High-quality Synthetic Aperture Radar Images Research Articles

Analysis Method for Determining Optimal Synthetic Aperture Time Using Estimated Range and Doppler Cone Angle at the Center of Synthetic Aperture Length

Synthetic aperture time (SAT) is a crucial component for acquiring high-quality synthetic aperture radar images with an excellent target cross-range resolution. SAT is analyzed using the range and Doppler cone angle at the center of the synthetic aperture length (SAL). However, in a real flight mission setting, only the range and Doppler cone angle at the SAL’s starting point are determined. Therefore, we present a method for estimating the range and Doppler cone angle at the center of the SAL to calculate an accurate SAT that is suitable for the spatial resolution of the assigned mission. We performed an iterative analysis of SAT at the range and Doppler cone angle at the starting point of the SAL (original SAT) and at the center of the SAL (proposed SAT). Consequently, the proposed SAT decreased by 0.69%–16.14% compared to the original SAT at a resolution of 0.1–3.0 m.

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.

A High-Quality Multicategory SAR Images Generation Method With Multiconstraint GAN for ATR

The high-quality training data sets are often insufficient in synthetic aperture radar (SAR) automatic target recognition (ATR) applications. The generative adversarial network (GAN) provides a way for SAR data augmentation. It is necessary to ensure the diversity, similarity, and correct category of the generated images so that these images can be served as the supplementary data set. In this letter, the multiconstraint GAN (MCGAN) is proposed to generate high-quality multicategory SAR images. First, an encoder is used to learn the features of the real images to enhance the similarity. Then, the encoded features are mixed with noise and category labels as the input of the generator to improve the diversity and category correctness. The generated images will be sent to a pretrained classifier to ensure the correct category. Finally, the improved Wasserstein loss with the gradient penalty is extended to the model to further improve the diversity and similarity of the generated images. The MSTAR data set is used to validate the proposed method on generation. The quality evaluation and classification tests are performed on the generated images, and the results show that the MCGAN can provide high-quality images, which could assist in achieving good classification accuracy.

Survey of Research Progress on Target Detection and Discrimination of Single-channel SAR Images for Complex Scenes

As an active microwave imaging sensor, Synthetic Aperture Radar (SAR) has become one of the main means of Earth observation owing to its unique technical advantages of all-day, all-weather operation and long working distance. As such, it plays a very important role in military and civilian fields. With the development of SAR remote-sensing technology, high-resolution, high-quality SAR images are produced continuously. However, manual detection and recognition of targets of interest is time-consuming and laborious, so the development of Automatic Target Recognition (ATR) technology is a matter of urgency. The typical SAR ATR system primarily comprises three stages: detection, discrimination, and classification/recognition. The detection and discrimination stages are the basis of the SAR ATR system, and research on SAR applications in the radar field has been conducted by researchers around the world. For single-channel SAR images, target detection and discrimination from simple scenes yield good results. However, in complex scenes, the clutter scattering intensity is relatively high, the clutter background is heterogenous, the target scattering intensity is relatively weak, and the target distribution is dense. These factors continue to make accurate SAR target detection and discrimination difficult. In this paper, we summarize the recent research progress on single-channel SAR target detection and discrimination methods for complex scenes, analyze the characteristics and problems associated with various methods, and consider the future development trend of single-channel SAR target detection and discrimination methods for complex scenes.

A Method for Road Network Extraction from High-Resolution SAR Imagery Using Direction Grouping and Curve Fitting

Roads are an important recognition target in synthetic aperture radar (SAR) image interpretation. Although a considerable number of high-quality SAR images are now available, the method of road extraction is lagging. To extract the road network with low missed and false rates, this paper proposed a road network extraction approach which includes line detection, road segmentation, road network extraction and optimization. First, the linear feature response and direction map are obtained from the SAR intensity image using the multiplicative Duda operation. Then, the backscattering coefficient and coefficient of variation are combined using a support vector machine to eliminate the linear structures of non-roads, and the binary image of road candidates is subsequently achieved by morphological profiles of path openings. Next, with the obtained direction map, a novel thinning method based on binary image decomposition and curve fitting is presented to obtain line segments of the road network. Finally, a series of measures which involve overlap, continuity, and junction optimization are proposed to construct the road network. In the experiments, the proposed method was applied to Radarsat-2 and TerraSAR-X high-resolution images. The experimental results showed that the proposed method had an excellent performance in terms of both completeness and correctness.

Backprojection Subimage Autofocus of Moving Ships for Synthetic Aperture Radar

We propose a new autofocus approach for the backprojection reconstruction algorithm to compute high-quality synthetic aperture radar images of non-linearly moving and maneuvering ships. In contrast to the state-of-the-art autofocus techniques, our approach allows a long coherent processing interval even in the case of a rough sea, which improves the image quality. An improved image quality enables the classification of ships in airborne synthetic aperture radar (SAR) images. For this purpose, we decompose the image into subimages and estimate pulse-by-pulse a phase error for each subimage by maximizing subimage sharpness. A regularized Levenberg–Marquardt algorithm guarantees a smooth phase correction on subimage level. By correcting the subsequent range distances from the flight path to all pixels using the currently estimated phase errors, sharp images of maneuvering ships with arbitrary velocities can now be reconstructed. The evaluation of our proposed ship autofocus technique on the basis of real airborne X-band data shows that our approach leads to a visible improvement of image quality in comparison with the state-of-the-art techniques. Given these results, even an automatic ship classification based on radar images might be possible in the future.

Efficient Synthesis of Antenna Pattern Using Improved PSO for Spaceborne SAR Performance and Imaging in Presence of Element Failure

For a synthetic aperture radar (SAR) system, the individual beam pattern pointing and shaping due to amplitudes and phase settings in transmit/receive modules (TRMs) are mainly used in the elevation direction over the total range of incidence angles. The criteria for the optimized antenna elevation beam pattern are profoundly linked to the overall SAR system performance requirements. In particular, a high sidelobe level (SLL) in the antenna pattern leads to a high range ambiguity-to-signal ratio (RASR), which degrades the quality of the SAR image. RASRs can be controlled by appropriate antenna SLL suppression at defined positions in the elevation pattern. This paper focuses on the improvement in the SAR system performance using an effective technique for optimizing antenna pattern synthesis. The desired antenna patterns can be synthesized referring to the optimized antenna mask templates using the newly devised cost function and improved particle swarm optimization (IPSO). Even though there are some defective TRMs in array phased antennas, one can regenerate an optimal pattern as close as possible to the desired one, owing to the proposed cost function and IPSO. Moreover, this paper provides a new perspective on RASR performance, which can be analyzed in SAR images, via the full-chain process from antenna pattern synthesis to SAR image formation. In addition, this is the first study to consider the problem of antenna pattern optimization with element failure, in terms of spaceborne SAR imaging, as well as SAR system performance. The simulations conducted in this study show that the optimized pattern obtained via the proposed technique can provide good SAR system performance, resulting in high-quality SAR images, even with defective TRMs.

Object‐level SAR imaging method with canonical scattering characterisation and inter‐subdictionary interferences mitigation

Under the assumption of ideal-point scattering model, conventional developed synthetic aperture radar (SAR) imaging techniques often ignore frequency/angle dependence, which may result in scattering estimation inaccurate and object-level information loss. To solve the problem, in this study, the authors propose an object-level SAR imaging method by virtual of sparse representations and canonical shape feature models. Specifically, the representation basis vectors are combined with parametric scattering models for characterising angle/frequency dependence, so that they can accurately capture physically relevant scattering geometry information. To acquire high-quality object-level SAR image and mitigate the inter-subdictionary interferences, the authors propose a regularisation algorithm to take advantage of the inherent prior information including the sparsity of coefficient vector and the interaction nature between different spatial locations. The advantages of the proposed method can be summarised as follows: (i) by using the location-wise coordinate descent strategy, it can save memory cost and also reduce computation complexity in comparison with the direct solving the full inverse problem; and (ii) it can effectively acquire more parsimonious representation in the number of spatial locations and canonical scattering type information of strong scattering points such as top-hats and trihedral. Finally, experimental results are provided to demonstrate the validity of the proposed method.

Wideband Interference Mitigation in High-Resolution Airborne Synthetic Aperture Radar Data

Radio frequency interference is a major issue for synthetic aperture radar (SAR) imaging. Especially with the presence of wideband interference (WBI), the signal-to-interference ratio (SIR) of the measurements is greatly degraded, thus making it difficult to produce a high-quality SAR image. Compared with narrow-band interference (NBI), WBI occupies a larger bandwidth and is more complicated to deal with. This paper addresses the detection and mitigation of WBI in high-resolution airborne SAR data. First, a WBI-corrupted echo is characterized in the time–frequency representation by utilizing the short-time Fourier transform. In this way, the original range-spectrum WBI mitigation problem can be simplified into a series of instantaneous-spectrum NBI mitigation problems. For each instantaneous spectrum, the existence of interference signal can be identified according to the negentropy-based statistical test. Furthermore, the interference signal is mitigated by notch filtering or eigensubspace filtering. The experimental results of the simulated data, as well as real measured data sets, show that the proposed scheme is effective in suppressing the interference signal and in obtaining a high-quality image.

Simultaneous High-Resolution Wide-Swath SAR Imaging and Ground Moving Target Indication: Processing Approaches and System Concepts

In this paper, a novel method for enabling ground moving target capability for high-resolution wide-swath (HRWS) synthetic aperture radar (SAR) systems is presented. Such HRWS SAR systems are generally operated with very low pulse repetition frequencies (PRFs). With the novel method, moving target signals highly aliased in Doppler can be reconstructed and the targets’ motion parameters and actual positions can be estimated with high accuracy. A special matched reconstruction filter bank (MRFB) is introduced and incorporated into two novel low PRF ground moving target indication (GMTI) algorithms. Owing to the application of the MRFB, GMTI and HRWS SAR imaging can be performed simultaneously without the need of a separate high PRF system operation mode, which, so far, was necessary for achieving a reasonable GMTI performance. With the MRFB, the advantages of the HRWS imaging principle, i.e., the wide swath and the high resolution are preserved for both HRWS SAR imaging and GMTI. Potential applications are wide-area land and maritime traffic monitoring, where the benefit of the preserved high resolution could, for instance, be used for generating high-quality inverse SAR (ISAR) images for target recognition purposes.

Generation of Pixel-Level SAR Image Time Series Using a Locally Adaptive Matching Technique

Synthetic Aperture Radar (SAR) image time series play an important role in many applications. To construct pixel-level SAR image time series, we propose a locally adaptive image matching technique for the high-precision geometric registration of SAR images. The basic idea is to adapt the local characteristics of ground objects during the process of image registration. Then, by analyzing the spatial distribution of the error of each matched pair in the previous iteration, local areas are divided based on the local clustering of pairs with large errors. A new polynomial is then used to satisfy the local geometric constraint. Based on this proposed matching technique, we introduce a pixel-level SAR image time series modeling method. The experimental results show that the average geometric error of corresponding pixels in this algorithm is 0.073 pixels, while that of the NEST software is 0.242 pixels. The Pearson correlation coefficients of 20 pixels’ time series are above 0.85, indicating that the series bears high curve similarity and geometric precision, which suggests the proposed technique can provide high-quality SAR image time series.

PAR Model SAR Image Interpolation Algorithm on GPU with CUDA

ABSTRACTResolution of synthetic aperture radar (SAR) images is limited by imaging hardware. To improve SAR image resolution, interpolation needs to be carried out on a SAR image. An image interpolation algorithm with a good effect incurs a high computational cost and cannot be used in a real-time scenario. This paper applies Compute Unified Device Architecture (CUDA) to speed up the piece-wise autoregressive model image interpolation algorithm for acquiring high-quality SAR images. A partial differential is used to carry out image de-noising. After de-noising, divide the image interpolation based on a local window of a piece-wise autoregressive model: the SAR image is first divided into many 9 × 9 small local windows. For each window, a CUDA thread is launched to interpolate using the autoregressive interpolation algorithm. Gradient descent algorithm is used to estimate parameters of the autoregressive model in the first- and second-round interpolation. Numerical simulation indicates that this GPU-based parallel algorithm can interpolate a 2592 × 1944 image within 1/110th of the time used by a CPU-based serial algorithm. Moreover, the computation time saved increases with the image size. The experimental results show that the method in this paper can achieve high-quality image interpolation in a low computation time.

A NOVEL IMAGE FORMATION ALGORITHM FOR HIGH-RESOLUTION WIDE-SWATH SPACEBORNE SAR USING COMPRESSED SENSING ON AZIMUTH DISPLACEMENT PHASE CENTER ANTENNA

High-resolution wide-swath (HRWS) imaging with space- borne synthetic aperture radar (SAR) can be achieved by using az- imuth displacement phase center antenna (DPCA) technique. How- ever, it will consequently leads to extremely high data rate on satellite downlink system. A novel sparse sampling scheme based on compressed sensing (CS) theory for azimuth DPCA SAR was proposed, by which only a small proportion of radar echoes are utilized for imaging to re- duce data rate. The corresponding image formation algorithm for the proposed scheme was presented in the paper. The SAR echo signal of each channel can be reconstructed with high probability by using orthogonal matching pursuit (OMP) algorithm in Doppler frequency domain. The reconstructed echo signals of each channel are jointly processed by means of spectrum reconstructing fllter for compensat- ing Doppler spectrum aliasing resulting from non-uniform sampling in azimuth direction. The high quality SAR image can be obtained by us- ing chirp scaling algorithm. The efiectiveness of the proposed approach was validated by computer simulations using both point targets and distributed targets.

Sparse representation-based synthetic aperture radar imaging

There is increasing interest in using synthetic aperture radar (SAR) images in automated target recognition and decision-making tasks. The success of such tasks depends on how well the reconstructed SAR images exhibit certain features of the underlying scene. Based on the observation that typical underlying scenes usually exhibit sparsity in terms of such features, this paper presents an image formation method that formulates the SAR imaging problem as a sparse signal representation problem. For problems of complex-valued nature, such as SAR, a key challenge is how to choose the dictionary and the representation scheme for effective sparse representation. Since features of the SAR reflectivity magnitude are usually of interest, the approach is designed to sparsely represent the magnitude of the complex-valued scattered field. This turns the image reconstruction problem into a joint optimisation problem over the representation of magnitude and phase of the underlying field reflectivities. The authors develop the mathematical framework for this method and propose an iterative solution for the corresponding joint optimisation problem. The experimental results demonstrate the superiority of this method over previous approaches in terms of both producing high-quality SAR images and exhibiting robustness to uncertain or limited data.