Synthetic Aperture Radar Image Quality Research Articles

Lightweight Super-Resolution Generative Adversarial Network for SAR Images

Due to a unique imaging mechanism, Synthetic Aperture Radar (SAR) images typically exhibit degradation phenomena. To enhance image quality and support real-time on-board processing capabilities, we propose a lightweight deep generative network framework, namely, the Lightweight Super-Resolution Generative Adversarial Network (LSRGAN). This method introduces Depthwise Separable Convolution (DSConv) in residual blocks to compress the original Generative Adversarial Network (GAN) and uses the SeLU activation function to construct a lightweight residual module (LRM) suitable for SAR image characteristics. Furthermore, we combine the LRM with an optimized Coordinated Attention (CA) module, enhancing the lightweight network’s capability to learn feature representations. Experimental results on spaceborne SAR images demonstrate that compared to other deep generative networks focused on SAR image super-resolution reconstruction, LSRGAN achieves compression ratios of 74.68% in model storage requirements and 55.93% in computational resource demands. In this work, we significantly reduce the model complexity, improve the quality of spaceborne SAR images, and validate the effectiveness of the SAR image super-resolution algorithm as well as the feasibility of real-time on-board processing technology.

Synthetic Aperture Radar Radio Frequency Interference Suppression Method Based on Fusing Segmentation and Inpainting Networks

Synthetic Aperture Radar (SAR) is a high-resolution imaging sensor commonly mounted on platforms such as airplanes and satellites for widespread use. In complex electromagnetic environments, radio frequency interference (RFI) severely degrades the quality of SAR images due to its widely varying bandwidth and numerous unknown emission sources. Although traditional deep learning-based methods have achieved remarkable results by directly processing SAR images as visual ones, there is still considerable room for improvement in their performance due to the wide coverage and high intensity of RFI. To address these issues, this paper proposes the fusion of segmentation and inpainting networks (FuSINet) to suppress SAR RFI in the time-frequency domain. Firstly, to weaken the dominance of RFI in SAR images caused by high-intensity interference, a simple CCN-based network is employed to learn and segment the RFI. This results in the removal of most of the original interference, leaving blanks that allow the targets to regain dominance in the overall image. Secondly, considering the wide coverage characteristic of RFI, a U-former network with global information capture capabilities is utilized to learn the content covered by the interference and fill in the blanks created by the segmentation network. Compared to the traditional Transformer, this paper enhances its global information capture capabilities through shift-windows and down-sampling layers. Finally, the segmentation and inpainting networks are fused together through a weighted parameter for joint training. This not only accelerates the learning speed but also enables better coordination between the two networks, leading to improved RFI suppression performance. Extensive experimental results demonstrate the substantial performance enhancement of the proposed FuSINet. Compared to the PISNet+, the proposed attention mechanism achieves a 2.49 dB improvement in peak signal-to-noise ratio (PSNR). Furthermore, compared to Uformer, the FuSINet achieves an additional 4.16 dB improvement in PSNR.

Entropy-Based re-sampling method on SAR class imbalance target detection

Detection tasks based on Synthetic aperture radar (SAR) images have been studied widely but severely constrained by the quality of datasets. Meanwhile, both the unperceived category imbalance problem and SAR image discrepancy of multi-class SAR datasets are not fully considered. Researchers usually care about the foreground-background imbalance more than the class imbalance for SAR images. To solve these problems, an entropy-based re-sampling method is proposed in which category imbalance and quality discrepancies of SAR images are both considered. Initially, the relationship is established between variance-weighted image entropy and factors affecting SAR image quality, such as noise, resolution, and density, thereby validating entropy as a robust metric for image quality assessment. Subsequently, quantity penalty scores of categories and difficulty penalty scores of each image are calculated separately to capture the inter-class and intra-class disparities. Next, logarithmic smoothing is employed to avoid overestimation of image difficulty due to the margin effect. Finally, all these scores are combined to generate normalized scores representing the final distribution of the dataset to guide the training process. The proposed approach serves as a plug-and-play strategy for general SAR detection tasks, and experimental results indicate a significant performance improvement. Specifically, detection accuracy in terms of AP for airplanes and bridges (minority classes) in the MSAR dataset is improved by 16.6 % and 13.0 %, respectively, compared to the YOLOv5 baseline, with only a minimal 1.6 % sacrifice in ship detection (majority class). The datasets and the codes can be found at https://www.radars.ac.cn/web/data/getData?dataType=MSAR, https://github.com/Phoenix0qi/Yolov5-entropy-balance/tree/master.

Inverse synthetic aperture radar image quality assessment based on PSO-BP neural network

ABSTRACT Based on particle swarm optimization-back propagation (PSO-BP) neural network, a novel inverse synthetic aperture radar (ISAR) image quality assessment (IQA) method is proposed to address the challenge of selecting high-quality ISAR images from a large number of images. This method incorporates six assessment metrics: energy gradient (EG), effective area ratio of image (EARI), image contrast (IC), image entropy (IE), equivalent number of looks (ENL), and signal-to-noise ratio (SNR) to evaluate the quality of ISAR images. The PSO-BP neural network is employed to fit the relationship between these metrics and ISAR image quality. Experimental results show that the new method is effective in ISAR-IQA under different imaging conditions and various attitudes, and closely matches the subjective evaluation of experts.

Radio Frequency Interference Mitigation in Synthetic Aperture Radar Data Based on Instantaneous Spectrum Forward Consecutive Mean Excision

Radio frequency interference (RFI) poses major threats to synthetic aperture radar (SAR) systems. Due to the suppression of useful target signals via high-power RFI, the SAR imaging quality is severely degraded. Nevertheless, existing studies on RFI mitigation mainly focus on narrowband filtering, while wideband RFI mitigation methods are relatively lacking and perform non-robustly. In this paper, an RFI mitigation scheme is proposed based on instantaneous spectrum forward consecutive mean excision (FCME), which is suitable for both narrowband and wideband RFI mitigation. The SAR echo signal is first transformed into a time–frequency (TF) domain through a short-time Fourier transform (STFT). On this basis, the instantaneous spectra polluted via RFI are detected via a kurtosis-based statistical test and then filtered via FCME to achieve RFI mitigation. Finally, connected component analysis is applied as a safety measure so as to avoid the unnecessary loss of useful target signal. The combination of FCME and connected component analysis enables the proposed method to thoroughly filter out RFI while retaining more useful target signals compared with other competing methods. The experimental results on real SAR raw data validate the effectiveness of the proposed method.

DMSC-GAN: A c-GAN-Based Framework for Super-Resolution Reconstruction of SAR Images

Synthetic Aperture Radar (SAR) imagery is significant in remote sensing, but the limited spatial resolution results in restricted detail and clarity. Current super-resolution methods confront challenges such as complex network structure, insufficient sensing capability, and difficulty extracting features with local and global dependencies. To address these challenges, DMSC-GAN, a SAR image super-resolution technique based on the c-GAN framework, is introduced in this study. The design objective of DMSC-GAN is to enhance the flexibility and controllability of the model by utilizing conditional inputs to modulate the generated image features. The method uses an encoder–decoder structure to construct a generator and introduces a feature extraction module that combines convolutional operations with Deformable Multi-Head Self-Attention (DMSA). This module can efficiently capture the features of objects of various shapes and extract important background information needed to recover complex image textures. In addition, a multi-scale feature extraction pyramid layer helps to capture image details at different scales. DMSC-GAN combines perceptual loss and feature matching loss and, with the enhanced dual-scale discriminator, successfully extracts features from SAR images for high-quality super-resolution reconstruction. Extensive experiments confirm the excellent performance of DMSC-GAN, which significantly improves the spatial resolution and visual quality of SAR images. This framework demonstrates strong capabilities and potential in advancing super-resolution techniques for SAR images.

An Advanced Non-Interrupted Internal Calibration Model Based on Azimuth Modulation and Waveform Diversity for SAR Systems

Internal calibration, designed for obtaining the gain and phase errors introduced by the fluctuations of the electronic instruments, is an essential operation to guarantee the imaging performance of modern synthetic aperture radar (SAR) systems. However, in most of the current internal calibration schemes, the internal calibration processes interrupt the normal SAR data acquisition, which results in a loss of azimuth samples and degradation of the SAR image quality. To this end, an advanced non-interrupted internal calibration model is proposed, performing the internal calibration during nominal data acquisitions. The calibration signals and echoes are orthogonal and are received simultaneously. Then, they can be separated by postprocessing via the waveform diversity concept. Two schemes based on the proposed model are demonstrated via theoretical analysis and simulation experiments. The proposed model can achieve a considerable signal-to-noise ratio (SNR) and reduce the influence of calibration signals on echoes, which have some potential applications for future SAR missions.

Analysis and Compensation for Systematical Errors in Airborne Microwave Photonic SAR Imaging by 2-D Autofocus

In the area of synthetic aperture radar (SAR), the ultra-high resolution is always a continuous pursuit for researchers. To realize ultra-high resolution, microwave photonics (MWP) technology is an excellent solution since it has advantages of low transmission loss, ultra-wide bandwidth (UWB), etc. However, with the improvement of resolution, new problems arise as the impact of more systematical errors becomes non-negligible. For example, the synchronization error between the transmit channel and the mixing channel, unknown system delay during signal reception, and fluctuation of frequency for UWB signals. These factors will bring degradation of the focusing quality of SAR images together with the motion errors and the trajectory measurement errors, not only cause residual range cell migration (RCM) and azimuth phase error (APE), but also higher-order phase error along the range frequency dimension. In this paper, we discuss the adverse influence of the above factors and give a detailed analysis of the 2-D phase error of the coarse focused image processed by range migration algorithm (RMA). Then, to compensate for the above unknown systematical errors, a 2-D autofocus method is proposed and the effectiveness is validated by experiments on both simulated and real data.

A Stretched Deramping Radar Technique for High-Resolution SAR Processing in Ka-Band Using the Extended Integration Time

To increase the image quality of Synthetic Aperture Radar (SAR) is one of the hot-issue for high-performance detection/reconnaissance. With this interest in the SAR technique, it generates various techniques according to the purposed platforms (Auto-SAR, ViSAR, InSAR, PolSAR, etc.). In this paper, we introduce high efficiency and useful techniques, which can be applied to various SAR applications for generating high-quality images in Ka-band. We propose stretched de-ramping hardware system applicable in Ka-band, and design the Range-Doppler algorithm which well matches the proposed radar system with the modified signal parameters. Thus, we can extend the integration time of the convolution process in slow-time and it leads to the improvement of the SAR image quality. To verify the performance of our proposed technique, we process the practical experiments at conventional outdoor fields. Furthermore, the proposed method is operated on Range Doppler Algorithm (RDA), which is mostly used for standard SAR.

Synthetic Aperture Radar Target Recognition Based on Multimodule Image Enhancement Network

Synthetic aperture radar (SAR) has been widely used in recent years, and SAR automatic target recognition (ATR) has become a research hotspot. Most of the existing SAR ATR methods focus on the network structure design and increasing data volume but omit image quality and real-time processing. We design a multimodule image enhancement network (MMIE-Net) to solve these problems, which include the target extraction module, the image processing module, and convolutional neural networks (CNNs). First, we use the target extraction module and the image processing module to enhance the quality of raw SAR images. Then we design a suitable network for SAR image recognition, which is simple, lightweight, and recognizable. The experiment was mainly carried out on the MSTAR dataset, which can be divided into two categories, Standard Operating Condition (SOC) and Extended Operating Condition (EOC). The identification accuracy, the parameter storage space, and the depth of the model are considered as the criterion. The experimental results show that, compared with other methods, the proposed method not only ensures the simple structure of the network model but also has better recognition accuracy. Additionally, our method is robust and stable to large depression angle variation as well.

3D inverse synthetic aperture radar image quality improvement using sparse signal representation

AbstractGeneralisation of one‐dimensional dictionary learning (1DDL) algorithms to Multidimensional (MD) mode and its utilisation in MD data applications, increases the speed and reduces the computational complexity. An example of such an application is 3D inverse synthetic aperture radar (ISAR) image reconstruction and noise reduction. In this study, in addition to MD mode generalisation, the formulation structure of the multidimensional dictionary learning (MDDL) problem is discussed followed by two novel algorithms to solve it. The first one is based on the K‐singular value decomposition algorithm for 1DDL, which uses alternating minimisation and singular value decomposition. The second algorithm is the extension of the sequential generalisation of K‐means 1DDL algorithm to the MD mode. Moreover, the MD tensor denoising method based on the MDDL algorithm (MDDL‐ALG) is proposed. As an application, the proposed method is used to denoise the 3D ISAR image. The numerical simulations reveal that the proposed methods, in addition to reducing the memory consumption and the computational complexity, also enjoy higher convergence rate in comparison to 1D algorithms. Specifically, convergence speed of MD algorithms, depending on the training data size, is up to at least 10 times faster than the equivalent 1D counterparts. As revealed through the simulations, the amount of signal to noise ratio recovered by the proposed methods is almost 2 dB higher than the case using a pre‐designed dictionary for denoising. Moreover, it outperforms about 10 dB over the case with the conventional 3D‐IFFT method for image construction.

Geometric calibration of helicopter-borne wide-angle SAR imagery based on the range Doppler model

ABSTRACT Helicopter-borne wide-angle synthetic aperture radar (SAR) can be used in a wide range of military and civil applications because of their high resolution and mobility. The absolute geometric positioning accuracy is an important index for SAR image quality assessment. However, the effectiveness of using traditional geometric calibration methods with helicopter-borne SAR images obtained by time-domain imaging algorithms remains to be verified. Based on the range-Doppler model, a high-precision geometric calibration method for helicopter-borne SAR is proposed in this study and verified by experiments using real data with different resolutions. The results show that after calibration, the positioning accuracy of SAR images with 20 m and 1 m resolutions is improved from 51.33 m to 4.71 m and from 62.49 m to 3.03 m, respectively. These results verify the effectiveness of the proposed method.

Self-calibrated dilated convolutional neural networks for SAR image despeckling

ABSTRACT Synthetic aperture radar (SAR) imaging process is essentially disturbed by speckle noise due to its unique mechanism. Speckle noise causes severe degradation of SAR image quality, which significantly limits its practical application. Recently, convolutional neural networks (CNNs) have indicated good potential for various image processing tasks. In this article, we propose a self-calibrated dilated convolutional neural network for SAR image despeckling called SAR-SCDCNN. The main body of SAR-SCDCNN is formed by seven self-calibrated blocks (SeCaBlock). Firstly, in each SeCaBlock, the input features are split into two branches: one represents the contextual features in the original space, and another represents those in the long-range space. Then, the down-up sampling operation and the convolutions with hybrid dilated rates are employed to enlarge the receptive field. The weights for calculating features themselves are adaptively extracted in the second branch. And then, two branch features are concatenated and fused through a convolution. Finally, a skip connection is used between the input and output of each SeCaBlock to give full play to the expressive power of the deep neural network and enhance training stability. Experiments on synthetic speckled and real SAR images are conducted to perform objective quantitative and subjective visual evaluations of image quality. Results show that our proposed method can effectively suppress speckle noise and adequately preserve detailed features.

Radio Frequency Interference Mitigation for Synthetic Aperture Radar Based on the Time-Frequency Constraint Joint Low-Rank and Sparsity Properties

Synthetic aperture radar (SAR) is susceptible to radio frequency interference (RFI), which becomes especially commonplace in the increasingly complex electromagnetic environments. RFI severely detracts from SAR imaging quality, which hinders image interpretation. Therefore, some RFI mitigation algorithms have been introduced based on the partial features of RFI, but the RFI reconstruction models in these algorithms are rough and can be improved further. This paper proposes two algorithms for accurately modeling the structural properties of RFI and target echo signal (TES). Firstly, an RFI mitigation algorithm joining the low-rank characteristic and dual-sparsity property (LRDS) is proposed. In this algorithm, RFI is treated as a low-rank and sparse matrix, and the sparse matrix assumption is made for TES in the time–frequency (TF) domain. Compared with the traditional low-rank and sparse models, it can achieve better RFI mitigation performance with less signal loss and accelerated algorithm convergence. Secondly, the other RFI mitigation algorithm, named as TFC-LRS, is proposed to further reduce the signal loss. The TF constraint concept, in lieu of the special sparsity, is introduced in this algorithm to describe the structural distribution of RFI because of its aggregation characteristic in the TF spectrogram. Finally, the effectiveness, superiority, and robustness of the proposed algorithms are verified by RFI mitigation experiments on the simulated and measured SAR datasets.

효과적인 인공위성 SAR 기만 재밍을 위한 요구 조건 분석

In this study, we analyzed the deceptive jamming performance of a synthetic aperture radar (SAR). Four cases were considered: (1) time synchronization between the SAR and the jammer, (2) the number of fast Fourier transform (FFT) points that the jammer can use, (3) the minimum signal processing time of the jammer, and (4) the minimum feasible time interval in the jammer. To analyze the effectiveness of each case, the satellite-borne SAR and jammer simulation environment were used. From the results, it was confirmed that the four cases of jammer performance can affect the false SAR image quality, leading to the deterioration of its deceptive jamming performance. Therefore, we suggest that an analysis of the ability of the jammer is necessary for implementing more sophisticated SAR deceptive jamming signal processing.

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.

Efficient SAR Imaging Integrated With Autofocus via Compressive Sensing

In synthetic aperture radar (SAR) imaging, perturbations in the motion of the moving platform induce an undesired phase error due to imprecise knowledge of the motion, which results in the significant degradations in the quality of SAR images. In this paper, we present an efficient compressive sensing (CS)-based SAR imaging integrated with autofocus technique. The novel approach is based on an estimation-theoretic <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">l</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -norm-based approach coupled with Tikhonov-type regularization which combines an observation model of the SAR image formation process with the CS reconstruction problem of the SAR image regarding the sparsity. The optimization problem derived by considering spatially variant phase errors along azimuth domain and the dataset sampled at low rates can be effectively addressed by an efficient iterative method, wherein each iteration both SAR image formation and phase error correction are simultaneously carried out. The simulations and experimental results are presented to validate the effectiveness of the proposed method in terms of reliable image recovery and efficient computational complexity.

A Fast Algorithm for High Accuracy Airborne SAR Geolocation Based on Local Linear Approximation

Synthetic aperture radar (SAR) is a kind of high-resolution imaging radar, which can work in any weather conditions and is often used for target positioning. Airborne SAR has the advantages of flexible scanning geometry and high spatial resolution compared with Spaceborne SAR, but the quality of SAR image and positioning accuracy is affected by air turbulence and mechanical vibration. In practical applications, it is vital to improve Airborne SAR’s geometric positioning accuracy without ground control points or other reference image information. When positioning a single SAR image with long-range, the Earth surface model is often used as an additional equation to solve the Range-Doppler (RD) model. However, the Earth surface equation is difficult to solve the target height accurately. This study proposed an Airborne SAR target positioning methodology based on a local linear approximation of the Earth surface equation. The proposed local Linear RD (L-RD) model establishes a linear constraint of the local elevation, and the iterative algorithm converges rapidly. To further improve the efficiency of geolocation, we propose a method to directly obtain the initial geolocation used for the iteration based on the geometric relationship. An iterative framework is also proposed to enhance positioning accuracy when DEM is available. The experimental results show that the proposed method can significantly improve the positioning accuracy and reliability of Airborne SAR positioning without control points compared to other methods.

Trajectory instabilities and onboard navigation system characteristics influence on synthetic aperture radar image quality

Trajectory instabilities and onboard navigation system characteristics influence on synthetic aperture radar image quality

A Joint Azimuth Multichannel Cancellation (JAMC) Antibarrage Jamming Scheme for Spaceborne SAR

The azimuth multichannel synthetic aperture radar (SAR) increases the swath width by reducing the pulse repetition frequency (PRF), thus providing an effective scheme for realizing high-resolution and wide-swath (HRWS) imaging simultaneously. However, the quality of multichannel SAR images will suffer from electromagnetic jamming in the electronic antagonism environment. The existing channel cancellation methods not only require high positioning accuracy of the jammer and high PRF of the SAR system, but also remain periodic dark stripes, which reduce the readability of the SAR images. To solve these problems, this article proposes a scheme for multichannel SAR to locate the barrage jammer and suppress jamming signals. Firstly, by establishing the least <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${{\bm {L}}^{1}}$</tex-math></inline-formula> -norm optimization model on the echoes after channel cancellation, the location of the jammer in both azimuth and range directions can be accurately estimated. Then, based on the estimated jammer position, a joint cancellation filter is constructed to remove the jamming signal from the contaminated multichannel SAR data. And the reconstruction filter is designed according to the characteristics of the remaining signal, so that the SAR system reduces the requirement for high PRF. Finally, for the case of dark strips, this paper calculates the image amplitude equalization coefficient by deducing the azimuth modulation function, and a uniform HRWS SAR image without jamming is acquired. Results of simulated and measured data validate that the proposed method can effectively remove jamming signals and obtain the HRWS SAR images.