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 used in synthetic aperture radar (SAR) imaging due to the maturity of compressed sensing theory. As a typical sparse reconstruction method, $L_1$ regularization generally causes bias effects as well as ignoring region-based features. Our team has proposed to linearly combine the nonconvex penalty and the total variation (TV)-norm penalty as a compound regularizer in the imaging model, called nonconvex and TV regularization, which can not only reduce the bias caused by $L_1$ regularization but also enhance point-based and region-based features. In this paper, we use the variable splitting scheme and modify the alternating direction method of multipliers (ADMM), generating a novel algorithm to solve the above optimization problem. Moreover, we analyze the radiometric properties of sparse-signal-processing-based SAR imaging results and introduce three indexes suitable for sparse SAR imaging for quantitative evaluation. In experiments, we process the Gaofen-3 (GF-3) data utilizing the proposed method, and quantitatively evaluate the reconstructed SAR image quality. Experimental results and image quality analysis verify the effectiveness of the proposed method in improving the reconstruction accuracy and the radiometric resolution without sacrificing the spatial resolution.

The azimuth multichannel synthetic aperture radar (SAR) technology is capable of overcoming the minimum antenna area constraint and achieving high-resolution and wide-swath (HRWS) imaging. Generally speaking, the pulse repetition frequency (PRF) of the spaceborne multichannel SAR systems should satisfy the azimuthal uniform sampling condition, but it is sometimes impossible due to the limitation of radar system timing conditions, which is often referred as “coverage diagram.” For the Gaofen-3 system, the PRF of each channel at some beam positions is slightly less than that of uniform sampling in the dual-channel mode, leading to the nonuniform undersampling, hence, resulting the azimuth ambiguities in the recovered images. Although the ambiguous energy in Gaofen-3 images is not high in general, it is still noticeable amid surrounding weak clutters of strong targets. In this article, a novel multichannel SAR imaging method for nonuniform undersampling based on ${{\boldsymbol{L}}_{2,{\boldsymbol{q}}}}$ regularization $(0 < {\boldsymbol{q}} \leq 1)$ is proposed. By analyzing the reasons of azimuth ambiguities in the multichannel SAR system, the imaging model is established with emphasizing the difference from conventional single-channel SAR. Then, we combine the multichannel SAR data processing operators with the group sparsity property to construct the novel imaging method. The group sparsity property is modeled by the $2,{\boldsymbol{q}}$ -norm, and the ${{\boldsymbol{L}}_{2,{\boldsymbol{q}}}}$ regularization problem can be solved via sparse group thresholding function. It is shown that the proposed method can efficiently suppress the azimuth ambiguities caused by nonuniform undersampling. Simulations and Gaofen-3 real data experiments are exploited to verify the effectiveness of the proposed method.

High resolution is a key trend in the development of synthetic aperture radar (SAR), which enables the capture of fine details and accurate representation of backscattering properties. However, traditional high-resolution SAR imaging algorithms face several challenges. Firstly, these algorithms tend to focus on local information, neglecting nonlocal information between different pixel patches. Secondly, speckle is more pronounced and difficult to filter out in high-resolution SAR images. Thirdly, the process of high-resolution SAR imaging generally involves high time and computational complexity, making real-time imaging difficult to achieve. To address these issues, we propose a hyper-pixel high-resolution SAR imaging network (HPHR-SAR-Net) for rapid despeckling in high-resolution modes. Based on the concept of hyper-pixel techniques, we initially combine nonconvex and nonlocal total variation as compound regularization. This approach more effectively despeckles and manages the relationship between pixels while reducing bias effects caused by convex constraints. Subsequently, we solve the compound regularization model using the Alternating Direction Method of Multipliers (ADMM) algorithm and unfold it into a Deep Unfolded Network (DUN). The network's parameters are adaptively learned in a data-driven manner, and the learned network significantly increases imaging speed. Additionally, the proposed HPHR-SAR-Net is compatible with high-resolution imaging modes such as spotlight, staring spotlight, and sliding spotlight. In this paper, we demonstrate the superiority of HPHR-SAR-Net through experiments in both simulated and real SAR scenarios. The results indicate that HPHR-SAR-Net can rapidly perform high-resolution SAR imaging from raw echo data, producing accurate imaging results.

Tomographic synthetic aperture radar (TomoSAR) is an advanced SAR interferometric technique to retrieve 3D spatial information. However, the standard deviation in the reconstructed elevation could be high due to the noise in the interferometric phases, which makes the denoising filter crucial before tomographic reconstruction. In this paper, we propose an unsupervised multichannel SAR interferometric phase denoising method based on the Convolution Neural Network (CNN). It utilizes the Weighted Least-Squares (WLS) regularization combining with the covariance of multichannel interferometric phases to minimize the standard deviation of phase noise, which leads to the accurate and complete TomoSAR reconstruction. This network is trained by real SAR images and the results of both simulated and real observations verify the effectiveness of our proposed method.

Synthetic aperture radar (SAR) imaging under the sparse constraint is a developing SAR imaging scheme that emerged in the recent decade. ℓ ₁ -norm and the total variation (TV)-norm are two widely used constraints in the reconstruction model for enhancing the features of the point distributed targets respectively. However, ℓ ₁ regularization often generates a biased estimation and the TV regularization always over-smooths isolated point targets in the scene. Thus, we propose the nonconvex-nonlocal total variation (NLTV) regularization in this paper, which can 1)improve the reconstruction accuracy and 2)avoid over-smoothing of the isolated point targets while simultaneously enhancing the features of point and distributed targets. The modified variable splitting-alternating direction method of multipliers (VS-ADMM) is introduced to solve the compound regularization. The performance of the proposed method is verified using simulated data and real data.