Azimuth ambiguity in azimuth multichannel system (AMCS) results from nonuniform sampling and degrades the synthetic aperture radar (SAR) imaging quality. In addition, the uniform pulse transmission pattern adopted in most AMCS is difficult to balance the azimuth resolution and range swath width performance. Furthermore, the design of pulse transmission pattern and reconstruction algorithm are often considered as two independent problems, which is difficult to achieve the optimal high-resolution and wide-swath (HRWS) imaging performance. In this article, we propose a joint optimization network to learn the nonuniform pulse transmission pattern and train reconstruction network parameters simultaneously, dubbed PTIR-Net. Specifically, we develope a nonuniform multichannel imaging model, consisting of the nonuniform approximate measurement operators with continuously defined pulse transmission time. To facilitate a more stable and generalized network, we introduce the implicit regularization learned by a model-based deep learning image reconstruction scheme to process the nonuniform sampling data. Extensive experiments on Gaofen-3 dataset show that the proposed network achieves better reconstruction performance in terms of both quantitative metrics and visual quality.

Synthetic Aperture Radar (SAR) 3D point cloud reconstruction improves target identification by mitigating issues like overlay masking and shadows found in 2D SAR image projections. Nevertheless, challenges arise from limited SAR data availability and complexities in deciphering and labeling, hindering research in SAR 3D reconstruction point cloud semantic segmentation. To address these hurdles, we propose an alternative training approach—shifting from LiDAR to SAR point clouds. Leveraging benchmark datasets in the LiDAR domain, we advocate using LiDAR point clouds for training to counter the scarcity of SAR training sets. However, applying a segmentation model across different domains leads to a performance decline, particularly in cross-modal SAR-reconstructed point clouds, attributed to distinct roughness introduced by outliers. This paper introduces a 3D semantic segmentation framework based on unsupervised domain adaptation (UDA) for cross-modal learning from LiDAR to SAR. Additionally, we present a simple yet effective geometric transformation data augmentation technique to handle highly imbalanced data distribution. Experimental results confirm the feasibility and effectiveness of our proposed method for SAR 3D reconstructed point cloud semantic segmentation.

Azimuth multichannel synthetic aperture radar (SAR) consistently encounters challenges associated with nonuniform sampling during its operation. The use of the conventional matrix inversion method leads to an increase in sampling irregularity, exacerbating noise interference during signal reconstruction and, consequently, contributing to a gradual decline in signal fidelity. This paper introduces a novel method to construct an L2regularized reconstruction matched filter, effectively mitigating noise impact prevalent in scenarios of extensive nonuniform sampling. The proposed method is highly computationally efficient, and the signal-to-noise ratio (SNR) index can be expressed in an unambiguous manner. Comparative simulation experiments illustrate the significant improvement in SNR performance achieved by the novel method in comparison to the traditional matrix inversion method.

A pulse repetition frequency (PRF) below the Doppler bandwidth is pivotal in reducing the costs of data transmission and increasing the swath width for synthetic aperture radar (SAR). However, azimuth undersampling leads to severe azimuth ambiguity. Existing methods for azimuth ambiguity suppression do not perform adequately under downsampling rate. To overcome the shortcomings of existing methods, this letter proposes an efficient deep unfolding network that combines group sparse and adaptive threshold techniques for SAR imaging, named group sparse and adaptive threshold techniques for SAR imaging (GSAT-Net). In GSAT-Net, thresholds and sparsity are incorporated for both the main area and ambiguity areas, allowing for threshold shrinkage that is not only layer-varied but also element-wise. Finally, the effectiveness of the proposed method is verified by visual comparisons and numerical analysis.

Synthetic aperture radar (SAR) tomography (TomoSAR) retrieves three-dimensional (3-D) information from multiple SAR images, effectively addresses the layover problem, and has become pivotal in urban mapping. Unmanned aerial vehicle (UAV) has gained popularity as a TomoSAR platform, offering distinct advantages such as the ability to achieve 3-D imaging in a single flight, cost-effectiveness, rapid deployment, and flexible trajectory planning. The evolution of compressed sensing (CS) has led to the widespread adoption of sparse reconstruction techniques in TomoSAR signal processing, with a focus on ℓ1norm regularization and other grid-based CS methods. However, the discretization of illuminated scene along elevation introduces modeling errors, resulting in reduced reconstruction accuracy, known as the “off-grid" effect. Recent advancements have introduced gridless CS algorithms to mitigate this issue. This paper presents an innovative gridless 3-D imaging framework tailored for UAV-borne TomoSAR. Capitalizing on the pulse repetition frequency (PRF) redundancy inherent in slow UAV platforms, a multiple measurement vectors (MMV) model is constructed to enhance noise immunity without compromising azimuth-range resolution. Given the sparsely placed array elements due to mounting platform constraints, an atomic norm soft thresholding algorithm is proposed for partially observed MMV, offering gridless reconstruction capability and super-resolution. An efficient alternative optimization algorithm is also employed to enhance computational efficiency. Validation of the proposed framework is achieved through computer simulations and flight experiments, affirming its efficacy in UAV-borne TomoSAR applications.

Sparse signal processing techniques, such as compressed sensing (CS), are commonly used in tomographic synthetic aperture radar (TomoSAR) imaging due to the sparsity present in the elevation direction. However, classical CS methods, such as$\ell _{1}$-norm regularization and orthogonal matching pursuit (OMP), suffer from the off-grid effect. Specifically, they discretize the elevation axis into multiple grids and assume that scatterers are located precisely on the grids, leading to reconstruction results that deviate from the true heights of the scatterers. Although gridless CS methods, such as atomic norm minimization (ANM), have achieved gridless reconstruction in specific scenarios, they face challenges such as the requirement of uniformly distributed baselines and large computational cost. In this article, we propose a gridless CS method based on the alternate descent conditional gradient (ADCG) kernel for TomoSAR inversion and compare it with ANM, iterative soft thresholding (IST), and OMP. We show through numerical simulations and experimental results that our proposed method is applicable not only to scenarios with uniformly distributed baselines but also to scenarios with nonuniformly distributed baselines, and it resolves the off-grid effect in both cases. Finally, we demonstrate the effectiveness of our proposed method by implementing gridless reconstruction using an actual dataset of urban buildings in Yuncheng.

Breaking the constraint of pulse repetition frequency (PRF) is one of the important development trends of synthetic aperture radar (SAR). Within the conventional azimuth sampling patterns, severe ambiguity arises when confronted at a low PRF. Conversely, elevated PRF introduces considerable data redundancy, thereby culminating in wasting of resources. To address these issues, this paper proposes a novel joint optimization network for sparse SAR imaging and azimuth undersampling pattern grounded in the model-based reconstruction using deep learned priors (MoDL) architecture, combined with matched filter (MF) approximate measurement operators, named MF-based sampling pattern Joint optimization MoDL sparse SAR imaging Network (MF-JMoDL-Net). The MF-JMoDL-Net incorporates non-uniform sampling operators, enabling the sampling positions to be learnable, and achieves the groundbreaking joint optimization of the sampling pattern and ambiguity suppression. When the PRF is below the Nyquist sampling rate, the proposed network can acquire SAR images with minimal ambiguity and optimal imaging quality. Furthermore, the final learned undersampling pattern can be visualized and combined with the SAR echo signal semantics for mutual feedback. Extensive experiments on simulated and real scenes datasets are conducted to demonstrate the effectiveness and superiority of the proposed framework in imaging results.

Tomographic SAR (TomoSAR) technology has gained significant attention in recent years due to its three-dimensional imaging capability. However, in practical applications, phase errors between different channels can degrade the quality of three-dimensional imaging. Current state-of-the-art methods for phase error compensation based on autofocus techniques suffer from high computational complexity, making them unsuitable for large-scale three-dimensional imaging. In this paper, we propose a multi-channel phase error estimation method based on error back-propagation training optimization. By utilizing the TomoSAR model that incorporates phase errors from multiple channels, we construct a matrix containing the parameters to be estimated for inter-channel phase errors. Through stochastic gradient descent algorithm, we iteratively optimize the parameters of the phase error matrix, ultimately obtaining an estimation of the inter-channel phase errors. Experimental results validate the accuracy of the proposed method.

Ship target recognition is a vital task in synthetic aperture radar (SAR) imaging applications. Although convolutional neural networks have been successfully employed for SAR image target recognition, surpassing traditional algorithms, most existing research concentrates on the amplitude domain and neglects the essential phase information. Furthermore, several complex-valued neural networks utilize average pooling to achieve full complex values, resulting in suboptimal performance. To address these concerns, this paper introduces a Complex-valued Convolutional Neural Network (CVGG-Net) specifically designed for SAR image ship recognition. CVGG-Net effectively leverages both the amplitude and phase information in complex-valued SAR data. Additionally, this study examines the impact of various widely-used complex activation functions on network performance and presents a novel complex max-pooling method, called Complex Area Max-Pooling. Experimental results from two measured SAR datasets demonstrate that the proposed algorithm outperforms conventional real-valued convolutional neural networks. The proposed framework is validated on several SAR datasets.

State-of-the-art phase linking (PL) methods for distributed scatterer interferometry (DSI) retrieve consistent phase histories from the sample coherence matrix or the one whose magnitudes are calibrated. To unify them, we first propose a framework consisting of sample coherence matrix estimation and Kullback–Leibler (KL) divergence minimization. Within such framework, we observe that the current state-of-the-art PL methods mainly focus on calibrating the magnitudes of sample coherence matrix while ignoring the errors caused by it exploited in the complex domain, especially when the PL problem is large-dimensional. In this paper, “large-dimensional” refers to the case where the temporal dimensionNof coherence matrices and the numberPof statistically homogeneous pixels (SHP) are at the same level. To solve this issue, we further propose a PL method, termed LaMIE, which is aimed at precise phase history retrieval from large-dimensional coherence matrices for DSI. It includes two steps: 1) sample coherence matrix shrinkage to calibrate the matrix in complex and real domains and 2) phase history retrieval via the flat coherence metric. Both simulated and real data experiments validate the effectiveness of the proposed method by comparing it with other PL methods. Through LaMIE, the densities of the selected points with stable phases can be significantly improved, and the displacement velocities for more regions can be obtained than with state-of-the-art methods.

This paper presents a novel efficient method for gridless line spectrum estimation problem with single snapshot and sparse signals, namely the gradient descent least squares (GDLS) method. Conventional single snapshot (a.k.a. single measure vector or SMV) line spectrum estimation methods either rely on smoothing techniques that sacrificing the range and/or azimuth resolution, or adopt the sparsity constraint and utilize compressed sensing (CS) method by defining prior grids and resulting in the off-grid problem. Recently emerged atomic norm minimization (ANM) methods achieved gridless SMV line spectrum estimation, but its computational complexity is extremely high; thus it is practically infeasible in real applications with large problem scales. Our proposed GDLS method reformulates the line spectrum estimations problem into a least squares (LS) estimation problem and solves the corresponding objective function via gradient descent algorithm in an iterative fashion with efficiency. The convergence guarantee, computational complexity, as well as performance analysis for evenly distributed antenna array case are discussed in this paper. Numerical simulations show that the proposed GDLS algorithm outperforms the state-of-the-art methods e.g., CS and ANM, in terms of estimation performances. It can completely avoid the off-grid problem, and its computational complexity is significantly lower than ANM. Our method has been tested in tomographic SAR (TomoSAR) imaging applications via simulated and real experiment data. Results show great potential of the proposed method in terms of better cloud point performance and eliminating the gridding effect.

Tomographic SAR technique has attracted remarkable interest for its ability of three-dimensional resolving along the elevation direction via a stack of SAR images collected from different cross-track angles. The emerged compressed sensing (CS)-based algorithms have been introduced into TomoSAR considering its super-resolution ability with limited samples. However, the conventional CS-based methods suffer from several drawbacks, including weak noise resistance, high computational complexity, and complex parameter fine-tuning. Aiming at efficient TomoSAR imaging, this paper proposes a novel and efficient sparse unfolding network based on the analytic learned iterative shrinkage thresholding algorithm (ALISTA) architecture with adaptive threshold, named Adaptive Threshold ALISTA-based Sparse Imaging Network (ATASI-Net). The weight matrix in each layer of ATASI-Net is pre-calculated as the solution of an off-line optimization problem, leaving only two scalar parameters to be learned from data, which significantly simplifies the training stage. Furthermore, the introduction of an adaptive threshold for each azimuth-range pixel permits the threshold shrinkage to be not only layer-varied but also element-wise. Additionally, the final learned thresholds can be visualized and combined with the SAR image semantics for mutual feedback. Finally, extensive experiments on simulated and real data are carried out to demonstrate the effectiveness and efficiency of the proposed method.

Through the development of Synthetic Aperture Radar (SAR) technology, it is now possible to observe dynamic processes on the earth with fine temporal resolution by forming SAR time series. Nonetheless, such sequential images remain difficult to interpret due to the speckle effect. Despeckling them is further complicated by outliers caused by abrupt changes in weather conditions or the appearance of objects. In spite of the fact that many state-of-the-art methods can achieve excellent filtering performances over stable areas, they often result in artifacts in those areas where outliers existed at the time of acquisition. To simultaneously mitigate the speckle noise and extract outliers, we propose a novel SAR time series despeckling method based on nonlocal total variation regularized robust principle component analysis, which is termed SAR-NL-TVRPCA. By comparing it to other state-of-the-art methods, the effectiveness of its despeckling has been validated in real data experiments. Furthermore, the extracted outliers can provide insight into abrupt changes occurring throughout the observation period, which provides byproducts for further analysis.

Interferometric phase restoration is a crucial step in retrieving large-scale geophysical parameters from Synthetic Aperture Radar (SAR) images. Existing noise impacts the accuracy of parameter retrieval as a result of decorrelation effects. Most state-of-the-art filtering methods belong to the group of nonlocal filters. In this paper, we propose a novel convolutional sparse coding method in complex domain with the prior knowledge of coherence integrated into the optimization model, which is termed as CoComCSC. CoComCSC is not only capable of reducing noise in regions with continuous phase changes, but also of preserving the phase details prominently. The experiments results on simulated and real data demonstrate the effectiveness of CoComCSC by comparing with other state-of-the-art methods. Moreover, the obtained Digital Elevation Model (DEM) product by CoComCSC from RADARSAT-2 data indicates its superior filtering performance over regions with heterogeneous land-covers, which shows its great potential for generating high-resolution DEM products.

The azimuth multichannel SAR is competent to achieve high-resolution and wide-swath (HRWS) imaging. For some spaceborne multichannel SAR systems, the pulse repetition frequency (PRF) of each channel at some beam positions is less than that of uniform sampling, hence leading to the azimuth ambiguities in the recovered images. In this paper, a novel azimuth ambiguity suppression method for multichannel SAR imaging based on$L_{2,q}$regularization$(0 < q\leq 1)$is proposed. First, by analyzing the reasons of azimuth ambiguities in multichannel SAR, we establish the imaging model different from that in single-channel SAR system. Second, we extend the$L_{2,q}$regularization from single-channel SAR system to multichannel SAR system and develop the proposed method. Finally, we demonstrate the effectiveness of the proposed method for non-sparse scenarios. Simulations and Gaofen-3 real data experiments are carried out to verify the validity of proposed method.

This paper presents an efficient optimization technique for super-resolution two-dimensional (2D) direction of arrival (DOA) estimation by introducing a new formulation of atomic norm minimization (ANM). ANM allows gridless angle estimation for correlated sources even when the number of snapshots is far less than the antenna size, yet it incurs huge computational cost in 2D processing. This paper introduces a novel formulation of ANM via semi-definite programming, which expresses the original high-dimensional problem by two decoupled Toeplitz matrices in one dimension, followed by 1D angle estimation with automatic angle pairing. Compared with the state-of-the-art 2D ANM, the proposed technique reduces the computational complexity by several orders of magnitude with respect to the antenna size, while retaining the benefits of ANMin terms of super-resolution performance with use of a small number of measurements, and robustness to source correlation and noise. The complexity benefits are particularly attractive for large-scale antenna systems such as massive MIMO and radio astronomy.

Recently, the sparse reconstruction algorithms (SRAs) based on compressive sensing (CS) have been applied in the fields of synthetic aperture radar (SAR) imaging and show plenty of potential advantages. However, due to the great computational complexity and memory cost caused by matrix-vector multiplications, most of these algorithms are not suitable to reconstruct large-scale observed scenes. To solve this problem, we construct a backprojection based imaging operator, and introduce it to the complex approximate message passing algorithm (CAMP). The new image formation algorithm is called BP-CAMP in this paper. Compared with the approximated observation methods deduced from the FFT-based imaging technology, BP-CAMP is not limited by observation models of the radar and motion modes of the platform, and it therefore possesses universal applicability. By the simulations and real data processing, the experimental results show that BP-CAMP has lower computational complexity and memory cost than CAMP, and also achieves SAR imaging with under-sampled echo data.

Microwave imaging, or synthetic aperture radar (SAR) shows its remarkable importances in various fields of remote sensing. Modern SAR system usually comes with high imaging resolution and wide mapping swath. This brings difficulties to the future development of SAR system. As a solution, the concept of SAR imaging under sparse constraint, or sparse microwave imaging radar is suggested, which is mainly the idea of introducing the sparse signal processing theory to the radar imaging. Under the sparse constraint, this technique could bring us benefits including better imaging performance e.g. lower ambiguity, higher resolution, lower side lobe and lower system complexity [1, 2, 3, 4].

This paper focus on the polarimetric synthetic aperture radar (SAR) tomography for forested areas based on compressive MUSIC. In the proposed method, full polarimetric SAR echo signal reflected from the imaging area is collected, the corresponding multiple measurement vector model is established according to the parameters of polarimetric channels, the wavelet basis is adopted for representing the sparse vertical structure of the imaging area, and finally, the backscattering coefficients of the area are reconstructed by compressive MUSIC algorithm. The necessary number of tracks for SAR tomography is reduced and the severity of spurious spikes is suppressed under the same measurement accuracy. Simulation results from the PolSARpro data validate the effectiveness.