Sparse SAR Imaging and Quantitative Evaluation Based on Nonconvex and TV Regularization

Xu, Zhongqiu; Zhang, Bingchen; Zhou, Guoru; Zhong, Lihua; Wu, Yirong

doi:10.3390/rs13091643

Cited by 14 publications

(8 citation statements)

References 28 publications

(42 reference statements)

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“…Algorithm 2 gives a detailed process of L-hypersurface for dual regularization parameters selection (K = 2). For NC-TV regularization SAR model of image reconstruction, use the modified ADMM algorithm given by [35] to calculate xλ1,λ2 . For spatial regularization model of TomoSAR image reconstruction, use the algorithm given by [11] to calculate xλ1,λ2,λ3,λ4 .…”

Section: Algorithmmentioning

confidence: 99%

L-Hypersurface Based Parameters Selection in Composite Regularization Models With Application to SAR and TomoSAR Imaging

Fan,

Wang,

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Composite regularization models are widely used in sparse signal processing, making multiple regularization parameters selection a significant problem to be solved. Variety kinds of composite regularization models are used in sparse microwave imaging, including ℓ1 and ℓ2 penalty, nonconvex and TV penalty, combined dictionary, etc. In this article, a new adaptive multiple regularization parameters selection method named L-hypersurface is proposed. The effectiveness of the proposed method is verified by experiments. Simulation experiments indicate that the selected optimal regularization parameters have satisfied reconstruction results, both visually and numerically. Furthermore, experiments on Gaofen-3 SAR satellite data are also exploited to show the performance of the proposed method.

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Section: Algorithmmentioning

confidence: 99%

L-Hypersurface Based Parameters Selection in Composite Regularization Models With Application to SAR and TomoSAR Imaging

Fan,

Wang,

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…In comparison with other iterative methods, ADMM is versatile and adaptable for various regularizations. Inspired by the modified variable splitting ADMM [50], the modified constrained form of Equ. ( 14) can be represented as:…”

Section: A Iterative Solution Of High Resolution Sar Imaging Modelmentioning

confidence: 99%

HPHR-SAR-Net: Hyperpixel High-Resolution SAR Imaging Network Based on Nonlocal Total Variation

Zhou

Fan

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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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

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“…In recent years, sparse SAR imaging has achieved a superior performance in terms of the image quality over traditional matched filtering (MF)-based SAR imaging methods [20], [21], [22], [23], [24], [25], [26], [27], [28], [29]. Sparse SAR imaging uses compressed sensing (CS) reconstruction algorithms to achieve high-quality recovery of sparse scenes with fewer data.…”

Section: Introductionmentioning

confidence: 99%

Saliency-Based SAR Target Detection via Convolutional Sparse Feature Enhancement and Bayesian Inference

Luo

Wang

et al. 2023

IEEE Trans. Geosci. Remote Sensing

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Traditional synthetic aperture radar (SAR) target detection methods use matched filtered SAR images as input, and the detection performance is restricted due to the high sidelobes and speckle noise of these images. Sparse SAR imaging methods developed in recent years provide the advantages of reducing sidelobes, noise, and clutter. The imaging results obtained with these methods could help improve the SAR target detection performance. In this article, to improve the target detection performance using sparse SAR images as input, we proposed a convolutional sparse feature enhancement method to meet the needs of Bayesian saliency detection. The proposed Bayesian saliency joint target detection method comprised the following three steps: first, to obtain sparse SAR images with continuous contours and fewer holes in the target area, we proposed a convolutional L 1 sparse regularization method. Second, a regularization parameter optimization method was derived to quickly obtain optimal regularization parameters for saliency detection. Finally, target detection results were obtained through a superpixel-based Bayesian saliency joint detector. Extensive experiments verified that the proposed method could improve the SAR target detection accuracy in complex backgrounds.

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Sparse SAR Imaging and Quantitative Evaluation Based on Nonconvex and TV Regularization

Cited by 14 publications

References 28 publications

L-Hypersurface Based Parameters Selection in Composite Regularization Models With Application to SAR and TomoSAR Imaging

L-Hypersurface Based Parameters Selection in Composite Regularization Models With Application to SAR and TomoSAR Imaging

HPHR-SAR-Net: Hyperpixel High-Resolution SAR Imaging Network Based on Nonlocal Total Variation

Saliency-Based SAR Target Detection via Convolutional Sparse Feature Enhancement and Bayesian Inference

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