Airborne SAR autofocus based on blurry imagery classification
Existing airborne SAR autofocus methods can be classified as parametric and non-
parametric. Generally, non-parametric methods, such as the widely used phase gradient …
parametric. Generally, non-parametric methods, such as the widely used phase gradient …
Afnet and PAFnet: fast and accurate SAR autofocus based on deep learning
Autofocus plays a key role in synthetic aperture radar (SAR) imaging, especially for high-
resolution imaging. In the literature, the minimum-entropy-based algorithms (MEA) have …
resolution imaging. In the literature, the minimum-entropy-based algorithms (MEA) have …
SAE-Net: A deep neural network for SAR autofocus
W Pu - IEEE Transactions on Geoscience and Remote …, 2022 - ieeexplore.ieee.org
The sparsity-driven technique is a widely used tool to solve the synthetic aperture radar
(SAR) imaging problem. However, it always encounters sensitivity to motion errors. To solve …
(SAR) imaging problem. However, it always encounters sensitivity to motion errors. To solve …
Fast SAR autofocus based on ensemble convolutional extreme learning machine
Inaccurate Synthetic Aperture Radar (SAR) navigation information will lead to unknown
phase errors in SAR data. Uncompensated phase errors can blur the SAR images …
phase errors in SAR data. Uncompensated phase errors can blur the SAR images …
Blind NCS-based autofocus for airborne wide-beam SAR imaging
The improvement in azimuth resolution and/or the increase in the instantaneous beam width
can result in a wide-beam autofocus problem in airborne synthetic aperture radar (SAR) …
can result in a wide-beam autofocus problem in airborne synthetic aperture radar (SAR) …
Ultrahigh-resolution autofocusing for squint airborne SAR based on cascaded MD-PGA
Squint ultrahigh-resolution (UHR) synthetic aperture radar (SAR) generally uses the
extended Omega-K algorithm (EOK) for range cell migration correction (RCMC). However …
extended Omega-K algorithm (EOK) for range cell migration correction (RCMC). However …
Deep learning algorithm for SAR autofocus
W Pu - 2021 XXXIVth General Assembly and Scientific …, 2021 - ieeexplore.ieee.org
The sparsity-driven technique is a widely used tool to solve the SAR imaging problem.
However, it always encounters the sensitivity to motion errors. To solve this problem, this …
However, it always encounters the sensitivity to motion errors. To solve this problem, this …
SAR image reconstruction and autofocus using complex-valued feature prior and deep network implementation
W Huo, M Li, J Wu, Z Li, J Yang… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Synthetic aperture radar (SAR) plays an important role in remote sensing by providing
electromagnetic images of the observation scene. The prior knowledge-based SAR image …
electromagnetic images of the observation scene. The prior knowledge-based SAR image …
STLS-LADMM-Net: A deep network for SAR autofocus imaging
M Li, J Wu, W Huo, Z Li, J Yang… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Synthetic aperture radar (SAR) can provide high-resolution electromagnetic backscattering
images of the illuminated area, playing a significant role in various applications. However …
images of the illuminated area, playing a significant role in various applications. However …
3-d sar autofocusing with learned sparsity
Inevitable inaccuracies of 3-D synthetic aperture radar (3-D SAR) imaging geometry may
cause undesired blurs in reconstructed images. Recent advances show impressive results …
cause undesired blurs in reconstructed images. Recent advances show impressive results …