Sparse signal processing is widely used in SAR imaging with the development of compressed sensing in recent years. As an efficient tool for sparse reconstruction, L1 regularization induces sparsity most effectively. L1-norm penalty and the total variation (TV)-norm penalty are usually combined as a compound regularizer to enhance point-based features as well as region-based features. However, L1-norm penalty is convex, and the analytic solution of sparse signal reconstrcution algorithm based on L1 regularization is a biased estimation. In this paper, we quantitatively analyze the variation of reconstruction bias with complex reflectivity of targets, undersampling ratio and noise power. In order to suppress the bias effect and improve the reconstruction accuracy, we adopt the nonconvex regularization based sparse SAR imaging method with a family of nonconvex penalties. Furthermore, this paper linearly combine the nonconvex penalty and the TV-norm penalty as a compound regularizer in the imaging model, which can improve the reconstruction accuracy of distributed targets as well as maintain the continuity of the backscattering coefficient. Simulation results show that compared with L1 regularization, nonconvex regularization can reduce the average relative bias from 10.88% to 0.25%, compared with the matched filtering method and L1 & TV regularization, nonconvex & TV regularization can reduce the variance of the distributed target with uniform backscattering coefficient by 80% while guaranteeing the reconstruction accuracy. Experiments based on Gaofen-3 SAR data are also performed to verify the effectiveness of the proposed method.

Sparse signal processing has been applied in synthetic-aperture radar (SAR) imaging. As a typical sparse reconstruction model, $L_{1}$ regularization often underestimates the intensities of the targets. The estimated radar cross section (RCS) is related to the pixel intensity. Thus, the linear relationship between the targets’ intensities cannot kept. The underestimation will also cause radiometric errors and affect the quantitative use of the SAR data. In this letter, we present a SAR imaging method based on generalized minimax concave (GMC) penalty. GMC is a nonconvex penalty and its cost function is convex. GMC can avoid the underestimation of pixel intensity. In the iteration, the azimuth-range decouple operators are used to avoid the huge memory and computational costs. The performance of the proposed method is verified using real data.

In wide angle synthetic aperture radar (WASAR), the scattering behavior varies with aspect angle. Subaperture methods can be adopted to accommodate the anisotropic behavior. Modified compressive sensing (MCS) focuses on reconstructing a sparse signal from a limited number of its linear projections when a part of its support is known. It can achieve the accurate recovery with less samples compared with the traditional compressive sensing (CS). Since the support sets of different subaperture images are highly overlapped across the whole aperture, MCS can be adopted in WASAR subaperture imaging. Iterative hard thresholding with partial known support (IHT-PKS) is adopted to recover the MCS model. Azimuth-range decouple operators based on backprojection are implemented in IHT-PKS to avoid huge memory cost of the measurement matrix. Compared with the traditional CS-based WASAR subaperture imaging method, the proposed method can reconstruct the aspect dependent scattering more accurately and is more robust to noise. The proposed method can also reconstruct the scene with less undesirable artifacts at downsampling rate. The simulation and experiment results validate the proposed method.

Wide angle synthetic aperture radar (WASAR) receives data from a large angle, which causes the problem of aspect dependent scattering. $L_1$ regularization is a common compressed sensing (CS) model. The $L_1$ regularization based WASAR imaging method divides the whole aperture into subapertures and reconstructs the subaperture images individually. However, the aspect dependent scattering recovery of it is not accurate. The subaperture images of WASAR can be regarded as the SAR video. The support set among the different frames of SAR video are highly overlapped. Least squares on compressed sensing residuals (LS-CS-Residuals) can reconstruct the time sequences of sparse signals which change slowly with time. This is to replace CS on the observation by CS on the least squares (LS) residual computed using the prior estimate of the support. In this paper, we introduce LS-CS-Residual into WASAR imaging. In the iteration of LS-CS-Residual, the azimuth-range decoupled operators are used to avoid the huge memory cost. Real data processing results show that LS-CS-Residual can estimate the aspect dependent scatterings of the targets more accurately than CS based methods.

Synthetic aperture radar (SAR) image registration is an important process in many applications, such as image stitching and remote sensing surveillance. The registration accuracy is commonly affected by the presence of speckle noise in SAR images. When speckle noise is intense, the number of image features acquired by single-feature-based methods is insufficient. An SAR image registration method that combines nonlinear diffusion filtering, Hessian features and edge points is proposed in this paper to reduce speckle noise and obtain more image features. The proposed method uses the infinite symmetric exponential filter (ISEF) for image pre-processing and nonlinear diffusion filtering for scale-space construction. These measures can remove speckle noise from SAR images while preserving image edges. Hessian features and edge points are also employed as image features to optimize the utilization of feature information. Experiments with different noise levels, geometric transformations and image scenes demonstrate that the proposed method effectively improves the accuracy of SAR image registration compared with the SIFT-OCT, SAR-SIFT, Harris-SIFT, NF-Hessian and KAZE-SAR algorithms.