuous observing capability and large swath. How-ever, the extremely long synthetic aperture time, the curved orbit and the nonplanar ground surface cause serious spatial variance in the GEO SAR signal. In this paper, a novel fast Cartesian Back Projection (BP) algorithm based on sub-aperture imaging on ground and multi-stage fusion is proposed for accurately and efficiently imaging of GEO SAR. The imaging grids are arranged on the ground surface to avoid the azimuth defocusing caused by flat-ground-approximation. Then, a new two-step spectrum compression method is derived to solve the spectrum aliasing of sub-aperture images. And a multi-stage image fusion method is adopted to combine all the sub-aperture images with high efficiency. The computational complexity and the approximation of the proposed algorithm are also discussed. Simulation results verify the effectiveness of the proposed algorithm.

In the existing time-domain autofocus algorithms, the azimuth deramping operation will change the azimuth-independent phase into the azimuth-dependent phase, which may greatly reduce the accuracy of autofocus processing in squint spotlight synthetic aperture radar (SAR). In contrast, the frequency-domain autofocus algorithms can avoid this problem because it does not involve the azimuth deramping operation. However, the existing frequency-domain autofocus algorithms are proposed based on the assumption of broadside mode, which cannot be directly applied to the squint mode. Therefore, this article extends the existing frequency-domain autofocus algorithm to the squint mode combined with the extended Omega-K (EOK) algorithm. Furthermore, a space division (SD) algorithm is embedded into the proposed algorithm as preprocessing, which can effectively compensate for the azimuth-dependent motion error. The simulation and real data are processed to verify the effectiveness of the algorithm.

High-resolution wide-swath (HRWS) multichannel synthetic aperture radar (SAR) system possesses a number of receiving channels along the azimuth direction, so it has the capacity of moving target indication and imaging. However, due to the radial velocity of the moving target, false targets occur in the focused image. By estimating the radial velocity and combining it with moving target imaging, false targets can be effectively suppressed. In this letter, a method of radial velocity estimation of a moving target is proposed based on the theory of subspace projection. This method does not need to estimate the real azimuth position of the moving target and can predict the processing time. Simulation and airborne measured data show the effectiveness of the proposed method.

Conventional beam-center approximation-based motion compensation (MOCO) algorithms fail to achieve an optimally focused image in the case of the high-resolution and high-frequency (HRHF) synthetic aperture radar (SAR) system. In this letter, a novel MOCO algorithm based on Doppler adjustment is developed with the ability to compensate the azimuth variant motion error. The change of the Doppler spectrum caused by the azimuth variant motion error is investigated and is eliminated by Doppler scaling. The proposed MOCO algorithm has dramatically improved precision when compared with the conventional MOCO methods in HRHF SAR imaging. Simulation experiments and extensive comparisons with other MOCO algorithms verify the effectiveness of the proposed algorithm.

In recent years, some complex linguistic expression techniques with probabilistic information, such as proportional linguistic terms, probabilistic linguistic term sets, have been proposed to describe the uncertainty of opinions and preferences. However, the probability information is expressed as the crisp numbers, which is usually difficult to be estimated and information losses may exist. To capture the inherent fuzziness and vagueness of decision information more comprehensively and accurately, in this article, we introduce a novel concept named dual linguistic term set (DLTS), which consists of two linguistic variables. The DLTS can allow the decision makers to express the probabilistic information by the means of linguistic variables. To describe the hesitant information of the decision makers, a more general concept named hesitant fuzzy DLTS is developed. Afterwards, a multicriteria decision making method with dual linguistic information is proposed based on the normal cloud model. To do so, the DLTS is first transformed into an equivalent two-tuple consisting of two independent normal clouds. Then, new multiplication operation and power operation of normal clouds are defined to obtain the expectation for each attribute value. After aggregating the evaluation information, we further develop a new score function to rank alternatives reasonably. Finally, an illustrative example is given to verify the effectiveness and feasibility of the proposed method, also some comparisons and analyses are provided to show the advantages of the proposed method.

Ship motions during long coherent processing interval (CPI) have six degrees of freedom, and the oscillatory motions are roughly periodical. The traditional ship imaging methods usually use a short time interval to form an image, while the image quality may suffer from low resolution, poor signal-to-noise ratio (SNR), and scatter scintillation. Using a longer CPI to generate an image may improve the quality but, however, largely increase the focusing difficulty. In this article, we investigate the focusing challenges of oscillatory ships with long CPI. Through analyzing the relative motion between the radar and the ship, the properties of wavenumber domain support (WDS) and point spreading function (PSF) of oscillatory ship imaging are studied. It is illustrated that the WDS is a 3-D sparse curved surface generated by the complex relative motion, with a time-variant energy density, nonparallel spectrum boundaries, and a complex structure. The PSF of an oscillatory ship may have a 3-D resolution but also multiple high-level sidelobes. The relationship between the WDS and the nonideal PSF is illustrated with the projection slice theorem (PST). Moreover, it is discussed that the scatterers distributed on a 3-D ship cannot be focused uniformly on a 2-D imaging plane (IP) due to the variation of the slant-range plane (SRP). The projection relationships of the resolutions and focusing positions between the SRP and the IP are also derived. Simulation results are presented to validate the analyses throughout this article.

This article presents a fast back-projection (BP) algorithm based on subaperture (SA) image coherent combination in a downsampled Cartesian coordinate grid for high squint diving terrain observation by progressive scans (HSD-TOPS) synthetic aperture radar (SAR) ground plane imaging. A two-step spectrum compression (SC) method is proposed to coherently combine the aliasing SA images by exploiting the relationship between the wavenumber and the image frequency. The first-step SC is introduced to align the spectrum support region centers. The second-step SC effectively corrects the space-variant spectrum inclination. The proposed algorithm does not need interpolation in the process of image combination, which ensures the accuracy and the efficiency of the algorithm. Furthermore, the SC method is well-modified to suppress the sidelobes of the focused image. Simulation and measured data processing verify the effectiveness of the proposed method.

Since path curvature becomes severer for higher orbit synthetic aperture radar (SAR), the stripmap mode may not provide a reliable azimuth resolution under different look angles or at different positions. Beam steering is especially valuable herein for adjusting the azimuth resolution under different observation conditions by designing the antenna steering rate. Moreover, considering that the large range migration and center range variation in the squint mode may increase the echo length and reduce the achievable scene width, we proposed a novel 2-D beam steering (TDBS) method, which promises not only a required azimuth resolution but also a wide swath (or shortened echo length) at squint when cooperated with the variable interpulse time (VIPT) technique. The simulation results obtained under different look directions are shown to validate the effectiveness of the proposed beam controlling method.

The microwave photonic (MWP) radar technique is capable of providing ultrawide frequency bandwidth waveforms to generate ultrahigh-resolution (UHR) inverse synthetic aperture radar (ISAR) imagery. Nevertheless, conventional ISAR imaging algorithms have limitations in focusing UHR MWP-ISAR imagery, where high-precision high-order range cell migration (RCM) and phase correction are crucially necessary. In this article, a UHR MWP-ISAR imaging algorithm integrating rotation estimation and high-order motion terms compensation is proposed. By establishing the relationship between parametric ISAR rotation model and high-order motion terms, an average range profile sharpness maximization (ARPSM) is developed to obtain rotation velocity by using nonuniform fast Fourier transform (NUFFT). Second-order range-dependent RCM is corrected with parametric compensation model by using the rotation velocity estimation. Furthermore, the spatial-variant high-order phase error is extracted to compensation by the entire image sharpness maximization (EISM). A new imaging framework is established with two one-dimensional (1-D) parameter estimations: ARPSM and EISM. Extensive experiments demonstrate that the proposed algorithm outperforms traditional ISAR imaging strategies in high-order RCM correction and azimuth focusing performance.

The water body detection in high-resolution synthetic aperture radar (SAR) images is a challenging task due to the changing interference caused by multiple imaging conditions and complex land backgrounds. Inspired by the excellent adaptability of deep neural networks (DNNs) and the structured modeling capabilities of probabilistic graphical models, the cascaded fully-convolutional network (CFCN) is proposed to improve the performance of water body detection in high-resolution SAR images. First, for the resolution loss caused by convolutions with large stride in traditional convolutional neural network (CNN), the fully-convolutional upsampling pyramid networks (UPNs) are proposed to suppress this loss and realize pixel-wise water body detection. Then considering blurred water boundary, the fully-convolutional conditional random fields (FC-CRFs) are introduced to UPNs, which reduce computational complexity and lead to the automatic learning of Gaussian kernels in CRFs and the higher boundary accuracy. Furthermore, to eliminate the inefficient training caused by imbalanced categorical distribution in the training data set, a novel variable focal loss (VFL) function is proposed, which replaces the constant weighting factor of focal loss with the frequency-dependent factor. The proposed methods can not only improve the pixel accuracy and boundary accuracy but also perform well in detection robustness and speed. Results of GaoFen-3 SAR images are presented to validate the proposed approaches.

The beam steering of high-squint terrain observation by progressive scans (TOPS) synthetic aperture radar (SAR) mounted on maneuvering platforms causes azimuth spectrum aliasing and nonlinear variation of the Doppler center with target azimuth position. A joint time-Doppler deramp (JTDD) based method is proposed and mainly contains two parts. First, for the azimuth spectrum aliasing, the unfolded 2-D spectrum is obtained by a modified linear deramp function in the azimuth time domain constructed from the 3-D motion parameters. After range cell migration correction (RCMC), the data supporting area in the azimuth time domain is expanded, and thus, aliased because of the nonlinear variation of Doppler center. Then, a nonlinear deramp operation in the Doppler domain is further proposed to obtain a nonaliasing signal. The proposed algorithm is efficient with less zero-padding due to the consideration of nonlinear components of Doppler center variation. Simulation and real SAR data processing are presented to validate the proposed algorithm.

The rigid oscillation and flexible deformation baseline errors occur in dual-antenna unmanned aerial vehicle (UAV) SAR interferometry (InSAR). The errors caused by airflow disturbances and UAV platform mechanical oscillation will lead to interferometric phase undulation. Measuring the errors has high requirements for length and time accuracy for the equipment. In this paper, a time-varying baseline error (TBE) estimation and compensation method based on continuous time-domain subaperture data is proposed. Firstly, we model the TBE and derive its expression in each subaperture image focused by the chirp scaling dechirp (CS-dechirp) algorithm. Then it is possible to extract the estimated differential TBE (D-TBE) from the differential interferogram of overlapping scenes in subaperture images. Further, the full-aperture TBE can be obtained through an integration of the estimated D-TBE. Finally, the full-aperture compensation can be accomplished by a phase correction after the range variation estimation. Taking advantage of the time-domain subaperture, the D-TBE phases are sampled at each subaperture center time, and the proposed method can be well combined with a motion compensation algorithm in the processing flow for UAV InSAR. Furthermore, the case of low coherence is overcome. The results of simulation and real measured airborne single-pass dual-antenna data validate the proposed approach.

Traditional clutter suppression methods are mainly studied under the condition that the pulse repetition frequency (PRF) of the system is not less than the Nyquist frequency. Whereas in the high-resolution and wide-swath (HRWS) multichannel synthetic aperture radar (SAR) system, a low PRF is used to break through the minimum antenna area constraint. The low PRF case brings new challenges to the traditional clutter suppression methods. In this letter, a subspace projection clutter suppression method is proposed based on the fact that moving targets and the clutter consist in different signal subspaces. This method can be directly applied to the HRWS multichannel SAR system, and it shows better performance compared to the space-time adaptive processing (STAP) when the moving target components cannot be ignored in the clutter covariance matrix calculation. Simulated data and airborne measured data are processed to verify its effectiveness.

The curved trajectory and long synthetic aperture time of medium-Earth-orbit (MEO) synthetic aperture radar (SAR) lead to a 2-D spatial variation in the signals. Traditional methods treat the range and azimuth variations separately and usually suffer from high computational complexities. In this article, we investigate the Doppler rate distribution across a large scene and exploit an optimal imaging coordinate system, in which the MEO SAR signals satisfy the azimuth-shift-invariant property. Thus, the additional processing of the azimuth spatial variation in MEO SAR imaging algorithms can be avoided, and the efficiency of the image formation processor can be obviously improved. The Doppler linearization is used to address the higher-order Doppler parameters to achieve more precise focusing, and at the same time, addresses the azimuth time shift caused by the changes of signal distribution. Finally, processing results of simulated stripmap-mode data with the 2-m resolution are presented to validate the proposed algorithm.