Parametric Image Reconstruction for Edge Recovery From Synthetic Aperture Radar Echoes

Zeng, Tao; Wei, Yangkai; Ding, Zegang; Chen, Xinliang; Wang, Yan; Yu, Fan; Li, Teng

doi:10.1109/tgrs.2020.3006884

Cited by 12 publications

(5 citation statements)

References 42 publications

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“…The structure of the building facade in 2D imaging and 3D imaging exists disappearance phenomenon, and the simulation results are consistent with the performance, the specific causes and details refer to the literature [25,26]. The results are shown for all height estimation results after normalizing the magnitude for targets greater than −20 dB.…”

Section: Real P-band Small Uav Tomosar Experimentssupporting

confidence: 79%

“…For the disappearance of the building facade structure in the estimated recovery, as shown by the red dashed line in Figure 8. For smooth walls, the main reason is the coherent superposition of electromagnetic waves on the wall, retaining only the top and bottom edge structure; for rough walls, its main reason is the small angle between the electromagnetic wave and the wall, resulting in weak backward scattering [25,26]. Therefore, it cannot be effectively recovered in its corresponding 3D image either.…”

Section: Computer Simulationmentioning

confidence: 99%

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Tomographic SAR imaging with large elevation aperture: a P-band small UAV demonstration

Zeng

Liu

Wang

et al. 2022

Sci. China Inf. Sci.

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Elevation resolution is an important indicator in tomographic SAR imaging as it represents the ability to discriminate closed targets in elevation. In general, the elevation resolution is proportional to the length of the elevation aperture. However, as the elevation aperture increases, the geometric consistency of the image will undesirably deteriorate and hence fails the image coregistration approach required by the traditional super-resolution tomographic imaging. In this paper, a new super-resolution tomographic imaging method is proposed to overcome the inconsistency problem caused by the large elevation aperture. The core strategy is to get rid of two-dimensional image coregistration by applying a three-dimensional (3D) back projection like imaging manner: the 3D space is firstly divided into a 3D imaging grid, each of which is individually imaged via compressive sensing for super-resolution. The effectiveness of the proposed approach is evaluated by both computer simulations and real P-band UAV SAR data.

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Section: Real P-band Small Uav Tomosar Experimentssupporting

confidence: 79%

Section: Computer Simulationmentioning

confidence: 99%

Tomographic SAR imaging with large elevation aperture: a P-band small UAV demonstration

Zeng

Liu

Wang

et al. 2022

Sci. China Inf. Sci.

Self Cite

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“…In this section, details about the developed CI numerical model are provided. Compared to traditional radar imaging techniques such as the synthetic aperture radar (SAR) [49], [50] or phased-array [51], [52] systems, CI techniques offer significant advantages in terms of hardware complexity. CI techniques utilize compressive single-pixel apertures that can emit spatiotemporally varying random radiation patterns [53], [54].…”

Section: A CI Numerical Modelmentioning

confidence: 99%

Single Radar Image Motion Deblurring Through PSF Estimation and Convolution Regularizer

Sharma,

García-Fernández,

Álvarez-Narciandi

et al. 2024

Trans. Rad. Sys.

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This paper proposes a deep learning-based motion deblurring method for radar images using point-spread function (PSF) estimations. Motion blur is a common problem in radar imaging due to the movement of the radar platform or the target. The proposed method uses a convolutional neural network (CNN) to learn the mapping between the blurred and sharp images. In addition, the PSF of the motion blur is estimated using a separate CNN. The estimated point spread function (PSF) is utilized in conjunction with the input image to reconstruct a deblurred image. The reconstruction process is further optimized by incorporating the relationship between the input image, PSF, and the ground truth into the training loss term and optimizing it. The network is trained on a large dataset of simulated blurred and sharp radar images with corresponding PSFs. The developed method is evaluated on blurred radar images, subjected to varying degrees and lengths of blur, and compared with state-of-the art methods. The results show that the proposed method outperforms the existing methods both qualitatively and quantitatively. The proposed method can be used for various radar imaging applications, such as target detection and recognition, surveillance and navigation.

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“…The identification and direction of antiaircraft guns during World War II was greatly aided by the use of RADAR. [6] The United States Navy came up with the word "RADAR" in the year 1940. In modern RADAR systems, more information about the target may be gathered from the echo, and these systems also have a greater variety of signals.…”

Section: Introductionmentioning

confidence: 99%

Designing of Synthetic Aperture Radar Based Control Algorithms for the Autonomous Vehicles

Rajender¹

2023

IJSREM

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Abstract— The rise in popularity of self-driving cars can be attributed to advancements in modern technology. The surge in interest in self-driving cars has led to an increase in their development, but this has also brought some challenges. A large part of the solution to these problems is satellite remote sensing and GIS technology. Optical data remote sensing technologies alone have limited potential for long-term forest management sustainability. Active Synthetic Aperture Radar (SAR) remote sensing technology has grown in importance in forestry because of its uniqueness and rapid advancement. For example, SAR has an all-weather capability that is sun light independent, cloud and rain-resistant, and highly penetrating. SAR and optical, SAR and LiDAR, optical and LiDAR remote sensing have all been shown to be useful for accurate forest AGB estimation when compared to single sensor data. These types of sensor data integrations are becoming increasingly common. This is made possible by the fact that the scattering process heavily influences the polarimetric signatures that can be observed. The inclusion of SAR polarimetry improves classification and segmentation quality compared to conventional SAR with a single channel. Decomposition products' outputs have been classified. Keywords— Synthetic Aperture Radar, LiDAR, Autonomous Vehicles,

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Parametric Image Reconstruction for Edge Recovery From Synthetic Aperture Radar Echoes

Cited by 12 publications

References 42 publications

Tomographic SAR imaging with large elevation aperture: a P-band small UAV demonstration

Tomographic SAR imaging with large elevation aperture: a P-band small UAV demonstration

Single Radar Image Motion Deblurring Through PSF Estimation and Convolution Regularizer

Designing of Synthetic Aperture Radar Based Control Algorithms for the Autonomous Vehicles

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