Single-channel Synthetic Aperture Radar Research Articles

RPCA-AENet: Clutter Suppression and Simultaneous Stationary Scene and Moving Targets Imaging in the Presence of Motion Errors.

Clutter suppression and ground moving target imaging in synthetic aperture radar (SAR) system have been receiving increasing attention for both civilian and military applications. The problem of clutter suppression and ground moving target imaging in practical applications is much more challenging due to the motion error of the radar platform. In this article, we focus on the problems of clutter suppression and simultaneous stationary and moving target imaging in the presence of motion errors. Specifically, we propose a robust principal component analysis autoencoder network (RPCA-AENet) in a single-channel SAR system. In RPCA-AENet, the encoder transforms the SAR echo into imaging results of stationary scene and ground moving targets, and the decoder regenerates the SAR echo using the obtained imaging results. The encoder is designed by the unfolded robust principal component analysis (RPCA), while the decoder is formulated into two dense layers and one additional layer. Joint reconstruction loss, entropy loss, and measurement distance loss are utilized to guide the training of the RPCA-AENet. Notably, the algorithm operates in a totally self-supervised form and requires no other labeled SAR data. The methodology was tested on numerical SAR data. These tests show that the proposed architecture outperforms other state-of-the-art methods.

A review of recent advance of ship detection in single-channel SAR images

Synthetic aperture radar (SAR) is an active microwave imaging sensor for high-resolution observation, with the ability of working in all-weather and all-day. Recently, SAR images have been widely used in many fields. Among them, ship detection in single-channel SAR images is a significant part of civilian and military fields. This article first discusses the characteristic of SAR images and the detectability of ships, then summarizes the recent advance of traditional and deep learning-based methods used for ship detection in single-channel SAR images. In addition, the characteristics and existing problems of various methods are discussed and their future development trends are predicted. Aiming at the problems of the large amount of calculation, multi-scale and densely docked ship detection in single-channel SAR images, an improved deep learning-based detection algorithm is proposed, which has achieved excellent performance on the SAR ship detection dataset (SSDD).

Ship Detection in PolSAR Images Based on a Modified Polarimetric Notch Filter

Ship detection based on synthetic aperture radar (SAR) imagery is one of the key applications for maritime security. Compared with single-channel SAR images, polarimetric SAR (PolSAR) data contains the fully-polarized information, which better facilitates better discriminating between targets, sea clutter, and interference. Therefore, many ship detection methods based on the polarimetric scattering mechanism have been studied. To deal with the false alarms caused by the existence of ghost targets, resulting from azimuth ambiguities and interference from side lobes, a modified polarimetric notch filter (PNF) is proposed for PolSAR ship detection. In the proposed method, the third eigenvalue obtained by the eigenvalue–eigenvector decomposition of the polarimetric covariance matrix is utilized to construct a new feature vector. Then, the target power can be computed to construct the modified PNF detector. On the one hand, the detection rate of ship targets can be enhanced by target-to-clutter contrast. On the other hand, false alarms resulting from azimuth ambiguities and side lobes can be reduced to an extent. Experimental results based on three C-band AIRSAR PolSAR datasets demonstrated the capability of the proposed PNF detector to improve detection performance while reducing false alarms. To be specific, the figure of merit (FoM) of the proposed method is the highest among comparative approaches with results of 80%, 100%, and 100% for the tested datasets, respectively.

Mixture Based Superpixel Segmentation and Classification of SAR Images

Abstract: The proposed approach suggests a mixture-based super pixel segmentation technique for synthetic aperture radar (SAR) images. SAR is a radar system used to generate 2D or 3D representations of objects like landscapes. This method utilizes SAR image amplitudes and pixel coordinates as features. Super pixels are large irregular-shaped regions obtained through oversegmentation of an image. While super pixel segmentation methods are commonly designed for colour images, this approach adapts a finite mixture model (FMM) for single-channel SAR images. By employing finite mixture models, the pixels are clustered into super pixels. Following the super pixel segmentation, the method employs a hierarchical decision tree clustering algorithm for classifying different land covers, such as climate monitoring and natural resources exploitation. The decision tree algorithm creates a dendrogram or tree structure to group feature vectors. The proposed super pixel method demonstrates improved classification accuracy compared to state-of-the-art methods like quick-shift, turbo pixels, simple linear iterative clustering, and pixel intensity and location similarity. The implementation of the proposed method is efficiently carried out using MATLAB software.

Phase Bias Estimation and Imaging for High-Squint Multichannel SAR in Azimuth

High-squint multichannel synthetic aperture radar (SAR) in azimuth can overcome the constraint of minimum antenna area in the traditional single-channel classical SAR system, and thereby it would be much more flexible to achieve high-resolution and wide-swath (HRWS) imaging than broadside SAR. However, for the high-squint multichannel SAR imaging mode, the squint angle causes Doppler centroid varying with the range frequency, which can further result in much more complicated problem of the spectral folding after down-sampled with low PRF. Meanwhile, the Doppler centroid frequency is indispensable for the estimation of phase bias and unambiguous signal reconstruction among received channels. In this paper, the impact of high-squint angle on the aliased Doppler spectrum and Doppler centroid is analyzed in detail. Then, a novel approach to phase bias estimation as well as the underlying signal processing in the case of high-squint multichannel SAR imaging mode in azimuth is proposed. Simulation results validate the effectiveness of the proposed processing flowchart for high-squint multichannel SAR.

Joint Radio Frequency Interference and Deceptive Jamming Suppression Method for Single-Channel SAR via Subpulse Coding

The radio frequency interference (RFI) and deceptive jamming (DJ), as two major external threats to synthetic aperture radar (SAR) systems, can greatly reduce the readability and veracity of the obtained SAR images. Current interference suppression methods have no capability to suppress both of them. In this article, a subpulse coding (SPC)-based joint RFI and DJ suppression method for single-channel SAR systems is proposed. By making full use of the elaborate coding scheme and subpulse transmitting mode, SPC can effectively suppress RFIs in the Doppler domain. On the other hand, after the decoding process, by utilizing the subpulse digital beamforming (DBF) technology with the well-designed DBF weight vectors, DJs can also be suppressed greatly. Numerical experiments verify the effectiveness of the proposed method.

A Ship Detection and Imagery Scheme for Airborne Single-Channel SAR in Coastal Regions

Ship detection and management in coastal regions are challenging tasks due to the complex appearances of ships and the background. For further applications in the context of fisheries monitoring and vessel traffic services, a single-channel synthetic aperture radar (SAR) is mounted on a number of maneuvering and inexpensive rotor platforms, which are utilized according to the consideration of flexible observation, cost savings, weight, and space constraints. In this paper, a hierarchical scheme of ship detection, ship imaging, and classification is proposed. It mainly includes three parts. First, a mixture statistical model of semi-parametric K-lognormal distribution based on adaptive background windows with a constant false alarm rate (CFAR) is proposed for ship prescreening in SAR imagery. Then, the discrimination stage, combined with ship imaging via the difference between the true ship targets and the false ones in the aspects of micro-Doppler motion properties, is performed. Finally, the simulation and field data processing results are presented to validate the proposed scheme.

Three-Dimensional Coordinate Extraction Based on Radargrammetry for Single-Channel Curvilinear SAR System

With the rapid development of high-resolution synthetic aperture radar (SAR) systems, the technique that utilizes multiple two-dimensional (2-D) SAR images with different view angles to extract three-dimensional (3-D) coordinates of targets has gained wide concern in recent years. Unlike the traditional multi-channel SAR utilized for 3-D coordinate extraction, the single-channel curvilinear SAR (CLSAR) has the advantages of large variation of view angle, requiring fewer acquisition data, and lower device cost. However, due to the complex aerodynamic configuration and flight characteristics, important issues should be considered, including the mathematical model establishment, imaging geometry analysis, and high-precision extraction model design. In this paper, to address these challenges, a 3-D vector model of CLSAR was presented and the imaging geometries under different view angles were analyzed. Then, a novel 3-D coordinate extraction approach based on radargrammetry was proposed, in which the unique property of the SAR system, called cylindrical symmetry, was utilized to establish a novel extraction model. Compared with the conventional approach, the proposed one has fewer constraints on the trajectory of radar platform, requires fewer model parameters, and can obtain higher extraction accuracy without the assistance of extra ground control points (GCPs). Numerical results using simulated data demonstrated the effectiveness of the proposed approach.

Addressing critical influences on L-band radar backscatter for improved estimates of basal area and change

L-band synthetic aperture radar (SAR) backscatter intensity is sensitive to land cover and can be used to estimate vegetation measures such as basal area (BA) and biomass. However, the estimation of BA, and especially change in BA, can be hampered by the influences upon backscatter of external factors such as imaging geometry, terrain topology, prevailing moisture conditions and even SAR sensor characteristics. This paper describes a method of reducing the adverse effects of such extraneous influences on vegetation and change estimates derived from single-channel SAR data. Empirical corrections for terrain slope and cross-track tendencies were applied and linear least squares difference minimization used to normalize the backscatter differences between scenes. The method was applied to state-wide coverage of L-band, fine-mode, HV polarization Advanced Land Observing Satellite (ALOS) Phased Array L-band SAR (PALSAR) data over New South Wales (NSW), Australia. The data were acquired with different sensors over two “observational epochs”: ALOS PALSAR in 2009 and ALOS-2 PALSAR-2 in 2016/17. The SAR datasets presented significant variations in backscatter intensity beyond those attributable to changes in vegetation cover. The corrective procedures resulted in improved uniformity of observed backscatter dependence on vegetation. Variations in backscattering coefficient between swaths were reduced by as much as 1.75 dB and 25% of the standard deviation in mean backscattering coefficients in common areas and at near- and far-range. This corresponded to a correction in BA estimate of 4.4 m2 ha−1. The method was observed to reduce ambiguities in regrowth estimates at swath boundaries and correct estimates of BA change by as much as 30% over large areas. The resulting estimates of 7-year change in BA provide spatially explicit forest structural information that is assisting in monitoring changes in woody vegetation across NSW.

Spatial Singularity-Exponent-Domain Multiresolution Imaging-Based SAR Ship Target Detection Method

A novel spatial singularity-exponent-domain multiresolution imaging (SSMRI) method is proposed in this article. It is aimed at improving the image processing ability for the single-channel synthetic aperture radar (SAR), thereby acquiring the robust image feature for SAR target detection at the very low SNR. Combining the 2-D singularity power spectrum (SPS) and the 2-D pseudo-Wigner–Ville distribution (PWVD), the SSMRI method is derived. Consequently, the single-channel SAR images are transformed to obtain spatial multiresolution SAR images with respect to the singularity exponent. Furthermore, the separability of the SAR image target in the space of the singularity exponent is analyzed, and the separability theorem in the sense of SSMRI is proved. In particular, under the background of GWN and fractal noise, the separability of SAR image targets based on SSMRI processing is studied. In addition, the stability and robustness of SSMRI-SAR feature extraction are demonstrated. On this basis, a maritime SAR ship target detection method based on SSMRI is put forward in the extremely low SNR condition. The experiment results on the SAR-Ship-Dataset indicate that the proposed method is superior in performance to the traditional CFAR or 2-D-SPS method. Especially when SNR = −30 dB, the detection performance with more than 99.4% can be achieved.

Joint Tracking of Moving Target in Single-Channel Video SAR

Video synthetic aperture radar (SAR) has been found very useful in ground moving target indication (GMTI) and tracking. The dynamic shadows in video SAR imagery sequences indicate the real positions of moving targets, which can be utilized in target detection and tracking. Unfortunately, the shadow-based method often fails when the shadows are not sufficiently developed. On the other hand, the traditional energy-based GMTI methods exhibit performance degradation when SAR images of a moving target are distorted or smeared. Neither of these two methods can stand alone to provide robust detection and tracking of moving targets. This article presents a joint processing framework for video SAR GMTI and tracking by combining the target shadow and echo energy information, which effectively lowers the false alarm and miss-detection rate. This approach is very suitable for real-time surveillance of ground moving targets in a single-channel video SAR system. The proposed approach has been verified by using the simulated and the real video SAR data.

Single-Channel Circular SAR Ground Moving Target Detection Based on LRSD and Adaptive Threshold Detector

Due to the advantages of long-time and multi-angle observation, ground moving target detection with circular synthetic aperture radar (SAR) has recently attracted lots of interest from researchers. Our team has previously proposed the logarithm background subtraction algorithm for moving target detection in single-channel circular SAR. Its principle is that the background image (static clutter) is obtained by median filtering of the image sequence, and the foreground image (moving target) is obtained by subtraction. To further improve the performance of background and foreground separation, we introduce the low-rank sparse decomposition (LRSD) method into the previous framework. A new algorithm based on LRSD and adaptive threshold detector (ATD) is proposed in this letter. First, this letter introduces entropy metric to optimize parameters in LRSD to obtain better background and foreground separation. Second, since the statistical distribution of the foreground image is unknown, the constant false alarm rate (CFAR) detector cannot be applied to the foreground image. Therefore, an adaptive threshold detector (ATD) built on Otsu is presented in this letter, which is independent of image statistical properties. The final detection result is obtained via clustering using the modified density-based spatial clustering of applications with noise (DBSCAN) method. The effectiveness of the proposed algorithm is verified by the experimental results on the airborne X-band Gotcha Volumetric SAR Data.

Single Channel SAR Ground Moving Target Detection Algorithm Based on Subband Interferometry

Traditional synthetic aperture radar (SAR) ground moving target detection (GMTD) technology mostly relies on multichannel data, which requires higher complexity of the radar system. This letter proposes a subband interferometry (SBI)-based algorithm for single-channel SAR GMTD. First, the echo signal is divided into high and low bands containing overlapping parts before the imaging process, then the subbands are imaged separately, and the SBI algorithm is used to achieve the interferometric phase of the subbands. It can be observed that the moving target has the characteristics of phase reversal in the interferometric phase image, and graphics operations such as median filtering or morphological processing can be used to detect the moving target. In this letter, the relationship between the subbands coincidence, the correlation coefficient (CC) of SBI, and the phase of moving target after SBI processing are derived. The real airborne SAR data verify the feasibility and effectiveness of the algorithm.

Target Recognition in Single-Channel SAR Images Based on the Complex-Valued Convolutional Neural Network With Data Augmentation

In recent years, with the rapid development of deep learning theory, real-valued convolutional neural networks have achieved significant success in the field of synthetic aperture radar (SAR) target recognition. However, different from natural images, the SAR images have complex information due to their special imaging mechanism. Traditional deep learning methods for SAR target recognition only employ the amplitude information and ignore the phase portion, which may sacrifice some useful information in the original complex SAR data. Moreover, the number of samples of SAR images is very limited. This is undoubtedly a huge challenge for the traditional real-valued convolutional neural networks (CNNs) which require numerous labeled data for training. Especially, since the single-channel SAR image contains only one channel, it has less available information than the multiple-channel SAR image. To deal with the above problems, a complex-valued convolutional neural network (CV-Net), for target recognition in single-channel SAR images is proposed in this paper. The amplitude and phase information in the complex SAR data are fully utilized for target recognition. In addition, to alleviate the problem of small samples, this paper also proposed a data augmentation method based on the complex SAR images. The experimental results based on the measured SAR data demonstrate that the proposed algorithm has better performance than the traditional real-valued convolutional neural networks.

Target Reconstruction Against Deceptive Jamming for Single-Channel SAR: An Imagery Domain Approach

The high fidelity and fraudulence of deceptive jamming can severely mislead synthetic aperture radar (SAR), which poses a big challenge in SAR imaging. In this letter, an imagery domain target reconstruction approach is proposed to suppress deceptive jamming for single-channel SAR. Specifically, the distinction between the echo signals of true targets and deceptive jamming in the azimuth phase is ascertained first. Then, the imaging process encountering deceptive jamming is formulated as a linear model, and the target reconstruction problem is converted to a linear inverse problem with the dictionary containing the explored phase characteristics. Finally, considering the sparsity of man-made targets, the imageries of the true and false targets can be reconstructed simultaneously through solving this sparse signal recovery problem. Theoretical analysis and experimental results showcase the superiority of the proposed method.

Polarimetric SAR Despeckling With Convolutional Neural Networks

Coherent imaging systems such as synthetic aperture radar (SAR) are subject to speckle, the reduction of which is an active area of study. Methods based on deep convolutional neural networks (CNNs) have recently demonstrated state-of-the-art performance in the removal of additive noise from natural images and speckle from single-channel SAR images. The application of deep learning to multichannel SAR modalities such as polarimetric SAR (PolSAR) is complicated in part by the nature of the data as images of complex-valued covariance matrices. In this article, we propose a CNN-based PolSAR despeckling approach that uses an invertible transformation involving a matrix logarithm to facilitate CNN processing of the PolSAR data. A residual learning strategy is adopted, in which the CNN is trained to identify the speckle component which is then removed from the corrupted image. The experimental results on simulated and measured PolSAR data show the proposed approach to markedly reduce speckle and preserve scene features.

HRWS SAR Narrowband Interference Mitigation Using Low-Rank Recovery and Image-Domain Sparse Regularization

Synthetic aperture radar (SAR), as a wideband radar system, may be subject to strong interferences with a variety of signals. The narrowband interference (NBI) exemplifies the most typical of its kind, such as the form of radio frequency interference (RFI). With the development of SAR imaging technology, the high-resolution wide-swath (HRWS) imagery technology now reaches its maturity to finally take shape in current SAR systems. To obtain HRWS images, the multichannel SAR (MC-SAR) system has been employed to tackle the contradictory requirements for both high resolution and low pulse repetition frequency (PRF). Previous interference methods focused on single-channel SAR systems and few research works for MC-SAR systems. In this article, we first derive a new interference-mitigation model for HRWS SAR systems and conclude that the low-rank property of the NBI is suitable for MC-SAR systems. Then we employ an image-domain sparse regularization to protect the real echoes of the SAR system and mitigate the NBIs by solving the low-rank recovery problems of NBIs. Also, the MC-SAR system errors are further taken into account as a measure for our method’s practical applicability. Finally, the real SAR data is used to demonstrate the effectiveness of the proposed method.

Water Extraction from Fully Polarized SAR Based on Combined Polarization and Texture Features

Given the limited features (for example, the backscattering coefficient threshold range) of single-channel Synthetic Aperture Radar (SAR) images, it is difficult to distinguish ground objects similar to the backscattering coefficients of water bodies. In this paper, two representative research areas are selected (Yancheng Coastal wetland and Shijiu Lake), and the fully polarized SAR data based on Gaofen-3 are used to extract water bodies using the method of polarization decomposition and gray level co-occurrence matrix. Firstly, the multi-dimensional features of ground objects are extracted, and then the redundancy processing of multi-dimensional features is carried out by the separability index, which effectively solves the misclassification of non-water bodies and water bodies and improves the accuracy of water body extraction. The comparison between the results of full-polarization extraction and single-polarization extraction shows that both full-polarization and single-polarization extraction can extract water information, but the extraction accuracy of the full-polarization method can reach 94.74% in the area with complex wetland features, which can effectively compensate for the lack of precision of the single-polarization method. Although multi-dimensional features can be extracted from fully polarimetric SAR data, data redundancy may exist. Therefore, using the Separability index (SI) to process multi-dimensional features can effectively solve the problem of feature redundancy and improve classification accuracy.

A super resolution target separation and reconstruction approach for single channel sar against deceptive jamming

The excellent remote sensing ability of synthetic aperture radar (SAR) will be misled seriously when it encounters deceptive jamming which possesses high fidelity and fraudulence. In this paper, the dynamic synthetic aperture (DSA) scheme is used to extract the difference between the true and false targets. A simultaneous deceptive jamming suppression and target reconstruction method is proposed for a single channel SAR system to guarantee remote sensing ability. The system model is formulated as a sparse signal recovery problem with an unknown parametric dictionary to be estimated. An iterative re-weighted method is employed to jointly handle the dictionary parameter learning and target reconstruction problem in an majorization-minimization framework, where a surrogate function majorizing the Gaussian entropy in the objective function is introduced to circumvent its non-convexity. After dictionary parameter learning, the grid mismatching problem in a fixed grid based method is avoided. Therefore, the proposed method can reap a super resolution result. Besides, a simple yet effective DSA section scheme is developed for the SAR data excerpting, in which only two DSAs are required. Experimental results about location error and reconstruction power error reveal that the proposed method is able to achieve a good performance in deceptive jamming suppression.

Unsupervised Ship Detection for Single-Channel SAR Images Based on Multiscale Saliency and Complex Signal Kurtosis

Traditional ship detection methods for synthetic aperture radar (SAR) mainly utilize the amplitude information to distinguish ship targets from sea clutter, including constant false alarm rate (CFAR), visual attention model, and deep learning methods. The CFAR algorithms adopt the dense sliding window strategy, which is very time-consuming and may generate numerous false alarms. The deep learning methods are supervised and difficult to obtain satisfactory performance when the number of labeled samples is insufficient. The visual attention models can quickly focus on the potential target area, and however, it is still difficult to eliminate the strong clutter, such as radio frequency interference and azimuth ambiguity. In fact, as a coherent imaging system, SAR data itself are complex-valued. Compared with the amplitude information, complex information can essentially reflect the difference between ship target and sea clutter. To improve the accuracy and efficiency of ship detection, in this letter, a novel unsupervised ship detection method based on multiscale saliency and complex signal kurtosis (MSS-CSK) for single-channel SAR images is proposed, which contains the proposal extraction stage and the target discrimination stage. The experimental results based on the Radarsat-2 real SAR data show that the proposed method has high detection accuracy and efficiency.