With the rapid development of compressed sensing theories and applications, sparse signal processing has been widely used in synthetic aperture radar (SAR) imaging during the recent years. As an efficient tool for sparse reconstruction, 1 optimization induces sparsity the most effectively, and the 1 -norm penalty is usually combined with the total variation norm (TV-norm) penalty to construct a compound regularizer in order to enhance the pointbased features as well as the region-based features. However, as a convex optimizer, the analytic solution of 1 regularization-based sparse signal reconstruction is usually a biased estimation. Aiming at this issue, in this article, we quantitatively analyzed the variation of reconstruction bias with respect to the complex reflectivity of targets, the undersampling ratio and the noise power. In order to reduce the bias effect and improve the reconstruction accuracy, we adopted the nonconvex regularization-based sparse SAR imaging method with a nonconvex penalty family. Furthermore, we linearly combined the nonconvex penalty and the TV-norm penalty to form a compound regularizer in the imaging model, which can improve the reconstruction accuracy of distributed targets and maintain the continuity of the backscattering coefficient. Simulation results showed that compared with 1 regularization, nonconvex regularization can reduce the average relative bias from 10.88% to 0.25%; compared with the matched filtering method and 1 and TV regularization, nonconvex & TV regularization can reduce the variance of the uniformly distributed targets by 80% without losing of reconstruction accuracy. Experiments on Gaofen-3 SAR data are also exploited 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, L1 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 L1 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.
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 hyperpixel 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 highresolution SAR imaging from raw echo data, producing accurate imaging results.Index Terms-Synthetic aperture radar (SAR), sparse microwave imaging, hyper-pixel, high-resolution, deep unfolding network (DUN), alternating direction method of multipliers (ADMM)
I. INTRODUCTIONS YNTHETIC aperture radar (SAR) is an active imaging system. Unlike optical sensors, SAR transmits microwave signals with surface penetration capabilities, enabling it to
Sparse signal processing has been widely used in synthetic aperture radar imaging and feature enhancement of images in the recent decade. Sparse regularization 1 can reduce the imaging noise level and suppress sidelobes. However, the suppression of sidelobes by sparse regularization often pays the price of losing information of weak targets. Therefore, the sparse regularization method combining spatially variant apodization is proposed in this paper, which can suppress noise, sidelobes and retain detail information. The performance of the proposed method is verified using simulated and real data.
Data availability statement:The data that support the findings of this study are available from the corresponding author upon reasonable request.
First principles calculations are performed to explore the effect of applied vertical press on the electronic structure of monolayer MoS 2 . It is demonstrated that MoS 2 monolayer exhibits significant modulation of its band gap and band edges by the applied vertical press. Monolayer MoS 2 homojunction with type-I band alignment can be formed by applying vertical press partially on the MoS 2 monolayer. The effect of the vertical press on the electronic structure of MoS 2 monolayer homojunction can be significantly enhanced by enlarging the pressed area. A semiconductor-to-metal transition occurs when the MoS 2 homojunction is subjected to large presses. The varying band diagram including the band edges and band gap leads to a funnelling effect with both holes and electrons concentrate on the pressed region. Our results will provide intriguing opportinuities for designing optoelectronic and photovoltaic devices with good performance based on 2D materials.
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