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The R. W. P. King Award is presented to an author under 36 years of age for the best paper published in the IEEE Transactions on Antennas and Propagation during the previous year. [ABSTRACT FROM AUTHOR]

This paper deals with adaptive radar detection problems where several alternative hypotheses may be plausible. This kind of problems naturally extends the conventional binary tests that often occur in radar (as well as in other application fields) by including a further uncertainty degree related to the number of unknown signal parameters (model order). Such a modification consequently leads to multiple composite alternative hypotheses. In the companion paper (Addabbo et al., 2021), we have defined a new design framework which allows us to come up with decision schemes for these hypothesis testing problems by exploiting the Kullback-Leibler Information Criterion and without resorting to heuristic design criteria. The architectures devised within the proposed framework consist of the sum between the compressed log-likelihood ratio and a penalty term inherited from model order selection rules. Such a penalty term accounts for the number of unknown parameters to overcome the limitation of the generalized likelihood ratio test in the presence of nested hypotheses. In the present paper, we apply the new design framework to different detection problems related to both real aperture and (polarimetric) synthetic aperture radar. The analysis is carried out in comparison with suitable competitors (possibly based upon heuristic design criteria) and shows that the architectures devised within the proposed theoretically-founded design framework represent an effective means to deal with detection problems where the uncertainty on some parameters leads to multiple alternative hypotheses. [ABSTRACT FROM AUTHOR]

Speckle or multiplicative noise is a critical issue in coherence-based imaging systems, such as synthetic aperture radar and optical coherence tomography. Existence of speckle noise considerably limits the applicability of such systems by degrading their performance. On the other hand, the sophistications that arise in the study of multiplicative noise have so far impeded theoretical analysis of such imaging systems. As a result, the current acquisition technology relies on heuristic solutions, such as oversampling the signal and converting the problem into a denoising problem with multiplicative noise. This paper attempts to bridge the gap between theory and practice by providing the first theoretical analysis of such systems. To achieve this goal the log-likelihood function corresponding to measurement systems with speckle noise is characterized. Then employing compression codes to model the source structure, for the case of under-sampled measurements, a compression-based maximum likelihood recovery method is proposed. The mean squared error (MSE) performance of the proposed method is characterized and is shown to scale as $O\left({\sqrt {\frac{k \log n }{ m}}}\right)$ , where $k$ , $m$ and $n$ denote the intrinsic dimension of the signal class according to the compression code, the number of observations, and the ambient dimension of the signal, respectively. This result, while in contrast to imaging systems with additive noise in which MSE scales as $O\left({{\frac{k \log n }{ m}}}\right)$ , suggests that if the signal class is structured (i.e., $k \ll n$), accurate recovery of a signal from under-determined measurements is still feasible, even in the presence of speckle noise. Simulation results are presented that suggest image recovery under multiplicative noise is inherently more challenging than additive noise, and that the derived theoretical results are sharp. [ABSTRACT FROM AUTHOR]

Being a key technology for beyond fifth-generation wireless systems, joint communication and radar sensing (JCAS) utilizes the reflections of communication signals to detect foreign objects and deliver situational awareness. A cellular-connected unmanned aerial vehicle (UAV) is uniquely suited to form a mobile bistatic synthetic aperture radar (SAR) with its serving base station (BS) to sense over large areas with superb sensing resolutions at no additional requirement of spectrum. This paper designs this novel BS-UAV bistatic SAR platform, and optimizes the flight path of the UAV to minimize its propulsion energy and guarantee the required sensing resolutions on a series of interesting landmarks. A new trajectory planning algorithm is developed to convexify the propulsion energy and resolution requirements by using successive convex approximation and block coordinate descent. Effective trajectories are obtained with a polynomial complexity. Extensive simulations reveal that the proposed trajectory planning algorithm outperforms significantly its alternative that minimizes the flight distance of cellular-aided sensing missions in terms of energy efficiency and effective consumption fluctuation. The energy saving offered by the proposed algorithm can be as significant as 55%. [ABSTRACT FROM AUTHOR]

Forward modeling of wave scattering and radar imaging mechanisms is the key to information extraction from synthetic aperture radar (SAR) images. Like inverse graphics in the optical domain, an inherently-integrated forward-inverse approach would be promising for SAR advanced information retrieval and target reconstruction. This paper presents such an attempt at inverse graphics for SAR imagery. A differentiable SAR renderer (DSR) is developed, which reformulates the mapping and projection algorithm of the SAR imaging mechanism in the differentiable form of probability maps. First-order gradients of the proposed DSR are then analytically derived, which can be back-propagated from rendered image/silhouette to the target geometry and scattering attributes. A 3D inverse target reconstruction algorithm from SAR images is devised. Several simulation and reconstruction experiments are conducted, including targets with and without background, using synthesized data or real measured inverse SAR (ISAR) data by ground radar. Results demonstrate the efficacy of the proposed DSR and its inverse approach. [ABSTRACT FROM AUTHOR]

Contrastive self-supervised learning (CSSL) has achieved promising results in extracting visual features from unlabeled data. Most of the current CSSL methods are used to learn global image features with low-resolution that are not suitable or efficient for pixel-level tasks. In this paper, we propose a coarse-to-fine CSSL framework based on a novel contrasting strategy to address this problem. It consists of two stages, one for encoder pre-training to learn global features and the other for decoder pre-training to derive local features. Firstly, the novel contrasting strategy takes advantage of the spatial structure and semantic meaning of different regions and provides more cues to learn than that relying only on data augmentation. Specifically, a positive pair is built from two nearby patches sampled along the direction of the texture if they fall into the same cluster. A negative pair is generated from different clusters. When the novel contrasting strategy is applied to the coarse-to-fine CSSL framework, global and local features are learned successively by forcing the positive pair close to each other and the negative pair apart in an embedding space. Secondly, a discriminant constraint is incorporated into the per-pixel classification model to maximize the inter-class distance. It makes the classification model more competent at distinguishing between different categories that have similar appearance. Finally, the proposed method is validated on four SAR images for land-cover classification with limited labeled data and substantially improves the experimental results. The effectiveness of the proposed method is demonstrated in pixel-level tasks after comparison with the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

In this paper, a novel unsupervised change detection method called adaptive Contourlet fusion clustering based on adaptive Contourlet fusion and fast non-local clustering is proposed for multi-temporal synthetic aperture radar (SAR) images. A binary image indicating changed regions is generated by a novel fuzzy clustering algorithm from a Contourlet fused difference image. Contourlet fusion uses complementary information from different types of difference images. For unchanged regions, the details should be restrained while highlighted for changed regions. Different fusion rules are designed for low frequency band and high frequency directional bands of Contourlet coefficients. Then a fast non-local clustering algorithm (FNLC) is proposed to classify the fused image to generate changed and unchanged regions. In order to reduce the impact of noise while preserve details of changed regions, not only local but also non-local information are incorporated into the FNLC in a fuzzy way. Experiments on both small and large scale datasets demonstrate the state-of-the-art performance of the proposed method in real applications. [ABSTRACT FROM AUTHOR]

The nonstationary and non-Gaussian distribution of the high-resolution (HR) synthetic aperture radar (SAR) image provides much valuable information. However, the current methods, especially deep learning models, directly learn spatial features from HR SAR data while ignoring global statistical information. Combining the local spatial features and global statistical properties of HR SAR images is urgently needed to capture complete HR SAR characteristics. In this paper, a feature fusion network (Fusion-Net) using both deep spatial context encoder and nonstationary joint statistical model (NS-JSM) is proposed for the first time. Fusion-Net realizes the fusion description of local spatial and global statistical features in an end-to-end supervised classification framework. First, a deep spatial context encoder network (DSCEN) is designed based on multiscale group convolution (MSGC) module and channel attention (CA) module. The DSCEN expands the scope of context information extraction with few parameters and increases the interaction between high- level feature channels. Then, the NS-JSM is adopted to capture the unique SAR statistical information. Specifically, the SAR image is transformed into the Gabor wavelet domain. The produced sub-band magnitudes and phases are modeled by the log-normal and uniform distribution. The covariance matrix (CM) is calculated for mapped sub-band data to capture the interscale and intrascale nonstationary correlation. Finally, the group compression and smooth normalization units are introduced into Fusion-Net to fuse the statistical features and spatial features, which not only exploits the complementary information between different features but also optimizes the fusion feature representation. Experiments on four real HR SAR images validate the superiority of the proposed method over other related algorithms. [ABSTRACT FROM AUTHOR]

Translational position error in microwave and millimeter-wave synthetic aperture radar (SAR) imaging systems can cause significant image quality degradation, particularly in nondestructive testing and evaluation (NDT&E) applications where the distance to the imaging object is relatively short. In this paper, this translational position error problem is fully studied through electromagnetic simulation. The results show that among possible geometrical causes of error, a translational position error, in the height direction, is the dominant factor in image quality degradation. Subsequently, a corresponding height position error compensation method is proposed and analyzed. The extensive simulations and measurement are performed in the X-band (8.2–12.4 GHz) frequency range. Then, by defining several evaluation metrics, the relationship between image quality and height position error is discussed quantitatively. The measured results show good agreement with the simulated results, which validates the effectiveness of the proposed analysis approach and the compensation method. The methodology proposed in this paper can be used to evaluate the feasibility or help define the required specifications of a microwave SAR imaging system for a specific application. [ABSTRACT FROM AUTHOR]

This paper explores the competency of the time domain ultra-wideband (UWB)-circular synthetic aperture radar (CSAR) to image the breast and detect tumors. The image reconstruction is performed using a time domain global back projection technique adapted to the circular trajectory data acquisition. This paper also proposes a sectional image reconstruction method to compensate for the group velocity changes in different layers of a multilayer medium. Experiments on an advanced breast phantom examines the suitability of this technique for breast tumor imaging. The advanced breast phantom is designed based on a MRI of a real patient, fabricated using 3D printing technology, and filled with liquids that emulate normal and cancerous tissues. The measurement results, compared with MRI imaging of the phantom, demonstrate the suitability of the UWB-CSAR method for breast tumor imaging. This method can be a tool for early diagnosis as well as for treatment monitoring during chemotherapy or radiotherapy. [ABSTRACT FROM AUTHOR]

Polarimetric whitening filter (PWF) can be used to filter polarimetric synthetic aperture radar (PolSAR) images to improve the contrast between ships and sea clutter background. For this reason, the output of the filter can be used to detect ships. This paper deals with the setting of the threshold over PolSAR images filtered by the PWF. Two parameter-constant false alarm rate (2P-CFAR) is a common detection method used on whitened polarimetric images. It assumes that the probability density function (PDF) of the filtered image intensity is characterized by a log-normal distribution. However, this assumption does not always hold. In this paper, we propose a systemic analytical framework for CFAR algorithms based on PWF or multi-look PWF (MPWF). The framework covers the entire log-cumulants space in terms of the textural distributions in the product model, including the constant, gamma, inverse gamma, Fisher, beta, inverse beta, and generalized gamma distributions ($\text{G}\Gamma $ Ds). We derive the analytical forms of the PDF for each of the textural distributions and the probability of false alarm (PFA). Finally, the threshold is derived by fixing the false alarm rate (FAR). Experimental results using both the simulated and real data demonstrate that the derived expressions and CFAR algorithms are valid and robust. [ABSTRACT FROM AUTHOR]

Nowadays, in the electromagnetism environment, the complex interferences, including the narrowband interferences (NBIs) and wideband interferences (WBIs), may severely affect the imaging quality of synthetic aperture radar (SAR) systems. Most traditional methods can only tackle with one kind of isolated interferences, NBIs or WBIs, which are widely distributed in the 1-D range frequency domain or 2-D range time–frequency domain. In this paper, we propose a complex tensor robust principal component analysis (CT-RPCA) method based on a novel 3-D range-azimuth-space tensor model to mitigate continuously distributed NBIs and WBIs simultaneously. The main contributions of this paper are summarized in three aspects. First, we strictly prove the low-rank property of the isolated NBIs and WBIs in the range-azimuth domain. Second, we use multiple views of the signal to construct a novel 3-D range-azimuth-space tensor model, where both the NBI tensor and the WBI tensor have spatial low-rank property due to the approximately stable frequency bands along the spatial dimension. Third, the CT-RPCA method is employed to efficiently suppress NBIs and WBIs simultaneously by solving the tensor RPCA problem. Finally, the real SAR data with simulated complex interferences are employed to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Monitoring the sea ice cover is important for both climate studies and ice operations, such as shipping. It is challenging to validate even basic essential variables, such as the sea ice extent, due to a lack of appropriate validation data. Instead of focusing on validation, this paper looks at the use of categorical triple collocation (CTC) for the task of quantitatively comparing three colocated data sets. CTC has been developed and used in earlier studies to rank binary data sets. In this paper, we extend earlier studies and bring in recent results from the binary classification community to estimate the class imbalance (the relative proportion of each class, ice or water). We then use this class imbalance to obtain quantitative estimates of the proportion correct of ice (sensitivity) and the proportion correct of water (specificity). The methodology is first tested using toy data, after which three data sets from the Gulf of Saint Lawrence, on the east coast of Canada, are used. These data sets are from an ice–ocean model, a passive microwave sea ice concentration retrieval, and a sea ice concentration retrieval from synthetic aperture radar (SAR). By looking at both the sensitivity and the specificity, it is found that the passive microwave data have difficulty in recognizing ice during freeze-up, but they perform well at obtaining the correct water observations. This distinction cannot be made by ranking the data sets. The CTC method is compared with, and found to be complementary to, a validation using ice/water states from the interactive multisensor snow and ice mapping system. [ABSTRACT FROM AUTHOR]

This paper investigates new approaches to estimating the motion of the dynamic ocean surface using a multi-channel synthetic aperture radar (MSAR) with $M$ phase centers arranged in an along-track configuration. The objective of this paper is to determine the processing methods that produce the finest velocity resolution, an issue that arises due to the finite coherence time of radar backscatter produced by the sea surface. The investigation is carried out both theoretically, using synthesized data produced from a modeled MSAR covariance matrix, as well as experimentally, using images collected with a 16-channel system. Three processing methods are considered: linear regression along with a multi-baseline phase progression, estimation of the velocity centroid, and coherent averaging of the shortest baseline interferograms. Both the theoretical and experimental results indicate that simple averaging of the shortest-baseline interferograms often produces the best velocity precision. [ABSTRACT FROM AUTHOR]

The main challenging problems in ocean current retrieval from along-track interferometric (ATI)-synthetic aperture radar (SAR) are phase calibration and wave bias removal. In this paper, a method based on differential InSAR (DInSAR) technique for correcting the phase offset and its variation is proposed. The wave bias removal is assessed using two different Doppler models and two different wind sources. In addition to the wind provided by an atmospheric model, the wind speed used for wave correction in this work is extracted from the calibrated SAR backscatter. This demonstrates that current retrieval from ATI-SAR can be completed independently of atmospheric models. The retrieved currents, from four TanDEM-X (TDX) acquisitions over the Öresund channel in the Baltic Sea, are compared to a regional ocean circulation model. It is shown that by applying the proposed phase correction and wave bias removal, a good agreement in spatial variation and current direction is achieved. The residual bias, between the ocean model and the current retrievals, varies between 0.013 and 0.3 m/s depending on the Doppler model and wind source used for wave correction. This paper shows that using SAR as a source of wind speed reduces the bias and root-mean-squared-error (RMSE) of the retrieved currents by 20% and 15%, respectively. Finally, the sensitivity of the sea current retrieval to Doppler model and wind errors are discussed. [ABSTRACT FROM AUTHOR]

This paper proposes a complex-valued convolutional neural network for land form classification and discovery in interferometric synthetic aperture radar (InSAR). Since the amount of satellite-borne SAR data has been increasing drastically, it is necessary to structurize the local features contained in observation data prior to utilization in the so-called big data framework for higher usability. Convolutional neural networks have such potential in general. However, there exists no network that can deal with complex amplitude data obtained in InSAR consistently. In this paper, we propose a complex-valued convolutional neural network to deal with InSAR. We demonstrate that the network classifies slopes and plains adaptively and, moreover, indicates small volcanos similar to a sample volcano (Omuroyama) included in the InSAR data. We also find their characteristic features emerging in the kernels in the convolution layers. These results reveal that the proposed complex-valued convolutional neural network is capable of successfully discovering unidentified lands similar to a prepared sample, which is highly useful for the InSAR data structurization. [ABSTRACT FROM AUTHOR]

Frequency-modulation continuous-wave (FMCW) synthetic-aperture radar (SAR) can minimize the peak transmission power of sensors and reduce the size and weight of the systems. Wavenumber domain algorithm (WDA) is an accurate focusing method for SAR imaging. By using the exact signal form to compensate the phase error, WDA can achieve exact scene recovery from high-squint and long aperture data as long as the platform velocity is stable. In this paper, we introduce WDA to FMCW SAR and discuss the WDA-based FMCW SAR sparse imaging method. There are two main contributions of this paper: 1) a motion compensation-based WDA imaging method is introduced to correct the motion error that is often associated in practical airborne FMCW SAR data and 2) a novel WDA-based FMCW SAR sparse imaging method is developed to further improve the performance of recovered image. Compared with the typical WDA algorithm, the sparse imaging method can suppress the noise and sidelobes and perform the sparse scene recovery from the downsampled data. Experimental results via simulated and real airborne data verify the presented WDA-based FMCW SAR imaging methods. [ABSTRACT FROM AUTHOR]

Due to the coherent processing of synthetic aperture radar (SAR) systems, multiplicative speckle noise arises providing a granular appearance in SAR images. This kind of noise makes it difficult to analyze and interpret surface images from the earth. Therefore, studying alternatives to attenuate the speckle is a constant task in the image processing literature. Current state-of-the-art filters in remote sensing area explore the philosophy of similarity between patches. This paper aims to expand the traditional nonlocal means (NLM) algorithm originally proposed for the additive white Gaussian noise (AWGN) to deal with the speckle. In our research, we consider the worst scenario, i.e., the single-look speckle noise, and apply the NLM to filter intensity SAR images in the Haar wavelet domain. To accomplish this task, the Haar coefficients were described by exponential-polynomial (EP) and gamma distributions. Furthermore, stochastic distances based on these two mentioned distributions were derived and embedded in the NLM filter by replacing the Euclidean distance of the original method. This represents the main contribution of the proposed research. Finally, this paper analyzes and compares the synthetic and real experiments of the proposed method with some recent filters of the literature demonstrating its competitive performance. [ABSTRACT FROM AUTHOR]

Studies pertaining to small scale structures producing scintillations using synthetic aperture radars (SARs) have predominantly been conducted at low-latitude regions. The high-latitude region (auroral belt and polar caps) is highly dynamic and varies in response to stimuli from solar winds and the magnetosphere in complex ways. In this paper, the authors have shown the capability of SAR for scintillation observation in the auroral region. An attempt has been made to fit an irregularity anisotropy model to SAR measurements for characterizing the ionospheric irregularities in the auroral regions. The dependency of anisotropy irregularity model on parameters, such as irregularity structure (axial ratio), their orientation with respect to magnetic field lines, and the ionospheric plasma drift, is closely studied using Advanced Land Observing Satellite (ALOS)-2 datasets acquired over Alaska. Geomagnetic indices and total electron content data are consistent with the occurrence of the scintillation event under study. Drift velocity measurements from high-frequency radars in the super dual array radar network (SuperDARN) showed that the anisotropy is independent of the magnitude and the azimuth angle of the plasma drift. The typical range of orientation angle suitable for the high latitude regions probed by ALOS-2 is demonstrated to be between 120°–135°. This paper explores the idea of inferring irregularity anisotropy by comparing the amplitude scintillation (S4) index measured in SAR data pairs using two well-established techniques. The image contrast technique heavily relies on the accurate modeling of anisotropy, whereas the radar cross-sectional enhancement method is independent of it. This feature has been exploited in the $S_{4}$ comparison to finally fit the choice of irregularity axial ratio and conclude that the sheet-like structures best describe the ionospheric irregularity structure in the region under observation. [ABSTRACT FROM AUTHOR]

Being capable of providing weather-independent, day-and-night, forward-looking, and high-resolution images, bistatic forward-looking synthetic aperture radar (BFSAR) is a promising sensing technique in applications such as the scene-matching-aided navigation for recently emerging high-speed maneuvering platforms (HMPs). Because of the high speed and the great maneuverability of HMPs and the bistatic forward-looking configuration, conventional image formation algorithms, such as polar format algorithm (PFA), are no longer suitable for HMP-borne BFSAR (HMP-BFSAR). Hence, in this paper, we propose a novel PFA for HMP-BFSAR image formation. In the proposed PFA, a range model, termed as quasi-continuous -move range model, is established by taking the maneuvers of the receiver during pulse propagation into account instead of adopting stop-and-go approximation. Moreover, to take advantage of the collected $k$ -set efficiently, an affine mapping, termed as $k$ -set affine mapping, is conceived to transform the parallelogram-shaped $k$ -set support region to a horizontal and quasi-rectangular one. Furthermore, to compensate for the defocus effect induced by wavefront curvature, a closed-form refocus filter based on the implicit function theorem is derived. Both point target simulation and distributed target simulation are presented in this paper. The simulation results show that the proposed PFA significantly outperforms the conventional PFA in terms of focusing quality and computational efficiency when applied to HMP-BFSAR image formation. [ABSTRACT FROM AUTHOR]

This paper deals with precise absolute geolocation of point targets by means of a pair of high-resolution synthetic aperture radar (SAR) acquisitions, acquired from a satellite. Even though a single SAR image is a 2-D projection of the backscatter, some 3-D information can be extracted from a defocussing analysis, depending on the resolution, thanks to orbital curvature. A second acquisition, observing the same scene under a different look angle, adds stereogrammetric capability and can achieve geolocation accuracy at decimeter level. However, for the stereogrammetric analysis to work, it is necessary to match targets correctly in the two images. This task is particularly difficult if it has to be automatic and targets are dense. Unfortunately, the defocussing-based geolocation is not sufficient for reliable target matching: the limiting factor is the unknown tropospheric delay that can cause geolocation errors of several meters in the elevation direction. However, observing that the tropospheric phase screen displays a low-pass character, this paper shows how to identify statistically the local atmospheric disturbances, therefore dramatically improving the score of successful matching. All steps involved exploit peculiar radar image characteristics and, thanks to this, avoid generic point cloud matching algorithms. The proposed algorithm is shown at work on a pair of TerraSAR-X staring spotlight images. [ABSTRACT FROM AUTHOR]

A novel physical paradigm is explored in this paper: synthetic-aperture radar (SAR) ocean speckle is informative. This paper experimentally analyzes the SAR ocean speckle intensity K-distribution model versus sea-surface wind field. It is shown that the normalized intensity moments of the K-distribution measured from actual C-band SAR data well fit the theoretical ones under different wind conditions, i.e., wind regime and relative SAR-wind azimuth direction. In addition, it is observed that the K-distribution shape parameter decreases as wind speed increases. A sensitivity of the K-distribution shape parameter on incidence angle and wind direction is also observed, where the largest variability is experienced at higher incidence angles and under crosswind relative azimuth angle. [ABSTRACT FROM AUTHOR]

Frequency agile radar (FAR) has improved anti-jamming performance over traditional pulse-Doppler radars under complex electromagnetic circumstances. To reconstruct the range-Doppler information in FAR, many compressed sensing (CS) methods including standard and block sparse recovery have been applied. In this paper, we study phase transitions of range-Doppler recovery in FAR using CS. In particular, we derive closed-form phase transition curves associated with block sparse recovery and complex Gaussian matrices, based on prior results of standard sparse recovery under real Gaussian matrices. We further approximate the obtained curves with elementary functions of radar and target parameters, facilitating practical applications of these curves. Our results indicate that block sparse recovery outperforms the standard counterpart when targets occupy more than one range cell, which are often referred to as extended targets. Simulations validate the availability of these curves and their approximations in FAR, which benefit the design of the radar parameters. [ABSTRACT FROM AUTHOR]

Millimeter-wave radars are being increasingly integrated into commercial vehicles to support advanced driver-assistance system features. A key shortcoming for present-day vehicular radar imaging is poor azimuth resolution (for side-looking operation) due to the form factor limits on antenna size and placement. In this paper, we propose a solution via a new multiple-input and multiple-output synthetic aperture radar (MIMO-SAR) imaging technique, that applies coherent SAR principles to vehicular MIMO radar to improve the side-view (angular) resolution. The proposed 2-stage hierarchical MIMO-SAR processing workflow drastically reduces the computation load while preserving image resolution. To enable coherent processing over the synthetic aperture, we integrate a radar odometry algorithm that estimates the trajectory of ego-radar. The MIMO-SAR algorithm is validated by both simulations and real experiment data collected by a vehicle-mounted radar platform. [ABSTRACT FROM AUTHOR]

The scattering signatures of a synthetic aperture radar (SAR) target image will be highly sensitive to different azimuth angles/poses, which aggravates the demand for training samples in learning-based SAR image automatic target recognition (ATR) algorithms, and makes SAR ATR a more challenging task. This paper develops a novel rotation awareness-based learning framework termed RotANet for SAR ATR under the condition of limited training samples. First, we propose an encoding scheme to characterize the rotational pattern of pose variations among intra-class targets. These targets will constitute several ordered sequences with different rotational patterns via permutations. By further exploiting the intrinsic relation constraints among these sequences as the supervision, we develop a novel self-supervised task which makes RotANet learn to predict the rotational pattern of a baseline sequence and then autonomously generalize this ability to the others without external supervision. Therefore, this task essentially contains a learning and self-validation process to achieve human-like rotation awareness, and it serves as a task-induced prior to regularize the learned feature domain of RotANet in conjunction with an individual target recognition task to improve the generalization ability of the features. Extensive experiments on moving and stationary target acquisition and recognition benchmark database demonstrate the effectiveness of our proposed framework. Compared with other state-of-the-art SAR ATR algorithms, RotANet will remarkably improve the recognition accuracy especially in the case of very limited training samples without performing any other data augmentation strategy. [ABSTRACT FROM AUTHOR]

Robust variants of Wald, Rao and likelihood ratio (LR) tests for the detection of a signal subspace in a signal interference subspace corrupted by contaminated Gaussian noise are proposed in this paper. They are derived using the $\alpha -$ divergence, and the trade-off between the robustness and the power (the probability of detection) of the tests is adjustable using a single hyperparameter $\alpha $. It is shown that when $\alpha \rightarrow 1$ , these tests are equivalent to their well known classical counterparts. For example the robust LR test coincides with the LR test or the matched subspace detector (MSD). Asymptotic results are provided to support the proposed tests and robustness to outliers is obtained using values of $\alpha < 1$. Numerical experiments illustrating the performance of these tests on simulated, real functional magnetic resonance imaging (fMRI), hyperspectral and synthetic aperture radar (SAR) data are also presented. [ABSTRACT FROM AUTHOR]

In conventional polar format algorithm (PFA), the effective imaging scene is bounded to a limited region near the reference point unless postprocessing is utilized. In a previous paper, refocusing and zoom-in PFA (RZPFA) for curvilinear spotlight SAR imaging were proposed to produce a refocused image for an arbitrary region of interest (ROI) with constant elevation. However, for certain applications, the residual distortion and defocus caused by rugged terrain could not be ignored. In this article, RZPFA is adapted to incorporate the known digital elevation model (DEM) into the imaging process, which is named as orthorectified PFA (OPFA). With just little additional computations than RZPFA, OPFA can realize georeferenced orthorectified imaging via a nonuniform fast Fourier transform of type 3 (NuFFT-3) without the need of postprocessing. The quantitative metrics for the residual DEM distortion and residual DEM defocus were also derived to determine the effective imaging extent of OPFA. Within the effective extent, the proposed OPFA can obtain an orthorectified image efficiently, and the image has a very high quality comparable to backprojection (BP). The imaging results of measured echo and DEM data demonstrated the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This paper proposes a new scheme of motion parameter estimation and relocation for airborne three-channel circular stripmap synthetic aperture radar (CSSAR)-ground moving target indication (GMTI) systems. Compared with the conventional straight-path SAR, the parameter estimation of a target is more challenging because the target’s range history and signal model are more complicated due to the complexity of the relative motion between CSSAR and ground moving target. In this paper, the signal model of a ground moving target and the expression for its along-track interferometric (ATI) phase from the environment of airborne three-channel CSSAR are derived. The coupling effect among the target’s motion and position parameters is also figured out. Then, a scheme of motion parameter estimation and relocation is proposed. The proposed scheme utilizes the ATI phase and the quadratic-term coefficient in the range equation to estimate the target’s motion and position parameters and utilizes an iterative strategy to address the coupling effect among these parameters. Numerical simulations are conducted to validate the satisfactory performance achieved by the proposed algorithm. [ABSTRACT FROM AUTHOR]

This paper discusses an image processing architecture and tools to address the problem of ship detection in synthetic-aperture radar images. The detection strategy relies on a tree-based representation of images, here a Maxtree, and graph signal processing tools. Radiometric as well as geometric attributes are evaluated and associated with the Maxtree nodes. They form graph attribute signals which are processed with graph filters. The goal of this filtering step is to exploit the correlation existing between attribute values on neighboring tree nodes. Considering that trees are specific graphs where the connectivity toward ancestors and descendants may have a different meaning, we analyze several linear, nonlinear, and morphological filtering strategies. Beside graph filters, two new filtering notions emerge from this analysis: tree and branch filters. Finally, we discuss a ship detection architecture that involves graph signal filters and machine learning tools. This architecture demonstrates the interest of applying graph signal processing tools on the tree-based representation of images and of going beyond classical graph filters. The resulting approach significantly outperforms state-of-the-art algorithms. Finally, a MATLAB toolbox allowing users to experiment with the tools discussed in this paper on Maxtree or Mintree has been created and made public. [ABSTRACT FROM AUTHOR]

In most of the existing ground penetrating radar (GPR) imaging algorithms, using either full or sparse data collection, the ground is modeled as a single half-space layer. In this paper, a generalized sparse imaging approach with total variation minimization (TVM) for multiple-input multiple-output (MIMO) GPR imaging through multilayered subsurface is proposed. The multilayered media Green’s function is incorporated in the imaging algorithm to take into account the complex wave propagation effects under multilayered subsurface. An analytical expression of the layered subsurface Green’s function is derived using the stationary-phase method, which significantly reduces the computation time and complexity. On the other hand, as TVM minimizes the gradient of the image, its incorporation in the imaging algorithm results in a reconstruction that preserves the geometry and edges of the targets better than the standard L1-minimization-based sparsity-driven imaging. The number of antenna elements and frequency measurements in MIMO GPR system can be significantly reduced using the proposed technique without degradation of the image quality. Although MIMO configuration is investigated in this paper, the presented approach can be simply applied to monostatic synthetic aperture radar. [ABSTRACT FROM AUTHOR]

This paper examines the attainable performance of various Doppler sublook or subband cross correlation techniques for vessel detection in synthetic aperture radar images. Research from the past two decades claims that these techniques were capable of improving the detection of small ships in challenging maritime environments. Despite many published experimental examples, a thorough analytical investigation corroborating this claim is noticeably absent. This paper is based on a rigorous theoretical analysis founded on the statistical properties of a textured sea surface model in thermal noise with simultaneous consideration of a constant false alarm rate. Emphasis has been placed on the correct accounting for detrimental physical effects caused by the Doppler spectrum being split into nonoverlapping parts, which have not been sufficiently considered in the literature to date. The theoretical results are confirmed via simulations and are substantiated with real RADARSAT-2 data. The analysis in this paper has neither found theoretical nor empirical evidence that sublook correlation techniques outperform the classical detector based on the image magnitude. [ABSTRACT FROM AUTHOR]

This paper evaluates the capability of Sentinel-1 VH-polarized synthetic aperture radar signals, involving 738 scenes in the interferometric wide swath (IW) mode, for ocean surface wind speed retrieval using a novel subswath-based C-band cross-polarized ocean model. When compared with in situ measurements, it is observed that wind speed retrieval accuracy varies progressively along swath, with the most accurate wind speed retrievals being derived from subswath 3 [root-mean-square error (RMSE) of $1.82\,\,\text {m}\cdot \mathrm {s}^{-1}$ ], followed by subswath 2 (RMSE of $1.92\,\,\text {m}\cdot \mathrm {s}^{-1}$), while subswath 1 showed the lowest retrieval accuracy (RMSE of $2.37\,\,\text {m}\,\cdot \,\mathrm {s}^{-1}$). The average RMSE of wind speeds retrieved from all the three subswaths is $2.08\,\,\text {m}\,\cdot \,\mathrm {s}^{-1}$ under low-to-high wind speed regimes (wind speeds $< 25\,\,\text {m}\,\cdot \,\mathrm {s}^{-1}$). We further observed that the dependence of VH-polarized normalized radar cross section (NRCS) on incidence angle is attributable to the high and changing noise equivalent sigma zero (NESZ) with incidence angle under low-to-moderate wind speed regimes. And that strong VH-polarized radar signals could overcome the NESZ effect, thereby eliminating the dependence of VH-polarized NRCS on incidence angle under strong wind conditions. For Sentinel-1 IW mode VH-polarized data, the effect of NESZ could be ignored when wind speeds are greater than $15\,\,\text {m}\cdot \mathrm {s}^{-1}$ , as a better wind speed retrieval performance of these data has been recorded in this paper at wind speeds greater than $10\,\,\text {m}\cdot \mathrm {s}^{-1}$ , owing to an RMSE below $1.6\,\,\text {m}\cdot \mathrm {s}^{-1}$ and biases ranging from −0.5 to $0.5\,\,\text {m}\cdot \mathrm {s}^{-1}$. [ABSTRACT FROM AUTHOR]

This paper presents the first experimental assessment of a highly integrated dual-band dual-polarized antenna tile designed for synthetic aperture radar (SAR) digital beamforming (DBF) satellite applications. The demonstrator described in this paper is the first comprehensive experimental validation of an RF module providing the X-band and Ka-band (9.6- and 35.75-GHz) operation with custom downconversion stages. All the antennas, transitions, and downconversion chips are integrated in the same antenna tile fabricated using a customized 15-layer high density interconnect process. The designed tile goes to the limits of the proposed technology and for the high trace density and for the size of the vertical transitions. The proposed results represent the state of the art in terms of compactness for a DBF SAR RF module even though the demonstrator was manufactured with a standard low-cost technology. The experimental assessment proves the validity of the proposed manufacturing and integration approaches showing a substantial agreement between the performance of the individual blocks and of the integrated system. [ABSTRACT FROM AUTHOR]

This paper addresses the highly challenging problem of automatically detecting man-made structures especially buildings in very high-resolution (VHR) synthetic aperture radar (SAR) images. In this context, this paper has two major contributions. First, it presents a novel and generic workflow that initially classifies the spaceborne SAR tomography (TomoSAR) point clouds—generated by processing VHR SAR image stacks using advanced interferometric techniques known as TomoSAR—into buildings and nonbuildings with the aid of auxiliary information (i.e., either using openly available 2-D building footprints or adopting an optical image classification scheme) and later back project the extracted building points onto the SAR imaging coordinates to produce automatic large-scale benchmark labeled (buildings/nonbuildings) SAR data sets. Second, these labeled data sets (i.e., building masks) have been utilized to construct and train the state-of-the-art deep fully convolution neural networks with an additional conditional random field represented as a recurrent neural network to detect building regions in a single VHR SAR image. Such a cascaded formation has been successfully employed in computer vision and remote sensing fields for optical image classification but, to our knowledge, has not been applied to SAR images. The results of the building detection are illustrated and validated over a TerraSAR-X VHR spotlight SAR image covering approximately 39 km 2—almost the whole city of Berlin— with the mean pixel accuracies of around 93.84%. [ABSTRACT FROM AUTHOR]

This paper leads a discussion on how to form a Synthetic Aperture Radar (SAR) image without knowing the relative track. That is, within the scope of factorized geometrical autofocus (FGA). The FGA algorithm is a base-2 fast factorized back-projection (FFBP) formulation with six free geometry parameters (per subaperture pair). These are tuned step by step until a sharp image is obtained. This innovative autofocus concept can compensate completely for an erroneous geometry. The FGA algorithm has been applied successfully on two ultrawideband (UWB) data sets, acquired by the CARABAS II system at very high frequency (VHF)-band. The relative tracks are known (measured accurately). We, however, adopt and modify a basic geometry model. A linear equidistant track at fixed altitude is initially assumed. Apart from deviations due to linearization, a ~2.5-m/s along-track velocity error is also introduced. Resulting FGA images are compared to reference images and verified to be focused. This indicates that it is feasible to form a wavelength-resolution SAR image at VHF-band without support from a motion measurement system. The execution time for the examples in this paper is about five times longer with autofocus than without. Hence, the FGA algorithm is now fit for use on a regular basis. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the effect of frequency uncertainty in signals generated or measured by a microwave instrument on the resulting synthetic aperture radar (SAR) images, particularly for nondestructive testing (NDT) applications. Wideband SAR imaging systems measure reflections from a target by irradiating it with locally generated signals that can potentially have some level of frequency uncertainty. Quantifying this frequency uncertainty provides the user with a realistic and expected level of image distortion which may manifest itself as blurring, noise artifacts, etc. In this paper, we show that as uncertainty in the actual frequency value increases, the level of image distortions increases predominantly for distant targets. This is an important fact for NDT applications since the imaged object is commonly close to the imaging system. In addition, these imaging system usually have a limited “aperture” size, which makes target distance an important consideration. For complex targets, we show how frequency uncertainty-based image distortions can dominate features in an image depending on the reflected signal amplitude from the target. We also show that in real imaging systems, the statistical distribution of frequency uncertainty combined with practical, near-target ranges (distances) produce nondiscernible image distortions. [ABSTRACT FROM AUTHOR]

Polarization orientation angle (POA) is an important parameter of polarimetric radar scattering from slopes in mountainous region. It is known that surface tilted in azimuth direction and buildings not aligned in the along-track direction induce polarization orientation shifts. Earlier research has established orientation angle as a function of radar imaging geometry and surface slopes, and that POA estimation can be derived from polarimetric radar data using circular polarization. Besides these, polarimetric scattering from steep slopes and its relation to POA remain not well understood. In this paper, we address these issues by adopting a tilted surface model based on Bragg scattering. We have found that, as the azimuthal slope increases, $\vert $ VV $\vert $ decreases at a faster rate than $\vert $ HH $\vert $ , they become equal, when POA is ±45°, and $\vert $ HH $\vert >\vert $ VV $\vert $ afterward. In other words, the Pauli component, $\vert $ HH-VV $\vert $ reduced to zero at POA = ± 45°, and the typical Bragg scattering characteristics of $\vert $ VV $\vert >\vert $ HH $\vert $ does not apply when steep slope is present inducing $\vert $ POA $\vert > 45^{\circ }$. Furthermore, the cross-pol $\vert $ HV $\vert $ does not always increase with azimuth slope but also reaches a maximum then decreases to zero. In addition, we investigate the effect of soil moisture on polarimetric SAR (PolSAR) scattering characteristics of steep terrain and the effect of vegetation over surface on POA estimation. The latter is demonstrated with NASA/JPL TOPSAR L-band PolSAR data and C-band InSAR data. Another significance of this paper is that it provides a direct and rigorous derivation of POA equations. The earlier version was derived from a different concept. [ABSTRACT FROM AUTHOR]

A generalized continuous wave synthetic aperture radar (GCW-SAR) concept is proposed in this paper. By using full-duplex radio frontend and continuous wave signaling, the GCW-SAR system can overcome a number of limitations inherent within the existing SAR systems and achieve high-resolution and wide-swath remote sensing with low-power signal transmission. Unlike the conventional pulsed SAR and the frequency-modulated continuous-wave SAR, the GCW-SAR reconstructs a radar image by directly correlating the received 1-D raw data after self-interference cancellation with predetermined location-dependent reference signals. A fast imaging algorithm, called the piecewise constant Doppler (PCD) algorithm, is also proposed, which produces the radar image recursively in the azimuth direction without any intermediate step, such as range compression and migration compensation, as required by conventional algorithms. By removing the stop-and-go assumption or slow-time sampling in azimuth, the PCD algorithm not only achieves better imaging quality but also allows for more flexible waveform and system designs. Analyses and simulations show that the GCW-SAR tolerates significant self-interference and works well with a large selection of various system parameters. The work presented in this paper establishes a solid theoretical foundation for next-generation imaging radars. [ABSTRACT FROM AUTHOR]

This paper focuses on the detection, from a stack of repeated-pass interferometric synthetic aperture radar (SAR) images, of such changes causing a target to completely lose the correlation between one epoch and another. This can be the consequence of human activities, such as construction, destruction, and agricultural activities, and also be the consequence of hazards, such as earthquake, landslides, or flooding, to buildings or terrains. The millimetric sensitivity of SAR makes it valuable for detecting such changes. This paper approaches two coherent change detection methods: a space coherent, time incoherent one and a full space and time coherent one, both based on the generalized likelihood ratiob (LR) test. A preliminary validation of the method is provided by processing two Sentinel-1 data stacks of 2016 Central Italy earthquake and by comparing the results with the map of damaged buildings in Amatrice and Accumoli made by Copernicus Emergency Management Service. [ABSTRACT FROM AUTHOR]

This paper deals with the compensation of phase miscalibration in the general context of tomographic synthetic aperture radar image focusing. Phase errors are typically independent of one acquisition to the other, thus leading to a spreading and defocusing in the multidimensional (3-D, 4-D, and 5-D) imaging space. Coping with this problem in presence of volumetric scattering is generally a complex issue. In this paper, we consider the approach for phase calibration characterized by the advantage, with respect to classical phase calibration algorithms, of not requiring either the identification of a reference target or specific assumptions about the unknown phase function, or a priori information about the terrain topography. The novelty of the proposed phase miscalibration estimation and compensation method is related to its ability to avoid unwanted and uncontrollable vertical shifts in the focused image. The estimation of the calibration phase is performed by optimizing the contrast or the entropy of the vertical profile with the constraint of a zero phase derivative. Such a constraint preserves the output height distribution. Experimental results of simulated and real data are included to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Fully polarimetric model-based decompositions are developed by accounting for the physical scattering model and experimental polarimetric SAR data acquisition processes. These decompositions offer the promising straightforward interpretation and highly improved inversion models for visualizing images of scattering scenarios optimally. However, the attempts in existing decompositions to implement the split real and imaginary components of the $T_{13}$ element of the coherency matrix have been hampered by the absence of physical models to fit the coherency matrix. In this paper, two additional physical scattering submodels are derived. The real and imaginary parts of $T_{13}$ are accounted for by implementing two newly developed physical scattering models. (One is for oriented dipole scattering and the other is for oriented quarter-wave reflection.) Furthermore, this paper is extended by implementing these physical models into a six-component scattering power model-based decomposition. To this date, the developed novel decompositions account for the maximum elements of the coherency matrix in a physical manner compared to the existing model-based decompositions. The proposed novel decomposition is tested on L-band and X-band fully polarimetric SAR data sets of the Advanced Land Observing Satellite-2/Phased Array L-band Synthetic Aperture Radar-2 and the X-band TerraSAR-X, respectively. This new decomposition produces additional two scattering submatrix components. Such scattering components are prevalent in vegetation and urban areas and even dominant over highly oriented urban scenarios. The new method enhances the truly existing double-bounce scattering contributions and reduces the overrated volume scattering from double-bounce scatterers. By comparing the results, it is found that the proposed decomposition considerably enhances the SAR image quality and its more correct visualizing presentation compared to existing decompositions. It is also found to be more robust over the oriented urban areas than the existing decompositions, resulting from the utilization of both the real and imaginary components of $T_{13}$ polarimetric information in a physical scattering manner. [ABSTRACT FROM AUTHOR]

This paper proposed a 3-D surface deformation retrieval algorithm for synthetic aperture radar interferometry (InSAR) based on a Global Navigation Satellite System (GNSS) as a transmitter and a fixed receiver (GNSS-based InSAR), where simultaneous multiple GNSS transmitters and a repeat-pass concept were adopted. This paper consists of three parts. First, the interferometric phase model under repeat-pass concept was established for both general and bistatic permanent scatterer (PS) cases in the GNSS-based InSAR. Second, the 3-D deformation retrieval algorithm was presented, and the position dilution of precision was analytically derived to evaluate the performance of measured 3-D deformation. Third, using a designed transponder onboard displacement device as the bistatic PS, the feasibility of 3-D deformation retrieval using GNSS-based InSAR was confirmed by repeat-pass experiments, where four simultaneously available Beidou-2 Inclined Geosynchronous Orbit satellites were used as transmitters, and 16 repeat-pass data sets were collected. Using the proposed algorithm, the final experimental results suggested that the GNSS-based InSAR could obtain deformation estimations with better accuracy than at 5 mm in all three directions. Thus, huge potential exists for applications such as landslide prediction and infrastructure safety monitoring. [ABSTRACT FROM AUTHOR]

A passive compressive device (PCD), which works at millimeter waves, is proposed in this paper. In a passive manner, the device allows to compress the signals from multiple separate channels into a single or a reduced number of channels. This permits a simplification of the transmitter or receiver chains for radar imaging applications. The PCD is a 3-D cavity that ensures each channel to be decorrelated with each other. If the transfer functions are known, the compressed signals are decompressed in postprocessing. This paper proposes a suitable method to estimate simultaneously the transfer functions of the PCD. This is validated by measurements in a multiple-input-multiple-output (MIMO) configuration to produce a complex synthetic aperture radar image. Measurements revealed that the point spread function generated by the MIMO configuration with the use of the PCD leads to almost similar results, in terms of spatial resolution and sidelobe level ratio, as those of a single-input-multiple-output configuration (uniform amplitude), with a fewer receiving elements. [ABSTRACT FROM AUTHOR]

Synthetic aperture sonar and synthetic aperture radar involve the creation of high-resolution images of a scene via scattered signals recorded at different locations. Each pixel of the reconstructed image includes information obtained from multiple aspects due to the changing position of the sources/receivers. In this paper, the aspect-dependent scattering at each pixel is exploited to provide additional information about the scene; this paper presents a framework for converting and utilizing multiaspect data, as well as several examples. For sonar data, as is presented here, the aspect dependence may be leveraged to separate objects of interest from the background, to understand the local bathymetry, or for visualizing acoustic shadowing in full circular synthetic aperture sonar images. Several examples of images of the seafloor containing objects of interest are presented for both circular and linear apertures. In addition, the aspect dependence of low-frequency elastic scattering from objects may be used to understand the underlying scattering physics, which is of potential use in fields such as target recognition and nondestructive testing; a laboratory example is presented. [ABSTRACT FROM AUTHOR]

Similar to the conventional squint mode synthetic aperture radar (SAR) imaging processing, the estimation of Doppler centroid is one key problem for the low-squint-mode (within the range of [−5°, 5°]) multichannel in an azimuth high-resolution and wide-swath (HRWS) SAR system. In this paper, based on the electromagnetic scattering model, a novel weighted Doppler centroid estimation approach is proposed for the multichannel in the azimuth HRWS (MC-HRWS) SAR system. First, Maxwell’s equations are employed to derive the echo model for the multichannel SAR system. Then, an improved backscattering model is presented based on the small perturbation method and the available echo model, which is adopted to produce the weights for Doppler centroid estimation. More importantly, in order to improve the precision of Doppler centroid estimation, a weighted ambiguity-free Doppler centroid estimation approach is proposed, where the range-variant characteristic of Doppler centroid is employed to resolve the ambiguity number of Doppler centroid, and the echoes from different range bins with different signal-to-noise ratios are considered. In addition, the Cramer–Rao low bound of the estimated Doppler centroid is also discussed in this paper. The effectiveness of the proposed Doppler centroid estimation approach is verified via simulated and real measured low-squint-mode MC-HRWS SAR data. [ABSTRACT FROM AUTHOR]

Tandem-L is proposed as a spaceborne synthetic aperture radar (SAR) mission developed and operated by the German Aerospace Center in cooperation with several Helmholtz research centers and the German space industries. The mission concept comprises two fully polarimetric radar satellites providing monostatic and bistatic SAR imagery. A key feature of these SAR sensors is the employment of large lightweight unfurlable mesh reflectors fed by digital feed arrays. The main advantage of this new SAR system concept is the provision of large antenna apertures in space and flexible operation via reconfigurable feed electronics. By this, it becomes possible to map, for the first time, a continuous 350-km wide swath with a 7-m azimuth resolution with excellent noise equivalent sigma zero and ambiguity suppression. This paper shall give an overview on the technical aspects of the Tandem-L SAR instrument and antenna design. In particular, after a short review of the SAR system requirements, the concept of reflector SAR systems is outlined and the operation principle is presented. General guidelines for the design of array-fed reflector antennas with application to SAR imaging are given. Then, the optimization approach of the feed array design is detailed with a specific emphasis on a fixed beamforming concept in azimuth. In this context, also the problem of cross-pol pattern mitigation is addressed. These optimization steps are shown to be crucial for achieving the performance requirements in quad-pol acquisitions. Beamforming in elevation is performed onboard the spacecraft via digital hardware. This paper presents the beamforming architecture on receive for Tandem-L, which would apply in general for instance also to planar multielevation beam SAR antennas with Scan-On-Receive capabilities. Tandem-L is operated as a staggered SAR, which means varying the pulse repetition interval from pulse to pulse. In this context, the major design challenges are presented. Moreover, the impact of pulse staggering on the imaging performance is discussed. Tandem-L’s SAR performance is presented by means of numerical simulations showing that the performance requirements imposed by the scientific user community could be met. The final part of this paper addresses options for high azimuth resolution imaging as well as a beamforming method for enhanced range ambiguity suppression. [ABSTRACT FROM AUTHOR]

This paper proposes a novel method based on deformable dictionary learning for detecting the regions of change between multitemporal image pairs. We build on our previous work, which constructed a pair of dictionaries. The main shortcoming of this method was its dependence on a large amount of training data. In practice, there is often a shortage of ground-truthed training images, which limits the expression capability of the resulting dictionaries. This paper overcomes this challenge by incorporating the concept of deformation, wherein each atom of a dictionary is no longer a simple image patch, but instead is a flexible image deformation function. This enables the creation of more expressive dictionaries, capable of generalizing to a far greater variety of image patterns, while using a far smaller amount of ground-truthed images for supervised dictionary training. Deformation similarity is employed for patch matching to find the best set of atoms in the difference image (DI) dictionary for reconstructing image patches for a new input DI. Each such atom can be deformed to achieve a better match, thus extending generality while reducing the number of atoms needed in the dictionary. Multiple deformed atoms are weighted and combined to best reconstruct the input DI patch. Then, the same set of deformations and weights is projected to the corresponding atoms in the CD dictionary to obtain the output change-detection map. Experiments in six realistic synthetic aperture radar data sets demonstrate the robustness and efficiency of the proposed method in comparison with five other state-of-the-art methods from the literature. [ABSTRACT FROM AUTHOR]

A novel mixed mode radar coincidence imaging (MMRCI) system, which combines the coherent radar detection and incoherent radar coincidence imaging function during the radar coincidence imaging (RCI) process, is presented in this paper. The target location function of the MMRCI system gives the target position information synchronously with imaging tests, and this makes the MMRCI work independently with low target position estimation error. In the MMRCI system, the resolution of target distance estimation is proportional to the whole bandwidth assisted by the double-frequency linear frequency modulation (DFLFM) signal when the occupied bandwidth of the DFLFM signal is less than one-tenth of the whole bandwidth. The target position estimation error caused by the target motion can be reduced effectively so that the image quality of the moving target can be guaranteed in a longer detection time. The RCI is reconstructed using the incoherent part of the transmitting signal. A general relationship between the spatial correlation of the random radiation field in the imaging plane and the deployments of linear random source is analyzed in the paper. The imaging efficiency is ensured through the grading imaging method based on the imaging requirement. The effectiveness of the MMRCI method is validated via a set of simulations and experiments, including superresolution RCI of both stationary and moving targets in various scenes using the convex optimization algorithms. [ABSTRACT FROM AUTHOR]

Recently, deep convolutional neural networks (CNNs) have started to be applied for automatic target recognition (ATR) problems of synthetic aperture radar (SAR) images. In conventional SAR-ATR algorithms, the pose angle information of the target has been importantly used. However, recent deep learning-based SAR-ATR algorithms often only utilize the intensity information. In this paper, based on the prior works that the pose angle is an important latent variable for boosting target recognition performance, we propose a CNN-based SAR target recognition network with pose angle marginalization learning, called SPAM-Net that marginalizes the conditional probabilities of SAR targets over their pose angles to precisely estimate the true class probabilities. The proposed SPAM-Net consists of two sub-nets: (i) a sub-net for class-conditional probability estimation, called CP sub-net, and (ii) a sub-net for pose angle probability estimation, called PP sub-net. The two sub-nets are jointly learned via an end-to-end manner in a Bayesian framework so that the SPAM-Net incorporates the pose angle information into target recognition task effectively. The SPAM-Net outperforms our baseline network that does not utilize the pose angle information. In the experiments, we intensively analyze the effectiveness of pose angle information for SAR-ATR, revealing that more accurate pose angle information helps the SPAM-Net precisely estimate target classes for the misclassified target groups that are obtained by the baseline network. Furthermore, our method also outperforms the other state-of-the-art SAR-ATR algorithms, yielding the correct target recognition rate with average 99.61%. [ABSTRACT FROM AUTHOR]