Synthetic Aperture Radar Image Reconstruction Research Articles

Experimental evaluation of a multi-installment scheduling strategy based on divisible load paradigm for SAR image reconstruction on a distributed computing infrastructure

Radar loads, especially Synthetic Aperture Radar (SAR) image reconstruction loads use a large volume of data collected from satellites to create a high-resolution image of the earth. To design near-real-time applications that utilise SAR data, speeding up the image reconstruction algorithm is imperative. This can be achieved by deploying a set of distributed computing infrastructures connected through a network. Scheduling such complex and large divisible loads on a distributed platform can be designed using the Divisible Load Theory (DLT) framework. We performed distributed SAR image reconstruction experiments using the SLURM library on a cloud virtual machine network using two scheduling strategies, namely the Multi-Installment Scheduling with Result Retrieval (MIS-RR) strategy and the traditional EQual-partitioning Strategy (EQS). The DLT model proposed in the MIS-RR strategy is incorporated to make the load divisible. Based on the experimental results and performance analysis carried out using different pixel lengths, pulse set sizes, and the number of virtual machines, we observe that the time performance of MIS-RR is much superior to that of EQS. Hence the MIS-RR strategy is of practical significance in reducing the overall processing time, and cost, and in improving the utilisation of the compute infrastructure. Furthermore, we note that the DLT-based theoretical analysis of MIS-RR coincides well with the experimental data, demonstrating the relevance of DLT in the real world.

Research on SAR image denoising method based on feature extraction

AbstractDue to the excellent characteristics of Synthetic Aperture Radar (SAR), it has become an important technical means for land and sea target detection applications worldwide. However, the SAR image imaging process is often accompanied by speckle noise, which affects the accuracy of target detection. For this problem, this paper proposes an SAR image reconstruction model based on image denoising and feature extraction. Given a noisy SAR image, at each pixel of the image, the value of the singularity index of a metric defined by the signal gradient norm density is calculated. Then through a hierarchical decomposition of these singular indices, a set of pixels containing the most informative feature set of the image is extracted, and a denoised image is reconstructed using the extracted features. In comparison to traditional filtering and denoising techniques such as non‐local filtering, this algorithm achieves remarkable local filtering and denoising effects while preserving the image's texture information. The denoising performance of the proposed method is evaluated through a series of quantitative indicators and visual comparisons. The results demonstrate that this method effectively reduces background clutter, resulting in a higher contrast between the target and the background.

URBAN 3D RECONSTRUCTION OF VHR SAR IMAGES USING ITERATIVE OPTIMIZATION ALGORITHM AND LAYOVER FIXED-ORDER MODEL

Abstract. Synthetic Aperture Radar (SAR) Tomography (TomoSAR) is a three-dimensional SAR imaging technique that uses multiple passes to process complex SAR images and obtain three-dimensional spatial scattering information to derive the elevation scattering distribution. Due to its own shortcomings, the elevation obtained by the traditional spectral estimation method has low resolution in the elevation direction and is affected by noise. The imaging algorithm based on compressed sensing can achieve super-resolution reconstruction in the elevation direction while reducing the number of observations. However, the CS algorithm still faces challenges when applied to real-world tomographic SAR imaging. In particular, it often requires numerous iterations to achieve satisfactory results, which significantly reduces its processing efficiency in large-scale tomography. To address the above issues, in this paper, we proposed an urban 3D reconstruction of VHR SAR images using an iterative optimization algorithm and layover fixed-order model. The iterative optimization algorithm and the layover fixed-order model consist of two parts: The TomoSAR imaging equation is solved by the two-step iterative shrinkage/thresholding (TwIST) algorithm, and the number of scatterers K is estimated by the Bayesian Information Criterion (BIC). In this paper, the effectiveness of TwIST-BIC in TomoSAR imaging in urban areas is verified with real TerraSARX data. By comparing with the OMP algorithm based on matching tracking and the FISTA algorithm based on gradient descent. The TWIST-BIC method is less complex, converges faster, and combines both execution speed and super-resolution, which can effectively solve the processing efficiency problem in large-area tomography and acquire high-resolution tomographic analysis.

SAR Image Reconstruction via Incremental Imaging With Compressive Sensing

For synthetic aperture radar (SAR) imaging, the irregular loss of received data and the non-uniformly under sampling yield the SAR azimuth ambiguity (SAA) resulting in the degradation in image quality. To address this issue, the incremental SAR imaging approach based on the estimation of sensing dictionary matrix in the pursuit of sparsity is presented in this paper. Several beneficial contributions are included in the proposed method. First, the SAA reduction achievable in the proposed method is considerably improved more than that of the conventional compressive sensing (CS) based approach in terms of the image quality and computational efficiency. Second, we established the signal parameterization scheme which is divided into coarse and fine search steps to estimate the sensing matrix for SAR image restoration via signal model reconstruction. Third, an incremental imaging approach is devised to overcome the drawback of the conventional CS-based approach which is not sufficiently good leading to limited SAA reduction performance under the non-sparse SAR image. These contributions are verified using numerical simulations and experimental results.

Ship Detection in Low-Quality SAR Images via an Unsupervised Domain Adaption Method

Ship detection in low-quality Synthetic Aperture Radar (SAR) images poses a persistent challenge. Noise signals in complex environments disrupt imaging conditions, hindering SAR systems from acquiring precise target information, thereby significantly compromising the performance of detectors. Some methods mitigate interference via denoising techniques, while others introduce noise using classical methods to learn target features in the presence of noise. This conundrum is regarded as a cross-domain problem in this paper; a ship detection method in low-quality images is proposed to learn features of targets and shrink serious deterioration of detection performance by utilizing Generative Adversarial Networks (GANs). First, an image-to-image translation task is implemented using CycleGAN to generate low-quality SAR images with complex interference from the source domain to the target domain. Second, with the annotation inheritance, these generated SAR images participate in a training process to improve the detection accuracy and model robustness. Multiple experiments indicate that the proposed method conspicuously improves the detection performance and efficaciously reduces the missed detection rate in the SAR ship detection task. This cross-domain approach achieved outstanding improvements in the form of 11.0% mAP and 3.22% mAP on the GaoFen-3 ship dataset and SRSSD-V1.0, respectively. In addition, the characteristics and potentials of near-shore and off-shore SAR image reconstruction with style transfer based on Generative Adversarial Networks were explored and analyzed in this work.

SAR Image Reconstruction and Autofocus Using Complex-Valued Feature Prior and Deep Network Implementation

Synthetic aperture radar (SAR) plays an important role in remote sensing by providing electromagnetic images of the observation scene. The prior knowledge-based SAR image reconstruction method can reduce the requirement of data sampling ratio and improve image quality. The existing prior knowledge-based method usually uses the magnitude information of SAR images while ignoring the phase information. However, since the echo and backscattering coefficients are complex values, the phase information of SAR images will help improve the reconstruction accuracy in the image reconstruction process. To improve the reconstruction performance, this paper proposes a SAR image reconstruction and autofocus method using complex-valued feature prior. In the proposed method, the complex-valued feature prior is learned from data by a complex-valued feature projection operator (CFPO), which can characterize and extract scene features in Range-Doppler domain and 2D frequency domain. The proposed CFPO enables more efficient use of echo data and helps to improve image reconstruction and autofocus performance. In addition, the proposed method is implemented by an unfolded deep network, which enables data-driven feature learning and efficient computation. The proposed method is verified by simulated and measured data.

Sampling-based Spotlight SAR Image Reconstruction from Phase History Data for Speckle Reduction and Uncertainty Quantification

Spotlight mode airborne synthetic aperture radar (SAR) is a coherent imaging modality that is an important tool in remote sensing. Existing methods for spotlight SAR image reconstruction from phase history data typically produce a single image estimate which approximates the reflectivity of an unknown ground scene, and therefore provide no quantification of the certainty with which the estimate can be trusted. In addition, speckle affects all coherent imaging modalities causing a degradation of image quality. Many point estimate image reconstruction methods incorrectly treat speckle as additive noise resulting in an unnatural smoothing of the speckle that also reduces image contrast. The purpose of this paper is to address the issues of speckle and uncertainty quantification by introducing a sampling-based approach to SAR image reconstruction directly from phase history data. In particular, a statistical model for speckle as well as a corresponding sparsity technique to reduce it are directly incorporated into the model. Rather than a single point estimate, samples of the resulting joint posterior density are efficiently obtained using a Gibbs sampler, which are in turn used to derive estimates and other statistics which aid in uncertainty quantification. The latter information is particularly important in SAR, where ground truth images even for synthetically created examples are typically unknown. While similar methods have been deployed to process formed images, this paper focuses on the integration of these techniques into image reconstruction from phase history data. An example result using real-world data shows that, when compared with existing methods, the sampling-based approach introduced provides parameter-free estimates with improved contrast and significantly reduced speckle, as well as uncertainty quantification information.

Super-resolved synthetic aperture radar image reconstruction based on multiresolution fusion discrimination

Generative adversarial networks (GANs) are utilized for synthetic aperture radar (SAR) image super-resolution reconstruction, affording realistic texture details. However, existing GANs only discriminate the final generated high-resolution (HR) image after two consecutive upsampling processes, which ignore some high-frequency information of the reconstructed images. To resolve this issue, a multiresolution fusion discrimination (MRFD) algorithm is proposed to discriminate the reconstructed feature maps after each upsampling. First, a multiresolution discrimination process discriminates the authenticity of each upsampled feature map separately, which reduces the image distortion imposed during two consecutive upsampling processes. Besides, multiresolution feature fusion further preserves the consistent high-frequency texture structures. Finally, a multiscale dense network extracts image features in different scales, with multiscale dense block’s dense connections improving parameter utilization. The experimental results on a SAR dataset demonstrate that the proposed MRFD algorithm performs better in reconstructing the texture details of HR images.

SAR Signal Formation and Image Reconstruction of a Moving Sea Target

Maritime application of synthetic aperture radar (SAR) technology for sea-target surveillance and imaging is considered in this study. A SAR scenario, including the kinematics of a SAR satellite and a ship moving on the sea, along with the geometry of the target, are analytically described. A linear frequency modulation (LFM) waveform is applied for the target’s illumination. Based on the target’s geometry, SAR and target kinematics and the LFM waveform, a SAR signal model is synthesized. It is proven that the process of signal formation is a transformation of the three-dimensional (3D) image into a two-dimensional (2D) signal, whereas the target’s 2D imaging is an inverse transformation of the 2D signal into the target’s 2D image. SAR signal components, linear Fourier terms and higher-order phase terms are analytically derived and discussed in detail. Moreover, it is proven that SAR image reconstruction is a motion-compensation procedure, i.e., it removes all phases induced by first- and higher-order motion. Based on the SAR signal analysis, an illustrative iterative image-reconstruction algorithm is derived. The quality of the imaging is evaluated by an entropy cost function. Simulation experiments are carried out to verify the correctness of the theoretical statements in respect of SAR signal formation and image reconstruction.

Restoration of speckle noise corrupted SAR images using regularization by denoising

Speckle noise removal is a well-established problem in synthetic aperture radar (SAR) image processing. Among different methods focused on the reconstruction of SAR images, variational models have achieved state-of-the-art performance. In this paper, a Rayleigh based speckle reduction algorithm is developed using the variational framework. The forward model is combined with recently proposed regularization by denoising (RED) prior. However, RED has been proposed in literature for the additive noise model. Multiplicative noise in SAR images prevents the direct application of RED to variational models. Hence, logarithm transformation is applied to change the multiplicative noise model to additive model, and the forward model from Rayleigh to Fisher–Tippett distribution. The resulting optimization problem is solved using the alternating direction method of multipliers. Further, the proof of the convergence analysis is carried out for the above framework. Simulations convey that the proposed method has better despeckling performance compared to that of state-of-the-art methods.

3D Sparse SAR Image Reconstruction Based on Cauchy Penalty and Convex Optimization

Three-dimensional (3D) synthetic aperture radar (SAR) images can provide comprehensive 3D spatial information for environmental monitoring, high dimensional mapping and radar cross sectional (RCS) measurement. However, the SAR image obtained by the traditional matched filtering (MF) method has a high sidelobe and is easily disturbed by noise. In order to obtain high-quality 3D SAR images, sparse signal processing has been used in SAR imaging in recent years. However, the typical L1 regularization model is a biased estimation, which tends to underestimate the target intensity. Therefore, in this article, we present a 3D sparse SAR image reconstruction method combining the Cauchy penalty and improved alternating direction method of multipliers (ADMM). The Cauchy penalty is a non-convex penalty function, which can estimate the target intensity more accurately than L1. At the same time, the objective function maintains convexity via the convex non-convex (CNC) strategy. Compared with L1 regularization, the proposed method can reconstruct the image more accurately and improve the image quality. Finally, three indexes suitable for SAR images are used to evaluate the performance of the method under different conditions. Simulation and experimental results verify the effectiveness of the proposed method.

Three-Dimensional Sparse SAR Imaging with Generalized Lq Regularization

Three-dimensional (3D) synthetic aperture radar (SAR) imaging provides complete 3D spatial information, which has been used in environmental monitoring in recent years. Compared with matched filtering (MF) algorithms, the regularization technique can improve image quality. However, due to the substantial computational cost, the existing observation-matrix-based sparse imaging algorithm is difficult to apply to large-scene and 3D reconstructions. Therefore, in this paper, novel 3D sparse reconstruction algorithms with generalized Lq-regularization are proposed. First, we combine majorization–minimization (MM) and L1 regularization (MM-L1) to improve SAR image quality. Next, we combine MM and L1/2 regularization (MM-L1/2) to achieve high-quality 3D images. Then, we present the algorithm which combines MM and L0 regularization (MM-L0) to obtain 3D images. Finally, we present a generalized MM-Lq algorithm (GMM-Lq) for sparse SAR imaging problems with arbitrary q0≤q≤1 values. The proposed algorithm can improve the performance of 3D SAR images, compared with existing regularization techniques, and effectively reduce the amount of calculation needed. Additionally, the reconstructed complex image retains the phase information, which makes the reconstructed SAR image still suitable for interferometry applications. Simulation and experimental results verify the effectiveness of the algorithms.

Compressive Synthetic Aperture Radar Imaging and Autofocusing by Augmented Lagrangian Methods

We consider the problem of synthetic aperture radar (SAR) image reconstruction from undersampled data in the presence of phase errors. We formulate the problem as one of estimating both the phase errors and the underlying image simultaneously through optimization. Within that optimization framework, we use a combination of priors that have proven useful in radar imaging, including strong sparse scattering in the image domain, as well as piecewise smoothness (i.e., gradient sparsity). To solve the resulting optimization problems, we propose an alternating direction method of multipliers (ADMM), an augmented Lagrangian method, for compressive SAR image reconstruction. We use simulated and real data at varying undersampling rates to study the performance of the proposed method and compare it against existing methods in terms of convergence speed and reconstruction quality. Our method provides significant improvements in terms of image reconstruction quality and computation speed, as well as phase error estimation performance.

Target-Oriented SAR Imaging for SCR Improvement via Deep MF-ADMM-Net

Synthetic aperture radar (SAR) is an important means for target surveillance through reconstructing the microwave image of the observation area. However, under the condition of low signal-to-clutter ratio (SCR), such as a strong sea clutter situation, it is difficult to surveil targets from SAR images acquired by the traditional matched filter-based imaging methods. To improve the target surveillance performance of SAR, this article proposes a target-oriented SAR imaging method, which can enhance the desired target and improve the SCR in the reconstructed SAR images. By separating the target area from the clutter area, we first establish a target-oriented SAR imaging model, where the generalized regularization is used to characterize the features of the target, contributing to the improvement of SCR in the reconstructed image. Then, the imaging model is solved through a deep network, MF-ADMM-Net, which is obtained by unfolding an alternating direction method of multipliers (ADMM)-based iterative solution. In addition, the training strategy is formulated with the consideration of complex values. Experiments are conducted to verify the performance of image reconstruction and SCR improvement of the proposed method, and comparisons show the superiority of MF-ADMM-Net in effect and efficiency.

An Unambiguous Imaging Method of Moving Target for Maritime Scenes With Spaceborne High-Resolution and Wide-Swath SAR

In azimuth multichannel high-resolution and wide-swath (AMC-HRWS) synthetic aperture radar (SAR) system, undersampling of azimuth signal results in the failure of the single-channel echo imaging, so the echoes of all the channels should be reconstructed to obtain the unambiguous SAR image. Many methods have been proposed to reconstruct the stationary scene echoes. However, moving targets (MTs) will cause false targets in the reconstructed SAR image, which has a great influence on SAR image interpretation and moving target detection, especially in maritime scenes. Hence, we propose an unambiguous imaging method of the moving target for maritime scenes with AMC-HRWS SAR. First, azimuth deramp processing is introduced to obtain coarse-focused moving targets, and then their echoes can be extracted from the sea clutter. Based on the extracted data, a radial velocity estimation method is proposed, which is high efficiency and does not require redundant channels. In addition, based on the estimated radial velocity and the characteristics of the coarse-focused signal, a novel signal reconstruction method of moving target is presented. The reconstruction performance of this method is not affected by channel imbalance, and false targets can be removed even if there is radial velocity error. Besides, the effectiveness of this scheme is verified by the simulation data and GaoFen-3 SAR real data. The estimated radial velocities of ships are verified by automatic identification system (AIS) information. The imaging results show that false targets are effectively suppressed, and moving targets are located in correct positions.

Compressive Sensing-Based SAR Image Reconstruction from Sparse Radar Sensor Data Acquisition in Automotive FMCW Radar System.

In this paper, we propose a method for reconstructing synthetic aperture radar (SAR) images by applying a compressive sensing (CS) technique to sparsely acquired radar sensor data. In general, SAR image reconstruction algorithms require radar sensor data acquired at regular spatial intervals. However, when the speed of the radar-equipped platform is not constant, it is difficult to consistently perform regular data acquisitions. Therefore, we used the CS-based signal recovery method to efficiently reconstruct SAR images even when regular data acquisition was not performed. In the proposed method, we used the l1-norm minimization to overcome the non-uniform data acquisition problem, which replaced the Fourier transform and inverse Fourier transform in the conventional SAR image reconstruction method. In addition, to reduce the phase distortion of the recovered signal, the proposed method was applied to each of the in-phase and quadrature components of the acquired radar sensor data. To evaluate the performance of the proposed method, we conducted experiments using an automotive frequency-modulated continuous wave radar sensor. Then, the quality of the SAR image reconstructed with data acquired at regular intervals was compared with the quality of images reconstructed with data acquired at non-uniform intervals. Using the proposed method, even if only 70% of the regularly acquired radar sensor data was used, a SAR image having a correlation of 0.83 could be reconstructed.

Sparse SAR Imaging and Quantitative Evaluation Based on Nonconvex and TV Regularization

Sparse signal processing has been used in synthetic aperture radar (SAR) imaging due to the maturity of compressed sensing theory. As a typical sparse reconstruction method, L1 regularization generally causes bias effects as well as ignoring region-based features. Our team has proposed to linearly combine the nonconvex penalty and the total variation (TV)-norm penalty as a compound regularizer in the imaging model, called nonconvex and TV regularization, which can not only reduce the bias caused by L1 regularization but also enhance point-based and region-based features. In this paper, we use the variable splitting scheme and modify the alternating direction method of multipliers (ADMM), generating a novel algorithm to solve the above optimization problem. Moreover, we analyze the radiometric properties of sparse-signal-processing-based SAR imaging results and introduce three indexes suitable for sparse SAR imaging for quantitative evaluation. In experiments, we process the Gaofen-3 (GF-3) data utilizing the proposed method, and quantitatively evaluate the reconstructed SAR image quality. Experimental results and image quality analysis verify the effectiveness of the proposed method in improving the reconstruction accuracy and the radiometric resolution without sacrificing the spatial resolution.

Plug-and-Play Synthetic Aperture Radar Image Formation Using Deep Priors

The reconstruction of synthetic aperture radar (SAR) images from phase history data is an ill-posed inverse problem which, in several lines of recent work, is solved by minimizing a cost function. Existing reconstruction methods use regularization to tackle the ill-posed nature of the imaging task. However, in general, these regularizers are either too simple to capture complex spatial patterns and can only promote fixed, predefined features, or lead to non-quadratic cost functions that are nontrivial to minimize. Recently emerging plug-and-play (PnP) priors technique is a flexible framework that allows forward models of imaging systems to be integrated with state-of-the-art regularizers. Inspired by this, we propose a novel PnP SAR image reconstruction framework for spotlight-mode SAR. SAR involves complex-valued reflectivities with spatial structure on the reflectivity magnitudes that can be learned and imposed as priors. This distinguishing aspect is formulated into our proposed framework. We demonstrate the use and effectiveness of a convolutional neural network (CNN) based prior model for the reconstruction of synthetic and real SAR scenes and compare the results with FFT-based, non-quadratic regularization-based, and dictionary learning-based reconstruction methods as well as a PnP framework with BM3D regularizer. Our results suggest that these deep priors enable the learning and incorporation of complicated spatial patterns more effectively than existing methods, and produce significantly improved images especially from limited observations.

Polar Format-Based Compressive SAR Image Reconstruction With Integrated Autofocus

In this article, we develop a polar format-based algorithm for recovering synthetic aperture radar (SAR) imagery from sparsely/randomly sampled data in the presence of motion compensation/phase errors. Our method integrates conventional SAR autofocus algorithms into an $\ell _{1}$ norm penalized image recovery problem. We demonstrate the effectiveness of this approach using a real airborne SAR data set covering a large scene.

Analysis of Artifacts in SAR Imagery Due to Fluctuation in Refractive Index

In this paper, we analyze the artifacts in synthetic aperture radar imagery due to fluctuations in the refractive index of the atmosphere, specifically due to absorption and variations in speed of light. SAR image reconstruction algorithms ignore absorption and assume unit refractive index. We assume a complex-valued refractive index for the atmosphere and analyze the errors in reconstructed images due to absorption and fluctuations in the speed of electromagnetic (EM) waves. Absorption leads to amplitude errors, which can be viewed as a filtering error. This error can be characterized as systematic variations in the ground reflectivity function. Fluctuations in the speed of EM waves lead to phase errors, which manifest as positioning errors or geometric distortions in synthetic aperture radar (SAR) imagery. We perform numerical simulations to verify our analysis.