Distributed compressive sensing for multi-baseline circular SAR image formation

Farhadi, Masoud; Chen, Jie

doi:10.1109/ist.2017.8261527

Cited by 7 publications

(6 citation statements)

References 19 publications

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“…It is an efficient way to apply sparsity constraint in space domain, in which targets are assumed to be sparse. Generally, the sparsity of matrix is expressed by p -norm, so the

[ 15 , 16 , 17 , 18 , 19 , 20 , 21 ]. In the transform domain generated by Wavelet or Fourier transformation, targets may show better sparsity than in space domain.…”

Section: Introductionmentioning

confidence: 99%

Single Target SAR 3D Reconstruction Based on Deep Learning

Wang

Guo

Zhang

et al. 2021

Sensors

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Synthetic aperture radar tomography (TomoSAR) is an important 3D mapping method. Traditional TomoSAR requires a large number of observation orbits however, it is hard to meet the requirement of massive orbits. While on the one hand, this is due to funding constraints, on the other hand, because the target scene is changing over time and each observation orbit consumes lots of time, the number of orbits can be fewer as required within a narrow time window. When the number of observation orbits is insufficient, the signal-to-noise ratio (SNR), peak-to-sidelobe ratio (PSR), and resolution of 3D reconstruction results will decline severely, which seriously limits the practical application of TomoSAR. In order to solve this problem, we propose to use a deep learning network to improve the resolution and SNR of 3D reconstruction results under the condition of very few observation orbits by learning the prior distribution of targets. We use all available orbits to reconstruct a high resolution target, while only very few (around 3) orbits to reconstruct a low resolution input. The low-res and high-res 3D voxel-grid pairs are used to train a 3D super-resolution (SR) CNN (convolutional neural network) model, just like ordinary 2D image SR tasks. Experiments on the Civilian Vehicle Radar dataset show that the proposed deep learning algorithm can effectively improve the reconstruction both in quality and in quantity. In addition, the model also shows good generalization performance for targets not shown in the training set.

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“…It is an efficient way to apply sparsity constraint in space domain, in which targets are assumed to be sparse. Generally, the sparsity of matrix is expressed by p -norm, so the

[ 15 , 16 , 17 , 18 , 19 , 20 , 21 ]. In the transform domain generated by Wavelet or Fourier transformation, targets may show better sparsity than in space domain.…”

Section: Introductionmentioning

confidence: 99%

Single Target SAR 3D Reconstruction Based on Deep Learning

Wang

Guo

Zhang

et al. 2021

Sensors

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“…Circular SAR can also provide 3D scattering information, but the 3D images are deformed by strong cone-shaped sidelobes [1][2][3]. Multicircular SAR, or holographic SAR tomography (HoloSAR), creates another synthetic aperture in elevation that mitigates these undesirable sidelobes, thus providing complete 3D data reconstruction with very high resolution [4][5][6][7][8][9]. HoloSAR geometry acquisition consists of multiple circular flight paths at different fixed heights.…”

Section: Introductionmentioning

confidence: 99%

“…Ponce et al did not pursue 3D focus-ing with their FFBP due to practical reasons [4]. Other HoloSAR solutions have used the direct BP algorithm [6,8], sparse reconstruction models [4,5,7], adaptive imaging [6,9], or a combination of them. Apart from the choice in the algorithm, there are two common approaches:…”

Section: Introductionmentioning

confidence: 99%

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Spiral SAR Imaging with Fast Factorized Back-Projection: A Phase Error Analysis

Góes

Castro

Bins

et al. 2021

Sensors

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This paper presents a fast factorized back-projection (FFBP) algorithm that can satisfactorily process real P-band synthetic aperture radar (SAR) data collected from a spiral flight pattern performed by a drone-borne SAR system. Choosing the best setup when processing SAR data with an FFBP algorithm is not so straightforward, so predicting how this choice will affect the quality of the output image is valuable information. This paper provides a statistical phase error analysis to validate the hypothesis that the phase error standard deviation can be predicted by geometric parameters specified at the start of processing. In particular, for a phase error standard deviation of ~12°, the FFBP is up to 21 times faster than the direct back-projection algorithm for 3D images and up to 13 times faster for 2D images.

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“…Without time-consuming 2D interpolation, it is capable of decreasing the loss of spectrum, while significantly improving the computational efficiency compared with direct backprojection [24]. Farhadi et al proposed a distributed compressed sensing algorithm for circular SAR imaging, which improves the resolution and reduces the side-lobe effect in the full-aperture 3D image, and meanwhile reduces the computation [25]. In recent years, the research on Circular SAR imaging mainly focuses on Deep Learning, Compression Sensing, 3D imaging and other aspects [26][27][28][29][30][31][32][33][34].…”

Section: Introductionmentioning

confidence: 99%

Modulus Stretch-Based Circular SAR Imaging with Contour Thinning

Peng

Zheng³

2019

Applied Sciences

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This paper presents a modulus stretch-based circular Synthetic Aperture Radar (SAR) imaging method. This method improves the traditional backprojection algorithm for circular SAR imaging, and introduces the modulus stretch transformation function in the imaging process. By performing a modulus stretch transformation on the intermediate results, the target contour in the final imaging result is thinner and clearer. A thinner and clearer contour can help to increase the recognizability of the target and provide a basis for subsequent target recognition. The proposed method is demonstrated on the line target imaging simulations and Gothca dataset.

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Distributed compressive sensing for multi-baseline circular SAR image formation

Cited by 7 publications

References 19 publications

Single Target SAR 3D Reconstruction Based on Deep Learning

Single Target SAR 3D Reconstruction Based on Deep Learning

Spiral SAR Imaging with Fast Factorized Back-Projection: A Phase Error Analysis

Modulus Stretch-Based Circular SAR Imaging with Contour Thinning

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