Synthetic Aperture Radar Imaging Method Research Articles

Superresolution Composite SAR Imaging Method by Hierarchical Pattern With Attributed Scattering Priors

Compared with the ideal point scattering model, the attributed scattering center (ASC) model provides concise and physically relevant description of the man-made targets, such as the vehicles and ships, giving an efficient way to interpret the measurements of high-frequency and wide-angle synthetic aperture radar (SAR). Since the ASC model is complicated, it gives rise to a high-dimensional model parameter estimation problem, leading to a heavy computational burden. Aimed at this problem, in this article, a novel superresolution composite SAR imaging algorithm is proposed based on sparse representation, in which the ASC attributes are estimated by a hierarchical pattern for reducing the computational cost. In the proposed algorithm, the ASC model parameters, classified into three categories, are estimated coarsely via minimum variance pursuit (MVP). Then these coarse estimates are refined jointly using the consensus alternating direction method of multipliers (CADMM) optimization for polarimetric measurements. The spectrum extrapolation and replacement are performed for superresolution and reducing the impact of model mismatch, followed by a fast Fourier transform (FFT)-based operation to efficiently obtain the final superresolution image. The proposed approach not only possesses the ability of superresolution reconstruction with the target nonideal scattering features’ enhancement but also offers the physically relevant attributes on the premise of estimation accuracy improvement and calculation capacity reduction. Extensive experiments are conducted to corroborate the effectiveness of the proposed algorithm.

A Novel Method for Staggered SAR Imaging in an Elevation Multichannel System

Synthetic aperture radar (SAR) is an advanced remote sensing technique, capable of observing Earth’s surface independent of weather conditions and sunlight illumination. Restricted by the minimum antenna area, however, conventional spaceborne SAR systems cannot achieve high azimuth resolution in a wide swath. In addition, blind ranges are present as the constant pulse repetition interval (PRI) is used. To solve these problems, a PRI-staggered elevation multichannel SAR (EMC-SAR) system is employed in this article. By transmitting the continuously PRI-varied sequence, the blind ranges are located at different regions in different receive instants, effectively avoiding the loss of coverage in elevation. In this system, three issues are required to be addressed: 1) recovering the missed data located at blind ranges; 2) suppressing range ambiguous components; and 3) restoring the PRI-varied signal into a regular grid. To deal with these problems, we propose a novel SAR imaging method for a PRI-staggered EMC-SAR system. To be applied on-ground, assume downlinking of the individual elevation channels. First, the modified <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\varepsilon $ </tex-math></inline-formula> -insensitive loss tube regression with the L2 regularization method is applied to recover the missed data. Then, the range ambiguous components are suppressed by performing digital beamforming (DBF) based on the elevation multichannel technique, where the covariance matrix is constructed by using an iterative adaptive algorithm. After that, a generalized scaling transform is employed to restore the PRI-varied signal into a uniform sampled grid. Finally, a well-focused SAR image can be obtained by performing the conventional SAR imaging techniques. The effectiveness of the proposed method is validated by both simulated and real SAR data processing results.

Flood Detection in Dual-Polarization SAR Images Based on Multi-Scale Deeplab Model

The proliferation of massive polarimetric Synthetic Aperture Radar (SAR) data helps promote the development of SAR image interpretation. Due to the advantages of powerful feature extraction capability and strong adaptability for different tasks, deep learning has been adopted in the work of SAR image interpretation and has achieved good results. However, most deep learning methods only employ single-polarization SAR images and ignore the water features embedded in multi-polarization SAR images. To fully exploit the dual-polarization SAR data and multi-scale features of SAR images, an effective flood detection method for SAR images is proposed in this paper. In the proposed flood detection method, a powerful Multi-Scale Deeplab (MS-Deeplab) model is constructed based on the dual-channel MobileNetV2 backbone and the classic DeeplabV3+ architecture to improve the ability of water feature extraction in SAR images. Firstly, the dual-channel feature extraction backbone based on the lightweight MobileNetV2 separately trains the dual-polarization SAR images, and the obtained training parameters are merged with the linear weighting to fuse dual-polarization water features. Given the multi-scale space information in SAR images, then, a multi-scale feature fusion module is introduced to effectively utilize multi-layer features and contextual information, which enhances the representation of water features. Finally, a joint loss function is constructed based on cross-entropy and a dice coefficient to deal with the imbalanced categorical distribution in the training dataset. The experimental results on the time series of Sentinel-1A SAR images show that the proposed method for flood detection has a strong ability to locate water boundaries and tiny water bodies in complex scenes. In terms of quantitative assessment, MS-Deeplab can achieve a better performance compared with other mainstream semantic segmentation models, including PSPNet, Unet and the original DeeplabV3+ model, with a 3.27% intersection over union (IoU) and 1.69% pixel accuracy (PA) improvement than the original DeeplabV3+ model.

A Fast Registration Method for Optical and SAR Images Based on SRAWG Feature Description

Due to differences in synthetic aperture radar (SAR) and optical imaging modes, there is a considerable degree of nonlinear intensity difference (NID) and geometric difference between the two images. The SAR image is also accompanied by strong multiplicative speckle noise. These phenomena lead to what is known as a challenging task to register optical and SAR images. With the development of remote sensing technology, both optical and SAR images equipped with sensor positioning parameters can be roughly registered according to geographic coordinates in advance. However, due to the inaccuracy of sensor parameters, the relative positioning accuracy is still as high as tens or even hundreds of pixels. This paper proposes a fast co-registration method including 3D dense feature description based on a single-scale Sobel and the ratio of exponentially weighted averages (ROEWA) combined with the angle-weighted gradient (SRAWG), overlapping template merging, and non-maxima suppressed template search. In order to more accurately describe the structural features of the image, the single-scale Sobel and ROEWA operators are used to calculate the gradients of optical and SAR images, respectively. On this basis, the 3 × 3 neighborhood angle-weighted gradients of each pixel are fused to form a pixel-wise 3D dense feature description. Aiming at the repeated feature description in the overlapping template and the multi-peak problem on the search surface, this paper adopts the template search strategy of overlapping template merging and non-maximum suppression. The registration results obtained on seven pairs of test images show that the proposed method has significant advantages over state-of-the-art methods in terms of comprehensive registration accuracy and efficiency.

Space-Time Varying Plasma Sheath Effect on Hypersonic Vehicle-Borne SAR Imaging

Hypersonic vehicle-borne synthetic aperture radar (SAR) imaging has enormous potential in civilian and military application due to high speed and strong survivability of the hypersonic vehicle. However, owing to high temperature and high pressure, a plasma flow, called plasma sheath, covers the hypersonic vehicle. The electron density of the plasma sheath varies with space and time, and space-time varying plasma sheath will affect hypersonic vehicle-borne SAR imaging severely. In this article, the effect of space-time varying plasma sheath on hypersonic vehicle-borne SAR imaging is investigated. First, the SAR signal model coupling space-time varying plasma sheath effect is established using the transmission line method. The variation law of electron density is based on flow field theory and experiment. The double propagation for SAR signal in plasma sheath and the time sequence of electron density for transmitting and receiving period are investigated and included in the SAR signal model. Moreover, the modulation effect of SAR signal under space-time varying plasma sheath is studied, and the range compressed signal under plasma sheath is derived. Based on the SAR signal model, hypersonic vehicle-borne SAR imaging defocus phenomenon with the presence of space-time varying plasma sheath is systematically analyzed. The simulation shows that the point target imaging result under plasma sheath is composed of several azimuth defocus strips, and in each strip the range dimension defocuses. The results of point target imaging and a SAR image of an area under plasma sheath verifies theory and analysis in this article. Finally, the SAR imaging degradation under different SAR and plasma sheath parameters are studied and the condition that serious defocus occurs is analyzed. The work in this article will benefit the study of hypersonic vehicle-borne SAR imaging method under plasma sheath in the future.

A Few-Shot Learning Method for SAR Images Based on Weighted Distance and Feature Fusion

Convolutional Neural Network (CNN) has been widely applied in the field of synthetic aperture radar (SAR) image recognition. Nevertheless, CNN-based recognition methods usually encounter the problem of poor feature representation ability due to insufficient labeled SAR images. In addition, the large inner-class variety and high cross-class similarity of SAR images pose a challenge for classification. To alleviate the problems mentioned above, we propose a novel few-shot learning (FSL) method for SAR image recognition, which is composed of the multi-feature fusion network (MFFN) and the weighted distance classifier (WDC). The MFFN is utilized to extract input images’ features, and the WDC outputs the classification results based on these features. The MFFN is constructed by adding a multi-scale feature fusion module (MsFFM) and a hand-crafted feature insertion module (HcFIM) to a standard CNN. The feature extraction and representation capability can be enhanced by inserting the traditional hand-crafted features as auxiliary features. With the aid of information from different scales of features, targets of the same class can be more easily aggregated. The weight generation module in WDC is designed to generate category-specific weights for query images. The WDC distributes these weights along the corresponding Euclidean distance to tackle the high cross-class similarity problem. In addition, weight generation loss is proposed to improve recognition performance by guiding the weight generation module. Experimental results on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset and the Vehicle and Aircraft (VA) dataset demonstrate that our proposed method surpasses several typical FSL methods.

Sparse SAR Imaging Method for Ground Moving Target via GMTSI-Net

Ground moving targets (GMT), due to the existence of velocity in range and azimuth direction, will lead to the deviation from their true position and defocus in the azimuth direction during the synthetic aperture radar (SAR) imaging process. To address this problem and compress the amount of echo data, a sparse SAR imaging method for ground moving targets is proposed. Specifically, we first constructed a two-dimensional sparse observation model of the GMT based on matched filter operators. Then, the observation model was solved by a deep network, GMT sparse imaging network (GMTSI-Net), which was mainly obtained by unfolding an iterative soft threshold algorithm (ISTA)-based iterative solution. Furthermore, we designed an adaptive unfolding module in the imaging network to improve the adaptability of the network to the input of echo data with different sampling ratios. The proposed imaging network can achieve faster and more accurate SAR images of ground moving targets under a low sampling ratio and signal-to-noise ratio (SNR). Simulated and measured data experiments were conducted to demonstrate the performance of imaging quality of the proposed method.

Autofocus algorithms with phase error correction for synthetic aperture radar imagery

Due to the unknown platform motion and/or signal propagation delays, phase errors are introduced to synthetic aperture radar (SAR) imagery. With the fact that the pixel values in the low-return region of the focused image are close to zero, we devise two autofocus SAR imaging methods with phase error correction in this paper. One is a fractional model to maximize the pixel values in the remaining region, termed high-return region, while minimizing those in the low-return region. And another is a model with the high-return region pixel value maximization and the controlled peak low-return pixel value. The resultant optimization problems are difficult to solve due to the coupled numerator and denominator of the quadratic fractional programming problem formulation with the constant modulus constraints and the controlled optimization problem with the nonconvex inequality constraints. To tackle them, we derive two iterative solutions via introducing auxiliary variables to decouple the numerator and denominator and the inequality constraints. Alternating direction method of multipliers is utilized in the algorithm development so that their convergence is guaranteed. Numerical examples are provided to show the effectiveness of the developed methods.

Multi-Resolution Collaborative Fusion of SAR, Multispectral and Hyperspectral Images for Coastal Wetlands Mapping

The hyperspectral, multispectral, and synthetic aperture radar (SAR) remote sensing images provide complementary advantages in high spectral resolution, high spatial resolution, and geometric and polarimetric properties, generally. How to effectively integrate cross-modal information to obtain a high spatial resolution hyperspectral image with the characteristics of the SAR is promising. However, due to divergent imaging mechanisms of modalities, existing SAR and optical image fusion techniques generally remain limited due to the spectral or spatial distortions, especially for complex surface features such as coastal wetlands. This paper provides, for the first time, an efficient multi-resolution collaborative fusion method for multispectral, hyperspectral, and SAR images. We improve generic multi-resolution analysis with spectral-spatial weighted modulation and spectral compensation to achieve minimal spectral loss. The backscattering gradients of SAR are guided to fuse, which is calculated from saliency gradients with edge preserving. The experiments were performed on ZiYuan-1 02D (ZY-1 02D) and GaoFen-5B (AHSI) hyperspectral, Sentinel-2 and GaoFen-5B (VIMI) multispectral, and Sentinel-1 SAR images in the challenging coastal wetlands. Specifically, the fusion results were comprehensively tested and verified on the qualitative, quantitative, and classification metrics. The experimental results show the competitive performance of the proposed method.

A Novel SAR Imaging Method Based on Morphological Component Analysis

Clutter suppression plays an important role in a synthetic aperture radar (SAR) system. The conventional SAR imaging methods are useful for distinguishing the echo signal and noise, but cannot separate the target signal from background clutter. Inspired by the signal separation ability of morphological component analysis (MCA), a novel SAR imaging method based on MCA is proposed to suppress the strong background clutter. In the new model, the SAR echo is considered as a linear superposition of target signal, clutter signal, and noise. According to different characteristics of morphological components, clutter dictionary and target dictionary are constructed to sparsely represent the clutter component and target component, respectively. Then, the MCA method based on the sparse representation and morphological diversity of signals is employed to decompose the SAR echo into the target signal, clutter signal, and noise. Finally, the separated target signal is processed to obtain the ultimate SAR image. Experimental results from simulated and real SAR data are provided to demonstrate the effectiveness of the proposed method.

Robust image similarity metric method for SAR images

Synthetic aperture radar (SAR) image similarity metric is at the core of SAR image interpretation techniques, however, it is still a challenging task due to complex nonlinear intensity, scale, and rotation differences between SAR images and other remote sensing images. This letter addresses this problem by proposing a novel similarity metric method for SAR images using structure and shape properties. The magnitude and orientation representation of the phase congruency model is first built based on the local phase of images. Then a new scale and rotation-invariant local binary pattern (SRI-LBP) descriptor is proposed using local structure and shape information. Finally, a similarity metric is defined using the symmetry Kullback Leibler divergence (SKLD) of the SRI-LBP descriptors. Numerical experiment results verify its robustness in terms of nonlinear intensity, scale, and rotation differences.

Dynamic Range Compression Self-Adaption Method for SAR Image Based on Deep Learning

The visualization of synthetic aperture radar (SAR) images involves the mapping of high dynamic range (HDR) amplitude values to gray levels for lower dynamic range (LDR) display devices. This dynamic range compression process determines the visibility of details in the displayed result. It therefore plays a critical role in remote sensing applications. There are some problems with existing methods, such as poor adaptability, detail loss, imbalance between contrast improvement and noise suppression. To effectively obtain the images suitable for human observation and subsequent interpretation, we introduce a novel self-adaptive SAR image dynamic range compression method based on deep learning. Its designed objective is to present the maximal amount of information content in the displayed image and eliminate the contradiction between contrast and noise. Considering that, we propose a decomposition-fusion framework. The input SAR image is rescaled to a certain size and then put into a bilateral feature enhancement module to remap high and low frequency features to realize noise suppression and contrast enhancement. Based on the bilateral features, a feature fusion module is employed for feature integration and optimization to achieve a more precise reconstruction result. Visual and quantitative experiments on synthesized and real-world SAR images show that the proposed method notably realizes visualization which exceeds several statistical methods. It has good adaptability and can improve SAR images’ contrast for interpretation.

Fast Superpixel-Based Non-Window CFAR Ship Detector for SAR Imagery

Ship detection in high-resolution synthetic aperture radar (SAR) images has attracted great attention. As a popular method, a constant false alarm rate (CFAR) detection algorithm is widely used. However, the detection performance of CFAR is easily affected by speckle noise. Moreover, the sliding window technique cannot effectively differentiate between clutter and target pixels and easily leads to a high computation load. In this paper, we propose a new superpixel-based non-window CFAR ship detection method for SAR images, which introduces superpixels to CFAR detection to resolve the aforementioned drawbacks. Firstly, our previously proposed fast density-based spatial clustering of applications with noise (DBSCAN) superpixel generation method is utilized to produce the superpixels for SAR images. With the assumption that SAR data obeys gamma distribution, the superpixel dissimilarity is defined. Then, superpixels can be accurately used to estimate the clutter parameters for the tested pixel, even in the multi-target situations, avoiding the drawbacks of the sliding window in the traditional CFAR. Moreover, a local superpixel contrast is proposed to optimize CFAR detection, which can eliminate numerous clutter false alarms, such as man-made urban areas and low bushes. Experimental results with real SAR images indicate that the proposed method can achieve ship detection with a higher speed and accuracy in comparison with other state-of-the-art methods.

A Collaborative Despeckling Method for SAR Images Based on Texture Classification

Speckle is an unavoidable noise-like phenomenon in Synthetic Aperture Radar (SAR) imaging. In order to remove speckle, many despeckling methods have been proposed during the past three decades, including spatial-based methods, transform domain-based methods, and non-local filtering methods. However, SAR images usually contain many different types of regions, including homogeneous and heterogeneous regions. Some filters could despeckle effectively in homogeneous regions but could not preserve structures in heterogeneous regions. Some filters preserve structures well but do not suppress speckle effectively. Following this theory, we design a combination of two state-of-the-art despeckling tools that can overcome their respective shortcomings. In order to select the best filter output for each area in the image, the clustering and Gray Level Co-Occurrence Matrices (GLCM) are used for image classification and weighting, respectively. Clustering and GLCM use the co-registered optical images of SAR images because their structure information is consistent, and the optical images are much cleaner than SAR images. The experimental results on synthetic and real-world SAR images show that our proposed method can provide a better objective performance index under a strong noise level. Subjective visual inspection demonstrates that the proposed method has great potential in preserving structural details and suppressing speckle noise.

Super‐resolution forward‐looking imaging method for manoeuvering platform with optimised dictionary and extended sparsity adaptive matching pursuit

In forward-looking imaging, left-right Doppler is ambiguous and cross-range resolution is limited by real aperture, which result in traditional synthetic aperture radar imaging methods no longer applicable. Besides, the impact of three-dimensional acceleration on an over-complete dictionary matrix and sparse reconstruction kernel is not considered in conventional super-resolution forward-looking imaging methods. To solve these problems, a super-resolution forward-looking imaging method for manoeuvering platforms is proposed. First, the range walk caused by forward-looking geometry and higher-order phase introduced by three-dimensional acceleration are eliminated by compensating a pre-processing function. Subsequently, according to beam steering-based geometry and linear regression model, the over-complete dictionary matrix is optimised to more accurately describe the complicated forward-looking geometry with curved trajectory. Finally, sparsity adaptive matching pursuit kernel with an improved halting condition is adopted to reconstruct the forward-looking scene. The proposed method is supported by point target simulation and surface target simulation.

Multi-Electromagnetic Jamming Countermeasure for Airborne SAR Based on Maximum SNR Blind Source Separation

Synthetic aperture radar (SAR) may be attacked by multifarious kinds of the active-jamming, which will lead to the ineffectiveness of SAR in complex electromagnetic environment. In this paper, a novel multi-electromagnetic jamming counter-measure for the airborne SAR is proposed based on maximum signal-to-noise ratio (SNR) blind source separation. Firstly, the imaging geometry and multi-component mixed signal model of airborne SAR are established. Then, based on multi-component mixed signal matrix, a blind source separation (BSS) method based on the maximum SNR is proposed to separate the real target echo signal from the multi-electromagnetic jamming signals. After real target echo signal identification, the high resolution images of the interested target area can be achieved by the corresponding SAR imaging method. The simulated and measured data results are present to prove the feasibility and effectiveness of the proposed method.

Joint Low-Rank and Sparse Tensors Recovery for Video Synthetic Aperture Radar Imaging

Video synthetic aperture radar (SAR) receives more and more attention in recent years because it can provide continuous images of the observed scene. However, the enormous data of video SAR to obtain the multiframe images bring big challenges to its transmission, storage, and processing, especially for small unmanned aerial vehicle (UAV) platform. In this article, we aim at proposing an efficient video formation method for video SAR systems with reduced data. First, the characteristics of video SAR observed scene are analyzed. It is found that the observed scene with multiple frames can be modeled as the sum of a low-rank tensor and a sparse tensor efficiently. After that, the video formation problem for video SAR is modeled as a joint low-rank and sparse tensors recovery problem. Finally, an efficient tensor alternating direction method of multiplier is proposed to obtain the final SAR video. Compared with the traditional frequency- or time-domain imaging methods, the amount of data samples can be greatly reduced. On the other hand, the proposed method outperforms the state-of-the-art SAR imaging methods with reduced samples, including the joint low-rank and sparse matrices recovery method and the low-rank tensor recovery method. Numerical simulations validate the effectiveness of the proposed method.

SAR Imaging Based on Deep Unfolded Network With Approximated Observation

Compressed sensing (CS) based synthetic aperture radar (SAR) imaging methods are showing superior potential in imaging performance over classical matched filtering based methods. However, the CS-based methods require much more computational cost to solve the iterative optimization composed of large-scale matrix operators. To hold the improvement of imaging performance and reduce the computational cost, in this paper, we propose a novel SAR imaging method by Deep Unfolded Network (DUN) of Iterative Shrinkage Threshold Algorithm (ISTA) with the approximated observation of Range-Doppler Algorithm (RDA) operator. The proposed method takes the radar echoes as the input to learn the imaging procedure. Firstly, the approximated observation is utilized in SAR imaging model to reduce the size of the DUN. Moreover, we use ISTA as an example to introduce how to establish DUN with approximated observation, in which the detailed structure to handle the complex-valued radar echoes is also designed. Finally, the auto-encoder is utilized to calculate the difference of the echoes rather than the imaging results so that we can train the proposed network by unsupervised learning. The experiments of both point targets, surface targets, and real scenes show that the proposed imaging method is superior in terms of imaging performance and computing efficiency.

A Siamese Template Matching Method for SAR and Optical Image

Template matching between synthetic aperture radar (SAR) and optical image is a meaningful and important topic for remote sensing tasks. In this letter, a deep learning framework based on Siamese structure and U-Net is proposed. First, a sparse 3-D transform-domain collaborative filtering (BM3D) for SAR image denoising is applied. Then, deep features of SAR and optical images are extracted from two branches of the proposed Siamese U-Net network. Finally, a cross correlation layer is used to generate the heatmap of image pairs based on the deep features extracted, and the peak maximal location of the heatmap is seen as the best matching location result. During training, a balanced loss between ground truth and prediction is used to get trained model weights in order to make a balance between positive samples and negative samples. The experiments on the same test dataset demonstrate that the proposed method improves the matching accuracy and precision to some degree in comparison with existing template matching methods for SAR and optical images.

Fast SAR Image Segmentation With Deep Task-Specific Superpixel Sampling and Soft Graph Convolution

Since the number of superpixels is lower than that of pixels, superpixels can substantially speed up subsequent processing steps and have been widely used in synthetic aperture radar (SAR) image segmentation. However, in most of the existing superpixel-wise segmentation algorithms, superpixel prediction is an isolated preprocessing step and is independent of the segmentation task. The performance of the segmentation results is determined by the accuracy of superpixels. Once superpixels are generated, their shape cannot be changed in the following segmentation stage, even if the same superpixels contain pixels of different landcovers. To address this, we propose an end-to-end trainable superpixel-wise segmentation method for single-polarization SAR images. First, we design a differentiable boundary-ware clustering method for estimating task-specific superpixels. Instead of the hard association between pixels and superpixels in the existing superpixel algorithms, this method introduces the soft association map to make the clustering differentiable. Hence, it can be implemented using a simple deep fully convolutional network. In the segmentation part, we propose a novel soft graph convolution network (Soft-GCN), which takes the association map as input and performs superpixel-wise segmentation. The advantage of our method is that superpixel generation and graph convolution parts can be trained under a unified framework, until two parts obtain the optimum parameters. In the training process, it can adaptively adjust the shape of the superpixels according to the segmentation results, ensuring the superpixels correctly adhere the boundaries. Experimental results with simulated and real SAR images demonstrate that our method outperforms other state-of-the-art segmentation algorithms, while also being faster.