SAR Image Autofocusing Based on Res-Unet
Airborne synthetic aperture radar images are easily smeared by the phase error due to the
unsteady platform movement. Autofocusing by traditional methods is unsatisfied in critical …
unsteady platform movement. Autofocusing by traditional methods is unsatisfied in critical …
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 convolutional neural networks
Autofocus is a key technology for high-resolution synthetic aperture radar imaging. However,
traditional SAR autofocus methods require too many iterations, have low computational …
traditional SAR autofocus methods require too many iterations, have low computational …
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 …
Enhanced synthetic aperture radar image autofocus and classification using 2D SARNet framework
A synthetic aperture radar (SAR) system is a notable source of information, recognized for its
capability to operate day and night and in all weather conditions, making it essential for …
capability to operate day and night and in all weather conditions, making it essential for …
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 …
SPA-GAN: SAR Parametric Autofocusing Method with Generative Adversarial Network
Traditional synthetic aperture radar (SAR) autofocusing methods are based on the point-
scattering model, which assumes the scattering phases of a target to be a constant …
scattering model, which assumes the scattering phases of a target to be a constant …
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 …
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 …