Azimuth Ambiguities Suppression Using Group Sparsity and Nonconvex Regularization for Sliding Spotlight Mode: Results on QILU-1 SAR Data

Zhou, Guoru; Liu, Mingqian; Xu, Zhongqiu; Zhang, Bingchen; Wu, Yirong

doi:10.1109/igarss46834.2022.9884638

Cited by 3 publications

(3 citation statements)

References 7 publications

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“…For the accurate preservation of SAR signals, most primary and secondary products derived from SAR images are stored in a 16-bit format [50,51]. However, in many downstream tasks of learning-based SAR image processing, such as compression, detection, and recognition [13][14][15][16][17], the utilized data are in 8-bit JPEG and PNG formats.…”

Section: Background and Motivationmentioning

confidence: 99%

Synthetic Aperture Radar Image Compression Based on Low-Frequency Rejection and Quality Map Guidance

Deng,

Huang

2024

Remote Sensing

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Synthetic Aperture Radar (SAR) images are widely utilized in the field of remote sensing. However, there is a limited body of literature specifically addressing the compression of SAR learning images. To address the escalating volume of SAR image data for storage and transmission, which necessitates more effective compression algorithms, this paper proposes a novel framework for compressing SAR images. Experimental validation is performed using a representative low-resolution Sentinel-1 dataset and the high-resolution QiLu-1 dataset. Initially, we introduce a novel two-stage transformation-based approach aimed at suppressing the low-frequency components of the input data, thereby achieving a high information entropy and minimizing quantization losses. Subsequently, a quality map guidance image compression algorithm is introduced, involving the fusion of the input SAR images with a target-aware map. This fusion involves convolutional transformations to generate a compact latent representation, effectively exploring redundancies between focused and non-focused areas. To assess the algorithm’s performance, experiments are carried out on both the low-resolution Sentinel-1 dataset and the high-resolution QiLu-1 dataset. The results indicate that the low-frequency suppression algorithm significantly outperforms traditional processing algorithms by 3–8 dB when quantifying the input data, effectively preserving image features and improving image performance metrics. Furthermore, the quality map guidance image compression algorithm demonstrates a superior performance compared to the baseline model.

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Section: Background and Motivationmentioning

confidence: 99%

Synthetic Aperture Radar Image Compression Based on Low-Frequency Rejection and Quality Map Guidance

Deng,

Huang

2024

Remote Sensing

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“…(12) and Equ. (13), respectively, as described in [48]. Here, F a and F −1 a , as well as F r and F −1 r , correspond to the Fourier and inverse Fourier transform matrices in azimuth and range, respectively; Θ 1 represents the differential RCMC matrix; Θ 2 is the bulk RCMC and matched filter matrix in range; Θ 3 denotes the matched filter matrix in azimuth; Θ 4 is the residual Doppler phase correction matrix; Θ deramping and Θ ramping are the deramping and ramping operation matrices, respectively; ⊙ denotes the Hadamard product; and (•)…”

Section: High-resolution Sar Fast 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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“…The combination of sparse signal processing and SAR imaging can potentially improve the performance and reduce calculation complexity. With the development of sparse signal processing from point targets [1] to phase targets, regularization models are no longer limited in single penalty term, but combined penalty terms, including ℓ 1 and TV norm [2], nonconvex and TV-norm [3], nonconvex and non-local TV norm [4], ℓ 1 and ℓ 2 norm [5], nonconvex and ℓ 2,1 norm [6], combined dictionaries [7], morphology regularization [8] etc. Each penalty term of these composite regularization models is multiplied by a regularization parameter.…”

Section: Introductionmentioning

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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Azimuth Ambiguities Suppression Using Group Sparsity and Nonconvex Regularization for Sliding Spotlight Mode: Results on QILU-1 SAR Data

Cited by 3 publications

References 7 publications

Synthetic Aperture Radar Image Compression Based on Low-Frequency Rejection and Quality Map Guidance

Synthetic Aperture Radar Image Compression Based on Low-Frequency Rejection and Quality Map Guidance

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

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

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