SAR Imaging Algorithm Research Articles

Research on High Resolution SAR Imaging Algorithm Based on Optimal Sampling Length

Research on High Resolution SAR Imaging Algorithm Based on Optimal Sampling Length

Three-Dimensional Ground-Based SAR Imaging Algorithm Based on Keystone Formatting and Subblock

Three-dimensional (3D) ground-based synthetic aperture radar (GB-SAR) draws attention because of the ability to obtain high accuracy 3D information of the monitoring terrain. Besides, the 3D GB-SAR system is welcome due to the flexibility and large coverage in angle and range. However, existing 3D imaging algorithms for GB-SAR data focusing have limits of high computational complexity or narrow applicable scope. To realize 3D displacement monitoring with high spatial resolution and short revisit time, in this paper, a novel 3D imaging algorithm is proposed. Based on characteristics of the model of echo data from the large range and wide-view angle scenario, the proposed method uses keystone formatting to complete range migration correction and subblocks dechirping to realize horizontal focus. With the method, the reflectivity of the monitoring scenario is obtained by only one time of linear interpolation and several times of fast Fourier transforms. The main advantages of this algorithm are high imaging precision and low computational cost, and in addition, it is applicable for large illuminating coverage including the near-field and the far-field of the radar aperture. The imaging results are sampled on pseudo-spherical grid, aiming to simplify the formulation. Finally, this method is extensively validated with simulations.

SAR high‐resolution imaging from missing raw data using StOMP

SAR raw data missing occurs when radar is interrupted for various reasons. In order to deal with this problem, a method is proposed to focus the missing SAR raw data via stage-wise orthogonal matching pursuit (StOMP). A reference function in time domain is designed for the missing raw data. After matched with the reference function, the energy of missing raw data is concentrated in two-dimensional frequency domain. Then, StOMP algorithm is available to recover the matched raw data in two-dimensional frequency domain. The recovered raw data is available to be processed with traditional SAR imaging algorithms. The Omega-K algorithm is chosen to deal with the recovered raw data in experiments. Point target and area target simulations are carried out to validate the effectiveness of the proposed method for azimuth missing SAR raw data. The data missing rate is 50% in both point and area target simulation. The resolution of point targets can reach 0.3 m in both range and azimuth direction.

Circular SAR imaging algorithm based on polar format algorithm for moving target

For stationary targets, circular synthetic aperture radar (CSAR) imaging has tended to be mature and perfect. However, for moving targets, due to the long synthetic aperture and the variation of slant plane with the observation angle, the motion compensation is more complicated and has no effective approach. Based on the latest moving target trajectory reconstruction method in CSAR (2015), the polar format algorithm can be improved to image the moving targets when the motion parameters have been estimated. First, the authors placed the imaging reference point on the target to track it, and then compensated for the phase error caused by the incident angle changing and the wavefront curvature. The simulation results of point show that the algorithm is effective and accurate.

Clustered Sparsity-Driven SAR Imaging and Autofocus Algorithm in Structured Phase-Noisy Environments

Clustered Sparsity-Driven SAR Imaging and Autofocus Algorithm in Structured Phase-Noisy Environments

Wide‐band interference mitigation algorithm for SAR based on time‐varying filtering and sparse recovery

Wide-band interference (WBI) can severely degrade synthetic aperture radar image quality. This Letter proposes a new WBI suppression technique using time-varying filtering method and sparse recovery. In this method, a time-varying filter is firstly set up in the short-time Fourier transform domain as a pre-processing step to suppress the major power of WBI, and then, an adaptive dictionary-based sparse recovery algorithm is developed to recover the useful target echo loss resulting from time-varying filtering. Finally, traditional SAR imaging algorithms can be performed to obtain a well-focused image. Simulation results demonstrate the validity of the proposed method.

Level Set Segmentation Algorithm for High-Resolution Polarimetric SAR Images Based on a Heterogeneous Clutter Model

In this paper, a heterogeneous clutter model named ${\mathcal L}$ distribution is introduced into the level set method for segmentation of high-resolution polarimetric synthetic aperture radar (PolSAR) images. Level set methods are robust and effective techniques for segmentation. However, traditional level set methods for PolSAR data are based on the complex Wishart distribution, which is not an applicable model to high-resolution PolSAR images and heterogeneous regions such as forest and urban areas. The ${\mathcal L}$ distribution is proved to be a highly flexible model for multilook PolSAR data, which is based on the product model with a generalized-gamma-distributed texture component. The ${\mathcal L}$ -model-based level set segmentation method is assessed using C-band, X-band, and L-band PolSAR data acquired by RADARSAT-2, TerraSAR-X, and ESAR sensors, respectively. Experimental results show that the ${\mathcal L}$ -distribution model has a better capacity for characterizing high-resolution PolSAR data, especially for extremely heterogeneous regions. Compared with the Wishart-model-based and Kummer-U-model-based level set methods and Markov random field based methods, it is observed that the proposed level set algorithm can obtain more precise segmentation results.

Multilook SAR Image Segmentation with an Unknown Number of Clusters Using a Gamma Mixture Model and Hierarchical Clustering.

This paper presents a novel multilook SAR image segmentation algorithm with an unknown number of clusters. Firstly, the marginal probability distribution for a given SAR image is defined by a Gamma mixture model (GaMM), in which the number of components corresponds to the number of homogeneous regions needed to segment and the spatial relationship among neighboring pixels is characterized by a Markov Random Field (MRF) defined by the weighting coefficients of components in GaMM. During the algorithm iteration procedure, the number of clusters is gradually reduced by merging two components until they are equal to one. For each fixed number of clusters, the parameters of GaMM are estimated and the optimal segmentation result corresponding to the number is obtained by maximizing the marginal probability. Finally, the number of clusters with minimum global energy defined as the negative logarithm of marginal probability is indicated as the expected number of clusters with the homogeneous regions needed to be segmented, and the corresponding segmentation result is considered as the final optimal one. The experimental results from the proposed and comparing algorithms for simulated and real multilook SAR images show that the proposed algorithm can find the real number of clusters and obtain more accurate segmentation results simultaneously.

A Tensor Decomposition Based Multiway Structured Sparse SAR Imaging Algorithm with Kronecker Constraint

This paper investigates a structured sparse SAR imaging algorithm for point scattering model based on tensor decomposition. Several SAR imaging schemes have been developed by researchers for improving the imaging quality. For a typical SAR target scenario, the scatterers distribution usually has the feature of structured sparsity. Without considering this feature thoroughly, the existing schemes have still certain drawbacks. The classic matching pursuit algorithms can obtain clearer imaging results, but the cost is resulting in an extreme complexity and a huge computation resource consumption. Therefore, this paper put forward a tensor-based SAR imaging algorithm by means of multiway structured sparsity which makes full use of the above geometrical feature of the scatterers distribution. The spotlight SAR observation signal is formulated as a Tucker model considering the Kronecker constraint, and then a sparse reconstruction algorithm is introduced by utilizing the structured sparsity of the scene. The proposed tensor-based SAR imaging model is able to take advantage of the Kronecker information in each mode, which ensures the robustness for the signal reconstruction. Both the algorithm complexity analysis and numerical simulations show that the proposed method requires less computation than the existing sparsity-driven SAR imaging algorithms. The imaging realizations based on the practical measured data also indicate that the proposed algorithm is superior to the reference methods even in the severe noisy environment, under the condition of multiway structured sparsity.

A New SAR–GMTI High-Accuracy Focusing and Relocation Method Using Instantaneous Interferometry

In this paper, for a multichannel synthetic aperture radar–ground moving target indication (SAR--GMTI) system, a new high-accuracy focusing and relocating method using instantaneous interferometry, i.e., carrying out interferometry operation in the azimuth time domain before focusing, is proposed. One of the key steps of this method is to perform instantaneous interferometry to get accurate equivalent cross-track velocity (ECV) estimation for cross-track motion compensation. After that, the signal from a moving target is concentrated in range, and along-track motion compensation becomes convenient. Motion compensation transforms a moving target into a stationary one; thus, the conventional SAR imaging algorithm can be applied to focus the moving target. Finally, a strategy for accurately relocating a moving target is presented. The processing results of simulated and measured data illustrate the effectiveness of the proposed method.

DLSLA 3-D SAR imaging algorithm for off-grid targets based on pseudo-polar formatting and atomic norm minimization

This paper concerns the imaging problem for downward looking sparse linear array three-dimensional synthetic aperture radar (DLSLA 3-D SAR) under the circumstance of sparse and non-uniform cross-track dimensional virtual phase centers configuration. Since the 3-D imaging scene behaves typical sparsity in a certain domain, sparse recovery approaches hold the potential to achieve a better reconstruction performance. However, most of the existing compressive sensing (CS) algorithms assume the scatterers located on the pre-discretized grids, which is often violated by the off-grid effect. By contrast, atomic norm minimization (ANM) deals with sparse recovery problem directly on continuous space instead of discrete grids. This paper firstly analyzes the off-grid effect in DLSLA 3-D SAR sparse image reconstruction, and then introduces an imaging method applied to off-gird targets reconstruction which combines 3-D pseudo-polar formatting algorithm (pseudo-PFA) with ANM. With the proposed method, wave propagation and along-track image reconstruction are operated with pseudo-PFA, then the cross-track reconstruction is implemented with semidefinite programming (SDP) based on the ANM model. The proposed method holds the advantage of avoiding the off-grid effect and managing to locate the off-grid targets to accurate locations in different imaging scenes. The performance of the proposed method is verified and evaluated by the 3-D image reconstruction of different scenes, i.e., point targets and distributed scene.

Spaceborne SAR Imaging Algorithm for Coherence Optimized

This paper proposes SAR imaging algorithm with largest coherence based on the existing SAR imaging algorithm. The basic idea of SAR imaging algorithm in imaging processing is that output signal can have maximum signal-to-noise ratio (SNR) by using the optimal imaging parameters. Traditional imaging algorithm can acquire the best focusing effect, but would bring the decoherence phenomenon in subsequent interference process. Algorithm proposed in this paper is that SAR echo adopts consistent imaging parameters in focusing processing. Although the SNR of the output signal is reduced slightly, their coherence is ensured greatly, and finally the interferogram with high quality is obtained. In this paper, two scenes of Envisat ASAR data in Zhangbei are employed to conduct experiment for this algorithm. Compared with the interferogram from the traditional algorithm, the results show that this algorithm is more suitable for SAR interferometry (InSAR) research and application.

Robust Automatic Target Recognition Algorithm for Large-Scene SAR Images and Its Adaptability Analysis on Speckle

Aiming at the multiple target recognition problems in large-scene SAR image with strong speckle, a robust full-process method from target detection, feature extraction to target recognition is studied in this paper. By introducing a simple 8-neighborhood orthogonal basis, a local multiscale decomposition method from the center of gravity of the target is presented. Using this method, an image can be processed with a multilevel sampling filter and the target’s multiscale features in eight directions and one low frequency filtering feature can be derived directly by the key pixels sampling. At the same time, a recognition algorithm organically integrating the local multiscale features and the multiscale wavelet kernel classifier is studied, which realizes the quick classification with robustness and high accuracy for multiclass image targets. The results of classification and adaptability analysis on speckle show that the robust algorithm is effective not only for the MSTAR (Moving and Stationary Target Automatic Recognition) target chips but also for the automatic target recognition of multiclass/multitarget in large-scene SAR image with strong speckle; meanwhile, the method has good robustness to target’s rotation and scale transformation.

A Downsampled SAR-BM3D Despeckling Approach for Single-Look SAR Images in High Resolution

SAR-BM3D is one of the state of the art despeckling algorithms for SAR images. However, when tackling with high resolution SAR images, it often has an unsatisfying despeckling performance in the homogeneous smooth regions, together with a high time complexity. In this paper, a novel downsampled SAR-BM3D despeckling approach combined with edge compensation is proposed. The proposed algorithm consists of two steps. First, despeckle the image which is a downsampled version of original image with SAR-BM3D. Then, compensate edges in each level when upsampling. This approach not only utilizes the good ability of feature preservation, but also improves performance of smoothing homogenous regions. When it comes to high resolution SAR images, the efficiency can be raised by six to seven times, compared to original SAR-BM3D. Experiments on simulated and real SAR images show that the proposed method reaches a high level in terms of visual quality and act more efficiently.

Quadratic Compressed Sensing Based SAR Imaging Algorithm for Phase Noise Mitigation

Quadratic Compressed Sensing Based SAR Imaging Algorithm for Phase Noise Mitigation

An improved speckle-reduction algorithm for SAR images based on anisotropic diffusion

This paper mainly studies the algorithm of anisotropic diffusion for speckle noise removal of SAR images. Because the Gauss curvature driven diffusion method is sensitive to the noise and is of low efficiency on suppressing the speckle noise, an improved denoising algorithm is proposed. The new algorithm introduces the difference curvature as the diffusion coefficients of the function, which solves the problem that Gauss curvature driven diffusion is sensitive to the speckle noise, further, Tukey’s biweight function is used to control the curvature diffusion model, which can not only better protect edges, but also automatically control the diffusion. Numerical experiments show that the improved algorithm can preserve the information of textures, edges while inhibiting the speckle of SAR images.

A Novel Helicopter-Borne Rotating SAR Imaging Model and Algorithm Based on Inverse Chirp-Z Transform Using Frequency-Modulated Continuous Wave

With an appropriate geometric configuration, a helicopter-borne rotating synthetic aperture radar (ROSAR) can break through the limitations of conventional strip-map monostatic SAR on forward-looking imaging. Owing to such a capability, ROSAR has extensive potential applications, such as self-navigation and self-landing. Moreover, it has many advantages if combined with frequency-modulated continuous wave (FMCW) technology. In this letter, a novel geometric platform configuration and an imaging algorithm for helicopter-borne FMCW-ROSAR are proposed. First, by adopting the higher order approximation of slant range model to improve the azimuth resolution for FMCW-ROSAR, the precise 2-D spectrum of the echo signal is derived based on series reversion. Moreover, at the same time, the Doppler offset caused by the continuous motion of the antenna is analyzed and compensated as well. Then, according to the analysis on the range-dependent velocity variation caused by ROSAR geometric configuration, an efficient inverse chirp-Z transform is utilized to remove the variant range cell migration, and a well-focused SAR image can thus be obtained. Finally, the experimental results with simulated data demonstrate the effectiveness of the proposed algorithm.

Improved Pencil Back-Projection Method with Image Segmentation for Far-Field/Near-Field SAR Imaging and RCS Extraction

In order to alleviate the far-field requirement from RCS measurements, a two-step procedure of first obtaining the SAR image of the object followed by an RCS extraction has been recently introduced. In this manuscript, a spotlight mode SAR-imaging algorithm that improves far-field and near-field tomographic reconstruction resolution (and RCS extraction accuracy) is presented. This algorithm comprises the following steps: 1) the range profiles are obtained with super resolution by utilizing the band-pass matrix pencil method. 2) Back-projection, which does not require polar-to-Cartesian interpolation in the spectral domain, is used for the far-field or near-field image reconstruction. 3) Background of the reconstructed image is removed with the Otsu's thresholding. 4) Complex amplitudes and locations of the scattering centers are selected from the thresholded image with an amplitude extraction procedure. Finally, obtained scattering centers are used for RCS extraction. The simulation and measurement results show that the unfavorable effects of the point spread function are reduced with the developed algorithm, and hence, the quality of the SAR image is increased, and the accuracy of the extracted RCS is improved.

Improved Chirp Scaling Algorithm Based on Fractional Fourier Transform and Motion Compensation

Fractional fourier transform (FrFT) is a new tool of time-frequency analysis, and a good tool to compress chirp signal and to process non-stationary motion error. Aiming at the problem of the conventional SAR imaging algorithm based on FFT and two-step motion compensation, that of the effect of non-stationary error elimination is not obvious and the improvement of resolution is restricted, an improved Chirp Scaling imaging algorithm based on FrFT and motion compensation is put forward in this paper, which is expected to eliminate the influence of range motion error, so as to ob- tain high quality SAR images. Simulation results and imaging results of real SAR data show that the proposed algorithm can eliminate the influence of range motion error effectively. (the real SAR data provided by Institute of Electronics, Chi- nese Academy of Sciences).

Visual Attention Model Based Vehicle Target Detection in Synthetic Aperture Radar Images: A Novel Approach

The human visual system (HVS) possesses a remarkable ability of real-time complex scene analysis despite the limited neuronal hardware available for such tasks. The HVS successfully overcomes the problem of information bottleneck by selecting potential regions of interest and reducing the amount of data transmitted to high-level visual processing. On the other hand, many man-made systems are also confronted with the same problem yet fail to achieve satisfactory performance. Among these, the synthetic aperture radar-based automatic target recognition (SAR-ATR) system is a typical one, where the traditional detection algorithm employed is termed the constant false alarm rate (CFAR). It is known to exhibit a low probability of detection (PD) and consumes too much time. The visual attention model (VAM) is a computational model, which aims to imitate the HVS in predicting where humans will look. The application of VAM to the SAR-ATR system could thus help solve the problem of effective real-time processing of complex large amounts of data. In this paper, we propose a new vehicle target detection algorithm for SAR images based on the VAM. The algorithm modifies the well-known Itti model according to the requirements of target detection in SAR images. The modified Itti model locates salient regions in SAR images and following top-down processing reduces false alarms by using prior knowledge. Real SAR data are used to demonstrate the validity and effectiveness of the proposed algorithm, which is also benchmarked against the traditional CFAR algorithm. Simulation results show comparatively improved performance in terms of PD, number of false alarms and computing time.